Organization: Neya Systems
Country: U.S.
Website: neyarobotics.com
Year Founded: 2009
Number of Employees: 51-100
Innovation Class: Technology, Product & Services

In November 2023, Neya Systems started developing a cybersecurity standard for autonomous ground vehicles (AGVs). The effort focuses on enhancing the protection, mitigation, recovery, and adaptability of AGVs. It will do this by applying U.S. Department of Defense Zero Trust cybersecurity to its autonomy software.

Neya Systems aims to push its autonomy software beyond existing automotive security. To manage this task, the company said it will use the Autonomous Intelligent Cyberdefense Agent (AICA) reference architecture to apply cyber extensions to its Mission Planning and Management Software.

The company is currently developing, training, and testing its new cyber systems using its Virtual Integration and Simulation Environment. Neya Systems said this will enable it to safely develop new capabilities and demonstrate viability thousands of times before it starts field testing.

“By introducing and managing zero-trust principles for autonomy in vehicles, Neya Systems is paving the way for safer and more secure transportation systems,” said Kurt Bruck, division manager of Neya Systems.

“We are excited to be focused on developing technology that will establish a more secure future for our ground and autonomous vehicles,” he added. “Our team is committed to delivering fully functional cyber autonomy that will help protect the safety and security of our nation’s commercial and defense-related transportation systems.”

The company’s goal for cyber autonomy is to intelligently identify threats and risks to autonomous missions. It is designed to be completely self-contained and capable of autonomously detecting, reporting, and defending against threats that can exploit or disrupt a mission. By implementing a suite of behavioral analytics to baseline an expected, normal state-of-vehicle operation, it can then use anomaly detection techniques that will act as the “intelligent system.”

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A Relay robot makes a hotel delivery using autonomous navigation. Source: Relay Robotics

When we think of autonomous navigation, the first thing that usually comes to mind is self-driving cars. Although their development has spanned decades, recent years have seen significant advancements.

One important framework that is used ubiquitously in the self-driving car industry is the classification of levels of driving automation. Defined by the Society of Automotive Engineers (SAE) in 2014, this framework remains a standard reference in the field.

While indoor mobile robots have enjoyed nowhere near the fame that self-driving cars have, they’ve evolved substantially in the past decade as well. Driven by staff shortages, service robots are increasingly being deployed across various industries, including hospitality, healthcare, warehouse and logistics, food service, and cleaning.

Relay robots in particular, are being deployed in busy hospitals and hotels across the world. However, unlike automated driving, there is currently no widely adopted standard for levels of autonomous navigation for indoor robots. Our objective is to present such a framework.

Given the inherent availability of a human driver as fallback in self-driving cars, much of the SAE framework is based on the distribution of driving responsibilities between the human driver and the self-driving agent. Level 0 indicates no automation where the human driver is completely in control.

Levels 1, 2, and 3 have varying degrees of partial automation. At Level 4, the vehicle is fully self-driving, but only under certain defined conditions. Leading self-driving companies like Waymo have achieved this level of autonomy.

Finally, Level 5 is full automation everywhere and in all conditions. This level has not been achieved yet.

What influences levels of autonomous navigation for indoor robots?

Installation complexity

Indoor robots do not have an inherent partnership with a human driver. Essentially, they begin at Level 4 of the SAE framework in this regard. But indoor robots do have a different advantage, another crutch to rely on instead at initial levels of autonomy — the ability to modify their environment.

For example, modifying a building’s infrastructure by painting lines on the floor or placing landmarks on the walls is not as difficult relative to modifying all road infrastructure. Such markers can be very helpful aids for automated guided vehicle (AGV) navigation.

In general, indoor robots today go through an installation process before being put into operation. In addition to modifying building infrastructure, mapping, labeling, and other required setup can be a part of this process. This can often be cost-, time-, and labor-intensive.

The more advanced the navigation skills of the robot though, the less complicated the installation process tends to be. And lower installation complexity leads to lower cost and friction for adoption.

Installation complexity is thus an important factor to consider while defining the levels of autonomous navigation for indoor robots.

Mobile robot considerations include safety and efficiency in dynamic settings like hospitals. Source: Relay Robotics

Social navigation

Another major distinction between self-driving cars and indoor autonomous robots is of course the difference in environments. With the exception of factory-like environments, most indoor environments are very unstructured. There are no lanes or signals, no dedicated crosswalks for people, and no well defined rules of the road.

Instead, indoor environments are highly social spaces. Robots have to co-navigate with all other agents, human and robot, that are also using the space. Well-defined rules of the road are replaced by a loosely defined set of social rules that change based on country, environment, situation and many other factors. For instance, do robots, people, or other vehicles pass on the left or the right?

Successfully navigating in these highly unstructured and social environments requires skills and behaviors that are usually placed under the label “social navigation.” At a high level, social navigation is a set of behaviors that allows a robot to navigate in human-populated environments in a way that preserves or even enhances the experience of the humans around it.

While functional navigation focuses on safety and efficiency, resulting in robots that can complete a task but often need humans to adapt to them, social navigation focuses on the quality of human experience and allows robots to adapt to humans. This may not be crucial for controlled, human-sparse environments like factories and warehouses but becomes increasingly important for unstructured, human-populated environments.

Operational domain helps define autonomous navigation

A robot’s operational domain is the kinds of environments it can be successful in. Not all indoor environments are the same. Different environments have different needs and might require different levels of navigation sophistication.

For instance, warehouses and factories allow for robots with simpler, safety focused navigation to be successful. On the other hand, environments like hotels or restaurants are unstructured, unpredictable and require higher levels of navigation skill, particularly social navigation. Even more challenging are highly crowded environments or sensitive environments like hospitals and elder care homes.

Not every indoor environment requires a robot of the highest social navigation level, but placing a robot with low social navigation skill in environments like hospitals can result in poor performance. So it is important to define the operational domain of a robot.

Multi-floor autonomous navigation

Self-driving cars need only worry about single-level roads. But a large number of buildings in the world are multi-floor, and robots need to be able to traverse those floors to be effective. Overcoming this challenge of vertical navigation can result in a huge increase in a robot’s operational domain and is an important factor to consider when defining a robot’s level.

So installation complexity, social navigation, and operational domain are the three barometers against which we can measure the level of autonomous navigation for indoor robots.

Multi-floor navigation, while hugely important, is somewhat orthogonal to 2D navigation skill and robots of every navigation level could potentially access it. So we create a level modifier for this capability that could be added to any level.

With that, let’s dive into defining levels of indoor robot navigation.

Levels of autonomous navigation for indoor robots

Level 0

These are robots that have no autonomous navigation capabilities and rely entirely on humans to operate them. Robots that fall into this category are telepresence robots and remote controlled robots like remote-controlled cars.

Level 1

Robots that have a minimal sensor suite and can only navigate on paths that are predefined using physical mechanisms like wires buried in the floor, magnetic tape or paint. These Level 1 robots have no ability to leave these predefined paths.

Such AGVs have no concept of location, using only the distance traveled along the path to make decisions. They can typically detect obstacles and slow down or stop for them, but they do not have the ability to avoid obstacles.

A Mouse AGC 3A10-20T automated guided cart. Source: Toyota

Level 1 robots need extensive changes to a building’s infrastructure during installation leading to significant cost. They have almost no social navigation capability, and so their operational domain is mainly highly structured and controlled manufacturing and logistics environments.

Level 1 characteristics. Source: Relay Robotics

Level 2

Robots operating at Level 2 are AGVs that do not need physical path definition but still rely on paths that are digitally defined during installation. These mobile robots can localize themselves within a site using external aids such as reflectors, fiducials or beacons that are placed in strategic locations at the site. They can use this location to follow the virtually defined paths.

Like Level 1 robots, these robots also cannot leave their virtual predefined paths and can only detect and stop for obstacles but cannot avoid them.

Demonstration of an AGV triangulating using reflectors on walls. Source: Cisco-Eagle

Although the infrastructure changes required are not as intrusive as Level 1, because of the need for installation of external localization sources, these robots have moderate complexity of installation. The fixed paths mean that they have low social navigation skill and are still best used in relatively structured environments with little to no interaction with humans.

Level 2 autonomous navigation characteristics. Source: Relay Robotics

Level 3

Robots operating at Level 3 rely entirely on onboard sensors for navigation. They use lidars and/or cameras to form a map of their environment and localize themselves within it. Using this map, they can plan their own paths through the site. They can also dynamically change their path if they detect obstacles on it. So they can not only detect obstacles, but can also avoid them.

A 3D lidar point-cloud visualization. Source: Jose Guivant, “Autonomous Navigation using a Real-Time 3D Point Cloud“

This independence and flexibility of Level 3 robots results in moderate social navigation skills and significantly reduced installation complexity since no infrastructure changes are required.

Level 3 robots can be used in unstructured environments where they can navigate alongside humans. They represent a significant increase in intelligence, and systems of this level and higher are called autonomous mobile robots (AMRs). Most modern service robots belong to this category.

Level 3 autonomous navigation characteristics. Source: Relay Robotics

Level 4

Even though robots of Level 3 cross the threshold of navigating in unstructured environments alongside humans, they still navigate with moderate social navigation skill. They do not have the advanced social navigation skills needed to adapt to all human interaction scenarios with sophistication. This sometimes requires the humans it interacts with to compensate for its behavioral limitations.

In contrast, Level 4 robots are AMRs with social navigation skills evolved enough to be on par with humans. They can capably navigate in any indoor environment in any situation provided there aren’t any physical limitations.

This means that their operational domain can include all indoor environments. Another ramification of this is that Level 4 robots should never need human intervention to navigate.

This level has not yet been fully achieved, and defining and evaluating everything that is required for such sophisticated social navigation is challenging and remains an active area of research. Here is an infographic from a recent attempt to capture all the facets of social navigation:

Source: Anthony Francis, et al., “Principles and Guidelines for Evaluating Social Robot Navigation Algorithms“

To navigate capably in all indoor environments, robots need to be able to optimize within a complex, ill-defined, and constantly changing set of rules. This is something that humans handle effortlessly and often without conscious thought, but that ease belies a lot of complexity. Here are a few challenges that lie on the path to achieving human-level social navigation –

• Proxemics: Every person has a space around them that is considered personal space. Invading that space can make them uncomfortable, and robots need to respect that while navigating. However, the size and shape of this space bubble can vary based on culture, environment, situation, crowd density, age, gender, etc. For example, a person with a walker might need a larger-than-average space bubble around them for comfort, but this space has to shrink considerably when taking an elevator. Specifying rules for every situation can quickly become intractable.
• Shared resources: The use of doors, elevators, and other shared resources in a building have their own implicit set of rules. Navigation patterns that hold for the rest of the building might not apply here. In addition, robots need to follow certain social norms while using these resources. Opening doors for others is considered polite. Waiting for people to exit an elevator before trying to enter, making space for people trying to get off a crowded elevator, or even temporarily getting off the elevator entirely to make space for people to exit are common courtesies that robots need to observe.
• Communicating intent: Robots need to be able to communicate their intent while co-navigating with other agents. Not doing so can sometimes create uncertainty and confusion. Humans do this with body language, eye contact, or verbal communication. We rely on this particularly when we find ourselves in deadlock situations like walking toward another person in a narrow corridor or when approaching the same door at the same time. Robots also need to be able to resolve situations like these while preserving the safety and comfort of the humans they’re interacting with.

All in all, achieving this level of social navigation is extremely challenging. While some Level 3 robots may have partially solved some of these problems, there is still quite a ways to go to reach true Level 4 autonomy.

Level 4 indoor navigation characteristics. Source: Relay Robotics

Level 5

As humans, we are able to find our way even in new, unfamiliar buildings by relying on signage, using semantic knowledge, and by asking for directions when necessary. Robots today cannot do this. At the very least, the site needs to be fully mapped during installation.

Level 5 autonomous indoor navigation of a service robot. Source: Relay Robotics, generated with Google Gemini

Level 5 robots are robots that could navigate in all indoor environments on par with human skill, as well as do so in a completely new environment without detailed prebuilt maps and a manually intensive installation process. This would remove installation complexity entirely, allowing robots to be operational in new environments instantly, reducing friction for adoption, and paving the way for robots to become more widespread.

This is a missing level in the framework for self-driving cars as they also go through a similar process where high precision 3D maps of an area are created and annotated before a self-driving car can operate in it. Developments in artificial intelligence could help realize Level 5 capability.

Level 5 mobile robot navigation characteristics. Source: Relay Robotics

Multi-floor autonomous navigation+

Robots that can either climb stairs or that can call, board, and leave elevators unlock the ability to do multi-floor navigation and get the “plus” designation. Also, highly reliable sensors are required to detect and avoid safety hazards like staircases and escalators for any robot that operates in multi-floor buildings. So a Level 2 robot that can successfully ride elevators would be designated Level 2+.

Elevator riding is the more common of the two approaches to this capability and may require infrastructure changes to the elevator system to achieve. So this introduces additional installation complexity.

It is also worth noting that in human-populated environments, elevators provide robots an additional social navigation challenge. This is because it requires movement in a confined space with many other agents, tight time constraints for elevator entry and exit, and dealing with special behavioral patterns that humans engage in while riding elevators.

In summary, robots of Levels 1 and 2 rely heavily on infrastructure changes for navigation and have low social navigation, so they are best suited for structured, human-sparse environments.

Robots of Level 3 are more intelligent and self-reliant. They require almost no infrastructure changes during installation, but at minimum they require the environment to be mapped and labeled. They possess moderate social navigation skills and can operate in unstructured, human-populated environments.

Level 4 represents an advancement to human-level navigation skill allowing for safe deployment in any indoor environment. Level 5 robots take this a step further, navigating with the same proficiency even in entirely new, unfamiliar spaces. Any of these robots that can do multi-floor navigation get the additional “+” designation.

Trends across levels. All infographics created by Irina Kim and Jason Hu, Relay Robotics

Autonomous navigation must be reliable

A crucial factor for success that is not represented in this framework is the overall robustness and reliability of the product. It is easy to underestimate the complexity and unpredictability of real-world environments. Robotic systems typically take several years of field experience to go from a cool lab demonstration to a robust and reliable product that people can rely on.

For example, Relay Robotics offers Level 3+ robots that have already completed over 1.5 million successful deliveries and accumulated years of real-world operational experience. With this mature technology as a foundation, the company is making strides toward Level 4+ navigation.

Relay’s focus on creating sophisticated social navigation that can handle even busy and stressful environments like hospital emergency departments has made our AMRs among the most sophisticated on the market today. For the Relay and the broader industry, the key to advancing further lies in enhancing social navigation capabilities.

Even though there is still much work to do, Relay Robotics is using breakthroughs in AI and deep learning to get there.

About the authors

Sonali Deshpande is senior navigation engineer at Relay Robotics. Prior to that, she was a robotics software engineer at Mayfield Robotics, a perception systems engineer at General Motors, and a robotics engineer at Discovery Robotics.

Deshpande has a master’s in robotic systems development from Carnegie Mellon University.

Jim Slater is a robot systems architect and member of the executive staff at Relay Robotics, acting as a consultant. Prior to that, he was the founder and CEO of two successful startups including Nomadic Technologies (mobile robotics) and Alliant Networks (wireless networks). 

Slater has his master’s in engineering from Stanford University, where he was a research assistant in the Computer Science Robotics lab.  He also holds an MBA from the University of Wisconsin – Madison.

The authors also thank Steve Cousins for his insight and feedback in creating this piece. This article is posted with permission.

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As robots become more self-sufficient, they have to navigate their surroundings with greater independence and reliability. | Credit: PointOne Nav

As robots become more self-sufficient, they have to navigate their surroundings with greater independence and reliability. Autonomous tractors, agricultural harvesters, and seeding machines must carefully make their way through crop fields while self-driving delivery vehicles must safely traverse the streets to place packages in the correct spot. Across a wide range of applications, autonomous mobile robots (AMRs) require highly accurate sources of positioning to safely and successfully complete the jobs for which they are designed.

Accomplishing such precision requires two sets of location capabilities. One is to understand the relative position of itself to other objects. This provides critical input to understand the world around it and, in the most obvious case, avoid obstacles that are both stationary and under motion. This dynamic maneuvering requires an extensive stack of navigational sensors like cameras, radar, lidar, and the supporting software to process these signals and give real-time direction to the AMR.

The second set of capabilities is for the AMR to understand its precise physical location (or absolute location) in the world so it can precisely and repeatedly navigate a path that was programmed into the device. An obvious use case here is high precision agriculture, where various AMRs need to travel down the same narrow path over the course of many months to plant, irrigate, and harvest crops, with every pass requiring the AMR to reference the same exact spot each time.

This requires a different set of navigational capabilities, starting with Global Navigation Satellite Systems (GNSS), which the entire ecosystem of sensors and software leverage. Augmenting GNSS are corrections capabilities like RTK and SSR that help drive 100x higher precision than GNSS alone for open-sky applications, and Inertial Measurement Units combined with sensor fusion software for navigating where GNSS is not available (dead reckoning).

Before we dive into these technologies, let us spend a minute looking at use cases where both relative and absolute locations are required for an AMR to do its job.

Robotics applications requiring relative and absolute positioning

AMRs reveal what humans take for granted — the innate ability to accurately locate oneself in the world and take precise action based on that information. The more varied the applications for AMRs, the more we discover what types of actions require extreme precision. Some examples include:

• Agricultural Automation: In agriculture, AMRs are becoming increasingly common for tasks like planting, harvesting, and crop monitoring. These robots utilize absolute positioning, typically through GPS, to navigate large and often uneven fields with precision. This ensures that they can cover vast areas systematically and return to specific locations as needed. However, once in the proximity of crops or within a designated area, AMRs rely on relative positioning for tasks that demand a higher level of accuracy, such as picking fruit that may have grown or changed position since the AMR last visited it. By combining both positioning methods, these robots can operate efficiently in the challenging and variable environments typical of agricultural fields.
• Last-Mile Delivery in Urban Settings: AMRs are transforming last-mile delivery in urban environments by autonomously transporting goods from distribution centers to final destinations. These robots use absolute positioning to navigate city streets, alleys, and complex urban layouts, ensuring they follow optimized routes while avoiding traffic and adhering to delivery schedules. Upon reaching the vicinity of the delivery location, the AMRs will also use relative positioning to maneuver around variable or unexpected obstacles, such as a vehicle that is double parked on the street. This dual approach enables the AMRs to handle the intricacies of urban landscapes and make precise deliveries directly to customers’ doorsteps.
• Construction Site Automation: On construction sites, AMRs are employed to ensure the project is built to the exact specifications that were designated by the engineers. They also help with tasks like transportation of materials and mapping or surveying of environments. These sites often span large areas with constantly changing environments, requiring AMRs to use absolute positioning to navigate and maintain orientation within the overall project site. Relative positioning comes into play when AMRs perform tasks that require interaction with dynamic elements, such as avoiding other equipment or even personnel on the site. The combination of both positioning systems allows AMRs to effectively contribute to the complex and dynamic nature of construction projects, enhancing efficiency and safety.
• Autonomous Road Maintenance: AMRs are increasingly being used in road maintenance tasks such as pavement inspection, crack sealing, and line painting. These robots utilize absolute positioning to travel along stretches of highway or roadways, ensuring they stay on course over long distances and can precisely capture the specific locations where maintenance needs to occur. When performing these maintenance tasks, they switch to relative positioning to accurately identify and address specific road imperfections, paint lane markings with precision, or navigate around obstacles. This dual capability allows AMRs to efficiently manage road maintenance tasks while reducing the need for human workers to operate in hazardous roadside environments, improving safety and productivity.
• Environmental Monitoring and Conservation: In outdoor environments, AMRs are often deployed for environmental monitoring and conservation efforts such as wildlife tracking, pollution detection, and habitat mapping. These robots leverage absolute positioning to navigate vast natural areas, from forests to coastal regions, ensuring comprehensive coverage of the terrain and allowing for the capture of detailed site surveys and mapping. AMRs can perform tasks like capturing high-resolution images, collecting samples, or tracking animal movements with pinpoint accuracy and can overlay these samples over time in a cohesive way.

In all of the above examples, absolute positioning accuracy of much less than a meter is required to avoid potentially catastrophic consequences. Worker injuries, substantial product losses, and costly delays are all likely without precise location. Essentially, Anywhere an AMR needs to operate within a few centimeters will require it to have both relative and absolute location solutions. 

Faction’s self-driving delivery cars rely on a complex array of sensors, including GNSS and Point One’s RTK network, to safely navigate their routes. | Credit: PointOne Nav

Technology for relative positioning

AMRs leverage a number of sensors to locate themselves in relation to other objects in their environment. These include:

• Cameras: Cameras function as the visual sensors of autonomous mobile robots, providing them with an immediate picture of their surroundings similar to the way human eyes work. These devices capture rich visual information that robots can use for object detection, obstacle avoidance, and environment mapping. However, cameras are dependent on adequate lighting and can be hampered by adverse weather conditions like fog, rain, or darkness. To address these limitations, cameras are often paired with near-infrared sensors or equipped with night vision capabilities, which allow the robots to see in low-light conditions. Cameras are a key component in visual odometry, a process where changes in position over time are calculated by analyzing sequential camera images. In general, cameras always require significant processing to convert their 2-D images into 3-D structures.
• Radar Sensors: Radar sensors operate by emitting pulsating radio waves that reflect off objects, providing information about the object’s speed, distance, and relative position. This technology is robust and can function effectively in various environmental conditions, including rain, fog, and dust, where cameras and lidar might struggle. However, radar sensors typically offer sparser data and lower resolution compared to other sensor types. Despite this, they are invaluable for their reliability in detecting the velocity of moving objects, making them particularly useful in dynamic environments where understanding the movement of other entities is critical.
• Lidar Sensors: Lidar, or Light Detection and Ranging, is a sensor technology that uses laser pulses to measure distances by timing the reflection of light off objects. By scanning the environment with rapid laser pulses, lidar creates highly accurate, detailed 3D maps of the surroundings. This makes it an essential tool for simultaneous location and mapping (SLAM), where the robot builds a map of an unknown environment while keeping track of its location within that map. lidar is known for its precision and ability to function well in various lighting conditions, though it can be less effective in rain, snow, or fog, where water droplets can scatter the laser beams. Despite being an expensive technology, lidar is favored in autonomous navigation due to its accuracy and reliability in complex environments.
• Ultrasonic Sensors: Ultrasonic sensors function by emitting high-frequency sound waves that bounce off nearby objects, with the sensor measuring the time it takes for the echo to return. This allows the robot to calculate the distance to objects and obstacles in its path. These sensors are particularly useful for short-range detection and are often employed in slow, close-range activities such as navigating within tight spaces like warehouse aisles, or for precise maneuvers like docking or backing up. Ultrasonic sensors are cost-effective and work well in a variety of conditions, but their limited range and slower response time compared to lidar and cameras mean they are best suited for specific, controlled environments where high precision at close proximity is required.

RTK relies on known base stations with fixed positions to correct any errors in GNSS receiver positioning estimates. | Credit: PointOne Nav

The baseline technology used for absolute positioning starts with GNSS (the term that includes GPS and other satellite systems like GLONASS, Galileo, and BeiDou). Given that GNSS is affected by atmospheric conditions and satellite inconsistencies, it can give a position solution that is off by many meters. For AMRs that require more precise navigation, this is not good enough – thus the emergence of a technology known as GNSS Corrections which narrows this error down to as low as one centimeter. 

• RTK: Real-time kinematic (RTK) uses a network of base stations with known positions as reference points for correcting GNSS receiver location estimates. As long as the AMR is within 50 kilometers of a base station and has a reliable communication link, RTK can reliably provide 1–2-centimeter accuracy.
• SSR or PPP-RTK:  State Space Representation (SSR), which is also sometimes called PPP-RTK, leverages information from the base station network, but instead of sending corrections directly from a local base station, it models the errors across a wide geographical area. The result is broader coverage allows distances far beyond 50km from a base station, but accuracy drops to 3-10 centimeters or more depending on the density and quality of the network.

While these two approaches work exceptionally well where GNSS signals are available (generally open sky), many AMRs will travel away from the open sky, where there is an obstruction between the GNSS receiver on the AMR and the sky. This can happen in tunnels, parking garages, orchards, and urban environments. This is where Inertial Navigation Systems (INS) come into play with their Inertial Measurement Unit (IMU) and Sensor Fusion software.

• IMU – An IMU combines accelerometers, gyroscopes, and sometimes magnetometers to measure a system’s linear acceleration, angular velocity, and magnetic field strength, respectively. This is crucial data that enables an INS to determine the position, velocity, and orientation of an object relative to a starting point in real-time.   

The history of the IMU dates back to the early 20th century, with its roots in the development of gyroscopic devices used in navigation systems for ships and aircraft. The first practical IMUs were developed during World War II, primarily for use in missile guidance systems and later in the space program. The Apollo missions, for example, relied heavily on IMUs for navigation in space, where traditional navigation methods were not feasible. Over the decades, IMU technology has advanced significantly, driven by the miniaturization of electronic components and the advent of Micro-Electro-Mechanical Systems (MEMS) technology in the late 20th century. This evolution has led to more compact, affordable, and accurate IMUs, enabling their integration into a wide range of consumer electronics, automotive systems, and industrial applications today.

• Sensor Fusion – Sensor fusion software is responsible for combining data from the IMU, as well as other sensors to create a cohesive and accurate understanding of an AMR’s absolute location when GNSS is not available. The most basic implementations “fill in the gaps” in real-time, between when the GNSS signal is dropped and when it is picked back up again by the AMR. The accuracy of sensor fusion software depends on several factors, including the quality and calibration of the sensors involved, the algorithms used for fusion, and the specific application or environment in which it is deployed. More sophisticated sensor fusion software is able to cross-correlate different sensor modalities, resulting in superior positional accuracy than from any one of the sensors in the solution working alone.

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Choosing the best RTK network for your autonomous robots

Point One’s Polaris RTK network features more than 1,700 base stations across the world, providing one of the most reliable sources of positioning corrections. | Credit: PointOne Nav

RTK for GNSS provides a highly accurate source of absolute location for autonomous robots. Without RTK, however, many robotics applications simply are not possible or practical. From construction survey rovers to autonomous delivery drones and autonomous agriculture tools, numerous AMRs depend on the centimeter-accurate absolute positioning that only RTK can provide.

That said, an RTK solution is only as good as the network behind it. Consistently reliable corrections require a highly dense network of base stations so that receivers are always within close enough range for accurate error corrections. The larger the network, the easier it is to get corrections for AMRs from anywhere. Density alone is not the only factor. Networks are highly complicated real-time systems and require professional monitoring, surveying, and integrity checking to ensure the data being sent to the AMR is accurate and reliable.

What does all of this mean for the developers of autonomous robots? At least where outdoor applications are concerned, no AMR is complete without an RTK-powered GNSS receiver. For the most accurate solution possible, developers should rely on the densest and most reliable RTK network. And where robots must move frequently in and out of ideal GNSS signal environments, such as for a self-driving delivery vehicle, RTK combined with an IMU provides the most comprehensive source of absolute positioning available. 

No two autonomous robotics applications are the same, and each unique setup requires its own mix of relative and absolute positioning information. For the outdoor AMRs of tomorrow, however, GNSS with a robust RTK corrections network is an essential component of the sensor stack.

Aaron Nathan is the founder and CEO of Point One Navigation, an entrepreneur and technical leader with over a decade of experience in cutting-edge robotics and critical software and hardware development. He has founded two venture-backed startups and has deep domain experience in sensor fusion, computer vision, navigation, and embedded systems, specifically in the context of self-driving vehicles and other robotic applications. Point One Navigation offers the first centimeter-accurate positioning platform designed for today’s most demanding applications, including the complex task of ensuring safe and effective AMRs. Point One’s Atlas INS provides real-time, precise positioning for a variety of autonomous robotics applications, using its best-in-class sensor-fusion algorithms.

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The Naviq sensor can sense angular orientation over a magnetic tape line. | Credit: Naviq

Naviq today unveiled the MTS160, a magnetic guide sensor for mobile robots. Magnetic guidance involves placing adhesive magnetic tape on the floor, which the robot senses and follows throughout the facility.

The new, patented sensor delivers angle measurement with 1-degree precision and position accuracy within 1 mm (0.039 in.). Naviq claimed that this dual functionality provides precision for both orientation and positioning, significantly improving the navigation capabilities of automated guided vehicles (AGVs).

For robots that follow a magnetic line around a facility, traditional magnetic guide sensors only provide one-dimensional position information, indicating how much the robot deviates from the center of the path. The common method for steering correction in robotics is proportional control, where the steering adjustment is proportional to the error detected between the robot’s measured position and the desired position.

Naviq promises faster curve following

The MTS160’s new angle detection allows the robot to assess the curvature of the track, said Naviq. This enables the robot to distinguish between minor trajectory adjustments needed on straight paths and more proactive steering for navigating curves.

This improved path-tracking precision allows robots to follow designated paths with enhanced accuracy and to navigate bends at higher speeds, said the Geneva, Switzerland-based company.

While magnet tape-following robots require that the facility be modified before the robot can start operations, modifying the path is as simple as moving the tape, a task requiring no special skills. The paths are similar to railroad tracks, and the vehicles stay on the path, Naviq noted.

The company added that the MT160 supports markers with reversed polarity to signal areas requiring speed adjustments, upcoming forks, and merges, and proximity to stop areas such as charging or loading/unloading stations. In addition, Naviq has created an algorithm for smooth direction changes at junctions.

The MT160 can function as the primary guidance sensor at less expense than vision systems or complement laser or vision navigation systems for precise, last-millimeter positioning, said the company.

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MTS160 supports CAN bus interface

The MTS160 features an M8 four-pin watertight connector for power and signal transmission. It supports CAN bus and RS232 interfaces, ensuring compatibility with all PLC brands and microcomputers, said Naviq.

The system’s compact design, measuring only 165 x 35 x 25 mm (6.4 x 1.3 x 0.9 in.), makes it easy to incorporate into robots for very narrow aisles, personal mobility shuttles, and robotics camera dollies, it asserted.

Naviq said it designed its user interface for convenience, featuring RGB status LEDs for visual feedback on tape and marker detection. A Web-based utility requires no installation and connects the sensor to a smartphone or PC via USB port for easy configuration, testing, logging, and monitoring.

The utility also supports automatic firmware updates., and the internal circuitry can automatically run self-tests to ensure safe operation and reliable performance, said Naviq.

The MTS160 lists for $495 (€480) per unit.

Naviq describes magnetic guidance as the industry workhorse because of the ease of path modification. Source: Naviq

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Cyngn’s autonomous DriveMod Tugger tugs up to 12,000 lbs (5,443 kg) to automate repetitive hauling. | Source: Cyngn

Cyngn announced this week that its AI-powered autonomous navigation solution, DriveMod, can now operate in outdoor environments. This means organizations can send the DriveMod Tugger on missions that go indoors and outdoors, giving facility managers even more opportunity to automate repetitive workflows and shift employees over to more interesting, higher-value tasks.

Modern warehouses and manufacturing facilities are increasingly large. Many of them surpass 200,000 square feet and even include multiple buildings. The result is that moving materials from point A to point B has become increasingly complex.

“One of the biggest pain points businesses face is the wasted time and resources involved in transporting materials between buildings,” Sean Stetson, Cyngn’s VP of engineering, said. “This time-consuming task ties up equipment and pulls workers away from where they’re most needed, resulting in expensive lost productivity. By automating these tasks, companies can eliminate these inefficiencies, shifting workers to other responsibilities.”

By extending DriveMod’s capabilities outdoors, Cyngn says it provides organizations with a system that eliminates bottlenecks in material movement. This includes transporting goods between outdoor storage areas and facilitating smoother transitions across multi-building facilities.

The Menlo Park, Calif.-based company develops and deploys scalable, differentiated autonomous vehicle technology for industrial organizations. Its DriveMod Kit can be installed on new industrial vehicles or retrofitted into existing fleets. Cyngn is a public company listed on the NASDAQ under the ticker CYN.

Cyngn’s new capabilities a response to customer demand

Given the costly challenge of moving materials between buildings in a large site, several companies have engaged Cyngn to automate outdoor operations, the company says.

“Businesses are asking for more than just indoor efficiency. As a result, this marks a major milestone in broadening our reach and catering to the diverse needs of customers,” Cyngn CEO Lior Tal said. “The future of automation isn’t just about optimizing indoor spaces; it’s about creating smarter, more flexible solutions that cater to the full spectrum of operational environments.”

Cyngn said the ability to operate outdoors opens new doors for DriveMod users. It can empower facility managers to automate the movement of goods in previously manual outdoor workflows, creating a fully connected system between indoor and outdoor operations.

Other indoor-outdoor navigation solutions

Outdoor navigation can be difficult for robots, especially ones built to operate inside warehouses. Outside, there are much fewer permanent features robots can use to figure out where it is and where it needs to go. While autonomous navigation for autonomous mobile robots (AMRs) or automated guided vehicles (AGVs) have traditionally been limited to indoor operations, Cyngn isn’t the first company to break the mold.

RGo Robotics, for example, has made the ability to operate inside and outside a cornerstone of its technology. The company offers a modular AI and computer vision software system called the RGo Perception Engine. It provides real-time localization, obstacle detection, and object recognition data.

Additionally, in July 2023, BlueBotics unveiled ANT everywhere, a product extension that allows AGVs and AMRs to operate inside and outside. The company added GNSS with real-time kinematic (RTK) positioning to its system, enabling outdoor operations.

Register now

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Organization: ifm efector Inc.
Country: U.S.
Website: www.ifm.com/us/en
Year Founded: 1985
Number of Employees: 101-500
Innovation Class: Technology

ifm efector’s Obstacle Detection System tackles the toughest hurdles of integrating 3D cameras into mobile robots, streamlining development, and reducing both capital expenditure and the total cost of ownership (TCO). This increases a user’s return on investment (ROI) and could help to democratize mobile robots, particularly for small and midsize enterprises (SMEs).

While mobile robot developers have effectively addressed person detection with standard safety lidars, collisions with objects beyond their view remain a challenge, potentially causing damage and hindering mission efficiency.

Both automated guided vehicles (AGVs), which navigate using static path planning, and autonomous mobile robots (AMRs), which navigate using dynamic path planning, react differently to detected obstacles. However, both grapple with potential collisions. 3D cameras can offer a comprehensive solution, affording a complete view of the robot’s path.

The ifm Obstacle Detection Solution reduces mobile robot development time by providing a ready-made option for manufacturers. The Worcester, Mass.-based company said this ensures integration across diverse environments, and reducing facility integration time, irrespective of floor conditions.

Although mobile robots are gaining popularity as a tool to enhance workforce efficiency and address labor shortages, they have not yet achieved widespread adoption. ROI is one factor holding mobile robots back. Addressing this requires a shift in focus toward TCO, with significant development and deployment times emerging as the primary impediments.

By offering an obstacle-detection system priced at just $450 per robot, without recurring fees, ifm provides a viable “buy” option for developers and aims to redefine affordability and performance benchmarks for mobile robots. This approach enables manufacturers to concentrate on delivering value to end users, catalyzing the integration of mobile robots into SMEs.

Register now

Explore the RBR50 Robotics Innovation Awards 2024.

RBR50 Robotics Innovation Awards 2024

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From industrial and collaborative robot arms to mobile robots and the latest humanoids, designers strive for ever lighter and more compact designs. At the same time, they need to handle increasingly heavy payloads and meet fast operating specifications. This is where motor science becomes essential, according to a speaker at RoboBusiness 2024. 

At 2:45 p.m. PT on Oct. 16, Todd Brewster, director of electromagnetic engineering at Kollmorgen, will discuss the evolution of motor science, from theory to action. He will examine the technology‘s role in enabling robots to satisfy industrial demands and to be commercially successful.

Brewster’s session, which will be on Day 1 of RoboBusiness, will share insights into how motors can be designed to improve thermal dynamics and robot efficiency. In addition, he will discuss how to design high-performance systems while also considering weight, size, and cost factors.

Attendees can learn engineering criteria for evaluating robot designs and determining the performance specifications and operating lives of their systems. Brewster will cover:

• Technical formulas behind motor performance
• Primary causes of heat losses seen in motor design and their trade-offs
• How optimized motor design improves heat management in robotic system designs

About the motor science expert and Kollmorgen

Brewster has more than 32 years of experience in the electromagnetic design of permanent magnet motors at Kollmorgen. This experience includes designing high-efficiency motors for a wide range of industrial and medical applications including servos, elevators, electric vehicles, heart pumps, and robots. 

Brewster earned his electrical engineering degree at Virginia Tech.

Radford, W.Va.-based Kollmorgen will be exhibiting at Booth 325 at on the expo floor at RoboBusiness. The Regal Rexnord brand has more than 100 years of motion experience and provides frameless motors, drives, cabling and connectors, and automated guided vehicle (AGV) control systems.

Kollmorgen recently added VDA 5050 interoperability support to its fleet manager for AGVs and autonomous mobile robots (AMRs), and its customers include DB Schenker. The company also provides motion control platforms, other components, and systems integration services.

Kollmorgen will again be an exhibitor at RoboBusiness. Source: Kollmorgen

Learn best practices for business, engineering at RoboBusiness

In addition to enabling tech and robotics innovation, RoboBusiness 2024 focuses on investments and business topics related to running a robotics company. Keynote talks at the event in Santa Clara, Calif., will include:

• Rodney Brooks, co-founder and chief technology officer at Robust AI
• Claire Delaunay, chief technology officer at farm-ng
• Sergey Levine, co-founder of Physical Intelligence and associate professor at UC Berkeley
• Torrey Smith, the co-founder and CEO of Endiatx

Register now

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Learn about the latest software, robots, and drones for the intelligent warehouse in this week’s webinar. Source: Adobe Stock

Tracking mobile robots or monitoring inventory are only parts of the challenge for the intelligent warehouse. As robotics and the accompanying software stack continue to evolve, warehouse operators have more tools to orchestrate and optimize multiple types of robots, as well as human associates.

In this week’s free webinar, attendees will learn from experts around the world about the following:

• How smart warehouses can address current industry pain points
• How mobile robots and software have improved
• The importance of proper collection, analysis, and delivery of data
• What levels of orchestration and interoperability are necessary?
• Challenges of integrating software with enterprise systems
• Considerations for scaling and managing fleets
• Is there a role for artificial intelligence?

Register now for this webinar on the intelligent warehouse, which will be at 2:00 p.m. EDT on Wednesday, Aug. 21, 2024. Attendees can have their questions answered live, and the recording will be available on demand after the initial broadcast.

About the intelligent warehouse speakers:

Aldus von der Burg is the founder and CEO of Meili Robots, a pioneering company in the field of robotics software. With a background in engineering and a passion for using cutting-edge technology to solve complex logistical challenges, von der Burg has steered Meili Robots to develop fleet management software to integrate and optimizes the operations of diverse mobile robot fleets.

Lior Elazary is founder and CEO of inVia Robotics. He has more than 20 years of experience as an executive in Internet networking, robotics, software development, and enterprise architecture businesses.

Elazary has led and directed diverse teams developing everything from back-office systems to core enterprise technologies. In addition, he has a track record of scaling technology companies. He co-founded and later sold EdgeCast, a content delivery platform, as well as HostPro (now Web.com), an internet hosting company.

Elazary completed a master’s degree of computer science at the University of Southern California (USC) with a specialty focus on AI. He attended a Ph.D. program in robotics at USC, where he met his inVia co-founders.

Austin Feagins is senior director of customer solutions at Staci Americas. He has a decade of experience using emerging technologies and supporting some of the leading third-party logistics (3PL) and e-commerce fulfillment providers in the industry.

For Staci Americas, Feagins focuses on selecting and implementing automation and emerging technologies to support the business. Other cross-functional responsibilities include aligning key departments on continuous improvement, sustainability, pricing, and standardization initiatives.

Chad MacGillivray is vice president of distribution operations at Scholastic Canada Ltd. Over 25 years, he has played a crucial role in building and shaping the company’s supply chain and warehouse operations in Canada.

MacGillivray is recognized for his focus on people before process, and his adaptability in the face of unexpected changes. Notable achievements include spearheading a warehouse AI automation project that resulted in $2 million in annual savings driven by a significant increase in picker efficiency.

Currently, MacGillivray is focusing on designing a new automated Canadian hub that is expected to reduce operational expenses by more than 30%.

Chuck Russell is vice president of automated guided vehicle (AGV) sales at Scott Automation. He was global VP of sales at Transbotics since 2007 and sold AGVs worldwide, including in South Korea, China, Brazil, and Europe.

Russell entered the AGV industry after working in defense technology at Unisys and Loral. He has more than 30 years of experience in all facets of industrial automation. Russell has a BBA from Iona University.

Jouni Sievilä is the CEO of Navitec Systems, a global leader specializing in automation software for mobile robots, with a focus on cutting-edge navigation and fleet management solutions for both indoor and outdoor AGVs and autonomous mobile robots (AMRs).

With over 20 years of experience in automation, Sievilä said his commitment to the innovation ensures that Navitec Systems continues to set the standard in the automation industry, driving the future of intelligent autonomous vehicles.

Jackie Wu is co-founder and CEO of Corvus Robotics, which uses aerial drones for global inventory visibility. With firsthand experience in warehouses across four continents, Wu said he recognized a universal challenge in supply chain management: the outdated methods of inventory checks.

With a strong foundation in robotics engineering and economics, Wu identified the potential to integrate AI, autonomy, and computer vision with cutting-edge hardware manufacturing.

Moderator Eugene Demaitre is editorial director for robotics at WTWH Media, which produces The Robot Report, Automated Warehouse, RoboBusiness, and the Robotics Summit & Expo.

Prior to working for WTWH Media, he was an editor at BNA (now part of Bloomberg), Computerworld, TechTarget, and Robotics Business Review. He has participated in conferences worldwide, as well as spoken on several webcasts and podcasts. Demaitre is always interested in learning more about robotics and the intelligent warehouse. He has a master’s from the George Washington University and lives in the Boston area.

Sponsored by:

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The F45.07 Robotics Applications Subcommittee will develop standards for A-UGVs such as this Husky agricultural robot. Source: Clearpath

As robots spread from factories and warehouses to fields, standards need to keep up. ASTM International today said it has approved the formation of a new subcommittee on robotics applications. Its goal is to provide a venue where representatives from a variety of industries can come together to develop standards specific to their sectors.

The new subcommittee will enable ASTM members to address robotics applications in oil and gas, agriculture, construction, solar, and nuclear, according to Adam Norton, chair of the organization‘s F45 Robotics, Automation, and Autonomous Systems Committee.

“The portfolio of F45 standards to date have been intentionally developed to be general so they can be easily adapted to particular applications,” stated Norton, who is also associate director of the New England Robotics Validation and Experimentation (NERVE) Center at the University of Massachusetts, Lowell.

Robotics Applications Subcommittee to build on existing standards

The Robotics Applications Subcommittee will use existing standards to develop new ones that include specifications, guides, and test methods. These standards will address the needs of specific applications of robotics, automation, and autonomous systems, said ASTM.

“Several task groups for each application area are in the process of being formed, led by subject-matter experts (SMEs) who will not only develop standards within the subcommittee, but also advise as SMEs to other subcommittees,” said Norton.

The subcommittee plans to launch with an initial focus on solar, construction, and agricultural applications. All interested parties are invited to join F45.07.

Register now

ASTM adds automation subcommittees

The Robotics Applications Subcommittee will join other subcommittees under ASTM F45, including:

• F45.01 Environmental Conditions and Effects
• F45.02 A-UGV (automatic uncrewed ground vehicle) Docking and Navigation
• F45.03 A-UGV Object Detection and Protection
• F45.04 System Communication and Interoperability
• F45.05 Grasping and Manipulation
• F45.06 Legged Robot Systems
• F45.90 Executive Subcommittee
• F45.91 Terminology

“Robotics and automation continue to expand into new industries and sectors,” said Aaron Prather, director of robotics and autonomous systems programs at ASTM International. “We have identified that some of these industries need additional standards, guides, and best practices on top of the existing robotics standards to succeed.”

“This new subcommittee under F45 will allow those industries to have their subject matter experts work with robotics and automation experts to develop that extra layer to speed up robot and automation deployments,” he said. “It is truly a win-win for both those industries and the companies that want to help deploy robots within those industries.”

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Robin’6 includes the latest Wi-Fi 6 (IEEE 802.11ax) technology for AGVs and AMRs. | Credit: Conductix-Wampfler

Conductix-Wampfler this week said it has created a comprehensive solution designed exclusively for the secure transmission of data in mobile robots. The new Robin’6 minimizes integration and operational expenses while bolstering cyber security measures, claimed the company.

“With the new fully managed communication device Robin’6, we are setting standards for reliable communication in an ultra-compact, robust design,” stated Thomas Förste, product manager for wireless data transmission at Conductix-Wampfler.

“For optimum connectivity and integration into the infrastructure, the Wi-Fi solution is equipped with LAN, CAN, and GPIO interfaces, which are all positioned on the front for easy mounting and wiring,” he said. “With a height of just 26.5 mm [1 in.], the device is extremely slim and therefore also suitable for vehicles with low overall height.”

Conductix-Wampfler is a leading global manufacturer of mobile electrification and data-transfer systems for industrial machinery including robots. The company’s U.S. offices are in Omaha.

Wireless data transmission uses latest Wi-Fi 6 standard

Rapid deployment of fleets of automated guided vehicles (AGVs) and autonomous mobile robots (AMRs) requires easy setup and remote monitoring, according to Conductix-Wampfler.

The company said Robin’6 includes energy-saving sleep modes and digital controls to efficiently regulate the power consumption of the functional blocks on the AGV/AMR board.

“All these features make Robin’6 the perfect wireless data communication device for AGV/AMR manufacturers, system integrators, and plant operators interested in long-term cost reduction in the commissioning and operation of industrial vehicle fleets,” said Förste.

Register now

Product specifications

• Wireless data rate: Up to 1.2 Gbit/s
• Radios: 1x Wi-Fi 6 (IEEE 802.11ax), 1x Wi-Fi 5
• Communication ports: 2x WLAN, 2x GbE, 2x CAN, 4 GPI, 4 GPO
• Fully managed: gNMI and NETCONF interfaces
• Secure bring-up: Yes, over LAN or over WLAN
• Dimensions without mounting brackets: 27 x 105 x 148 mm
• Mounting options: DIN-Rail-Bracket, L-Back-Bracket, Snap-in Side-Brackets
• Protection Class: IP30
• Pollution Degree Environment: 3
• Temperature range: -25 to +65 °C

Robin’6 is ready to connect, says Conductix-Wampfler

Robin’6 offers mobile robot makers, integrators, and users a ready-to-connect wireless data communication device for onboard communications, Conductix-Wampfler said.

The unit is part of the company’s broader portfolio, which includes a range of products for energy transfer, energy storage, and secure data communication for AGVs and AMRs. Examples include battery systems, battery-charging systems, and communication and safety systems, all of which it said are complementary to one another.

The post Conductix-Wampfler launches wireless communication device for mobile robots appeared first on The Robot Report.

Here are the top 10 most popular stories on The Robot Report last month. Subscribe to The Robot Report Newsletter or listen to The Robot Report Podcast to stay updated on the robotics stories you need to know about.

10.  ABB releases OmniCore platform for control across its robotics line

Thanks to advances in cloud computing, perception technology, and artificial intelligence, industrial and other robots are becoming smarter and more capable. ABB Robotics launched its next-generation OmniCore platform, which can now control most of its automation line. Read More

9. Realtime Robotics celebrates motion-planning collaboration with Mitsubishi Electric

As factories and warehouses look to automate more of their operations, they need confidence that multiple robots can conduct complex tasks repeatedly, reliably, and safely. Realtime Robotics has developed hardware-agnostic software to run and coordinate industrial workcells smoothly without error or collision. Read More

8. NVIDIA highlights Omniverse, Isaac adoption by robot market leaders

In addition to artificial intelligence products, NVIDIA Corp. founder and CEO Jensen Huang announced several robotics-related items during his keynote at COMPUTEX in Taiwan. The company said that many manufacturers are producing a new generation of “AI computers” using its chips to enable Omniverse for modeling and business workflows. Read More

7. Beep deploys autonomous shuttles at Honolulu airport with partners

As millions of people in the Northern Hemisphere begin summer vacations, some of them will board autonomous vehicles for part of their journeys. Beep Inc. is working with the Hawai’i Department of Transportation, or HDOT, and Sustainability Partners on an 18-month self-driving shuttle pilot at Daniel K. Inouye International Airport, formerly HNL. Read More

6. GrayMatter raises $45M Series B to ease robot programming for manufacturers

Like businesses in other industries, U.S. manufacturers face widening labor shortfalls and need automation to help fill those gaps. GrayMatter Robotics announced that it has raised $45 million. The Carson, Calif.-based company said it plans to use the investment to expand to meet customer demand. Read More

5. Vecna Robotics raises more than $100M, hires COO to expand warehouse automation

Although investment in robotics dipped in the past year, suppliers with proven products and business models have been finding funding. In June 2024, Vecna Robotics closed its Series C round at $100 million, plus $40 million in new funding including equity and debt. The financing nearly doubled the company valuation since its Series B round. Read More

4. IEEE launches study group to explore and develop humanoid robot standards

As humanoid robots garner widespread public attention, such systems will also need to stand up to safety and performance standards. IEEE’s Robotics & Automation Society formed a new study group that will look into the current humanoid landscape and then develop a roadmap for future standards that various organizations can follow. Read More

3. ROBOTIS and Realbotix become official development partners

Robots including humanoids are complex and typically require components from multiple suppliers. Tokens.com Corp., now known as Realbotix, has entered a strategic partnership with ROBOTIS Inc. The companies said the agreement designates robotics and AI developer Realbotix as an official partner to facilitate integration with technologies from ROBOTIS. Read More

2. Robotics investments drive past $2.1B in May

Robotics investments for May 2024 topped $2.1 billion, the result of funding for 38 companies. The $2.1 billion is the highest monthly funding amount for the year 2024 and greatly exceeds the trailing 12-month average of $1.2 billion. Robotics funding through May 2024 totaled approximately $5.7 billion. Read More

1. Agility Robotics’ Digit humanoid lands first official job

Agility Robotics continues to have a leg up in the humanoid race. In June 2024, the company signed a multi-year deal with GXO Logistics to deploy its Digit humanoids in various logistics operations. Read More

The post Top 10 robotics stories of June 2024 appeared first on The Robot Report.

Neya offers autonomy, mission planning, and open architecture for uncrewed ground vehicles. | Source: Neya Systems

Neya Systems yesterday announced that it is partnering with the Association for Uncrewed Vehicle Systems International, or AUVSI. The partners said they plan to develop a cybersecurity and supply chain framework and certification program for uncrewed ground vehicles (UGVs). 

AUVSI and Neya Systems said they have observed a growing need for standardized evaluation and certification of UGVs. The goal of the collaboration is to establish comprehensive standards and testing protocols to enhance the security, safety, performance, and reliability of uncrewed and autonomous ground vehicles and robots.

The framework and voluntary certification program will focus on enhancing the protection, mitigation, recovery, and adaptability of AGVs, said the organizations. 

“We are excited to announce the development of this cybersecurity certification program for UGVs,” stated Kurt Bruck, vice president at Neya Systems. “This initiative represents a significant step forward in our efforts to establish an industry standard for protecting UGVs from unauthorized access. Our partnership with AUVSI will enable us to foster innovation and trust within the industry as a whole, ultimately enhancing the safety and reliability of these autonomous systems.”

Neya Systems has cybersecurity, simulation expertise

Warrendale, Pa.-based Neya Systems develops and integrates advanced, vehicle-agnostic, off-road, and airborne autonomy. The subsidiary of Applied Research Associates is a 2024 RBR50 Robotics Innovation Award winner for its cyber autonomy initiative.

In March, Neya said it is working with the Embodied AI Foundation to update the CARLA open-source simulator for autonomous driving research to Unreal Engine 5.

Neya Systems said will be bringing its expertise in applying the U.S. Department of Defense’s (DoD) Zero Trust cybersecurity principles to its autonomy software to the partnership.

Neya has worked with the U.S. Army to turn the Palletized Load System into an optionally crewed, autonomous vehicle. Source: Neya Systems

AUVSI brings complementary experience

Arlington, Va.-based AUVSI plans to share the industry expertise of members in its Cyber Working Group and Ground Advocacy Committee. 

The nonprofit organization is dedicated to the advancement of uncrewed systems and robotics. It represents corporate, government, and academic professionals from more than 60 countries. AUVSI said its members work in defense, civil, and commercial markets. 

AUVSI’s Cyber Working Group previously advised on the development of AVUSI’s Green UAS Frameworks and certification. It said this is the only verification method besides Blue UAS that the DoD’s Defense Innovation Unit has approved as confirming compliance with National Defense Authorization Act (NDAA) requirements for drones. 

“The need for standards and certifications for uncrewed systems continues to grow alongside the development and integration of uncrewed and autonomous vehicles and robotics,” noted Casie Ocaña, director of trusted programs at AUVSI. “In the ground domain, AUVSI is looking to leverage our Trusted Cyber framework so that we can offer a solution to verify and support compliance among ground vehicle and robotics companies – which will further advance the safe and reliable future of these technologies.”

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Organization: Opteran
Country: U.K.
Website: https://opteran.com
Year Founded: 2019
Number of Employees: 11-50
Innovation Class: Technology

Current approaches to machine autonomy require a lot of sensor data and expensive compute and often still fail when exposed to the dynamic nature of the real world, according to Opteran. The company earned RBR50 recognition in 2021 for its lightweight Opteran Development kit, which took inspiration from research into insect intelligence.

In December 2023, Opteran commercialized its vision-based approach to autonomy by releasing Opteran Mind. The company, which has a presence in the U.K., Japan, and the U.S., announced that its new algorithms don’t require training, extensive infrastructure, or connectivity for perception and navigation.

This is an alternative to other AI and simultaneous localization and mapping (SLAM), which are based on decades-old models of the human visual cortex, said James Marshall, a professor at the University of Sheffield and chief scientific officer at Opteran. Animal brains evolved to solve for motion first, not points in space, he noted.

Instead, Opteran Mind is a software product that can run with low-cost, 2D CMOS cameras and on low-power compute for non-deterministic path planning. OEMs and systems integrators can build bespoke systems on the reference hardware for mobile robots, aerial drones, and other devices.

“We provide localization, mapping, and collision prediction from robust panoramic, stabilized 3D CMOS camera input,” explained Marshall.

At a recent live demonstration at MassRobotics in Boston, the company showed how a simple autonomous mobile robot (AMR) using Opteran Mind 4.1 could navigate and avoid obstacles in a mirrored course that would normally be difficult for other technologies.

It is currently focusing on automated guided vehicles (AGVs), AMRs, and drones for warehousing, inspection, and maintenance.

“We have the only solution that provides robust localization in challenging environments with scene changes, aliasing, and highly dynamic light using the lowest-cost cameras and compute,” it said.

The company is currently working toward safety certifications and “decision engines,” according to Marshall.

Register now

Explore the RBR50 Robotics Innovation Awards 2024.

RBR50 Robotics Innovation Awards 2024

The post RBR50 Spotlight: Opteran Mind reverse-engineers natural brain algorithms for mobile robot autonomy appeared first on The Robot Report.

Motion control has become critical to mobile robot design, says Applied Motion Products. Credit: Gorodenkoff, Adobe Stock

Automated guided vehicles and autonomous mobile robots, or AGVs and AMRs, respectively, are the result of numerous design decisions and tradeoffs. In this free webinar, Applied Motion Products will discuss key factors for mobile robot design, such as performance, safety, and power availability.

Miguel Larios, applications engineer at the company, will explain crucial environmental considerations including IP protection and wheel specifications. He will guide viewers through the essential AGV/AMR specification and feature questions that are critical to ask when selecting the most appropriate motors and gearing for a new system.

Motor selection is increasingly important to include during the design phase because developers, integrators, and users continue to push performance requirements to new levels, according to Larios. He will also explore auxiliary axes for materials handling and jacking axes.

Attendees can learn more about the following in this webinar:

• AGV/AMR performance requirements and the impact on motor selection
• Powering the motion control in your mobile robot
• Safety and environmental considerations around automated systems
• How to select wheels based on operational requirements
• Auxiliary axis types and motion control solutions

“Get Your AGV/AMR Moving! Motion Control in AGVs and AMRs” will be at 2:00 p.m. EDT on Wednesday, May 29, 2024. Register now, and ask questions during the live discussion. The webinar will be accessible on demand after the initial broadcast.

About the speakers, Applied Motion Products

Miguel Larios is an applications engineer at Applied Motion Products Inc. and has worked in industrial automation for five years. During that time, he has worked in both sales and engineering roles and has been exposed to many industries implementing automation. Most recently, Larios has started to work in a product management capacity, focusing on AGV/AMR users.

Founded in 1978, Applied Motion Products has been committed to innovation and advancement of motion-control systems, supplying components to original equipment manufacturers (OEMs). The company sponsoring this webinar said it provides motors and drives for applications where precise control of position, speed, and torque is required. They include robotics, machine control, factory automation, semiconductor handling, packaging machines, and medical devices.

Morgan Hill, Calif.-based Applied Motion Products formed a joint venture in 2014 with Moons’ Shanghai to focus on developing cutting-edge technologies. The partners have research and development centers, as well as more than 200 patents for inventions, utility models, appearances, and software copyrights.

Eugene Demaitre is editorial director for robotics at WTWH Media, which produces The Robot Report, Mobile Robot Guide, RoboBusiness, and the Robotics Summit & Expo. He has extensive experience in business-to-business technology journalism and has participated in conferences worldwide, as well as spoken on many webcasts and podcasts. He is always interested in learning more about robotics. Demaitre has a master’s from the George Washington University and lives in the Boston area.

The post Webinar: Get your mobile robots moving with the right motion control appeared first on The Robot Report.

WiBotic 1kW System (battery not included). | Credit: WiBotic

WiBotic today unveiled a new 1kW wireless charging product for larger-capacity battery systems. The Seattle-based company has made a name for itself in the mobile robotics market by providing contactless charging as an aftermarket alternative to OEM contact-based changing systems. The prior product link included 150-watt, 250-watt, and 300-watt wireless charging options.

The new higher-power 1kW system can operate from any 110V 15A circuit or greater, said WiBotic. A 15A circuit provides about 1.5kW, so its transmitter can deliver enough power through the wireless system to the battery, it explained, but a 20A or 30A breaker would be needed to plug multiple units into the same outlet.

The WiBotic Commander software will manage the charging current based on source power circuit characteristics, so as not to trip a facility power circuit.

WiBotic says contactless charging promises benefits

The advantage of moving from contact charging to wireless charging is twofold, according to WiBotic. 

First, with contact charging, systems need to be manually connected and disconnected to the charger, it said. Some automated guided vehicle (AGV) and self-driving vehicle manufacturers provide autonomous contact chargers, but these systems use proprietary connectors.

End users that deploy mobile robots with contact chargers from more than one OEM will ultimately end up with a wall of dedicated charging stations that can only be used by specific robots. This is inefficient and takes up critical facility floor space, the company noted.

Cypher Robotics mobile robot in a warehouse – with integrated drone pad for aerial inventory – docked to WiBotic’s Edge transmitter. | Credit: WiBotic

“WiBotic’s new wireless 1kW charging platform enhances operations,” stated Peter King, vice president of Cypher Robotics Inc., an Ottowa-based provider of warehouse AMRs and drones. 

“We’ve successfully deployed WiBotic chargers for customers in a range of applications and in some difficult environmental conditions — but historically only in applications where overnight charging was possible,” he said. “With the new 1kW system, robots will charge at three times the previous speed, opening up a whole new set of applications where fast and ultra-reliable charging is needed.”

The second advantage of wireless charging is in the uniformity of the charging infrastructure, said WiBotic. Any available wireless charger station in a facility can be used by any robot in the fleet in need of a recharge.

Depending on the number of mobile robots in an end user’s fleet, this concept can vastly simplify the charging process, claimed the company. Facility operators can place wireless charging stations at various sites so they can used by any autonomous mobile robots (AMRs).

Wireless communication for operational data

When retrofitting a mobile robot with the WiBotic charging system, a smart charger controller unit needs to be installed onto the AMR. This smart charger autonomously manages the power transfer from the wireless charging pad to the mobile robot unit.

WiBotic has an open API [application programming interface] that enables an AMR to easily communicate with the onboard smart charger.

The smart charger then wirelessly communicates with the powered pad to control the flow of energy wirelessly between the source pad and the onboard power receiver. With the new 1 kW power system, it can transfer more energy, more quickly, to larger mobile robots, said WiBotic.

Using a proprietary wireless communication channel, the onboard charger can also relay statistical information and other data, not only about the onboard battery, but also from the robot controller. This data dump is separate from any Wi-Fi or 5G communication connection to the robot and provides OEMs with another option for data transfer and communications.

Ben Waters, co-founder and CEO of WiBotic, described the smart communication feature:

“This higher-power product allows us to get into markets that aren’t robotic, machines like carton handlers, pallet jacks, and floor-cleaning vehicles. There are a lot of battery-powered vehicles out there today that are not yet robotic or autonomous. They’re just dumb devices in the sense that they are not connected devices, and OEMs don’t have a way to remotely talk to them. By adding wireless charging to their devices, they also now can communicate data from the devices. It can be a simple diagnostic, like: ‘How many hours does my floor scrubber have on it?’”

The company has also developed its own management software, WiBotic Commander. Customers can easily monitor the charging status and battery health of entire fleets of robots in real time. Charging procedures can then be implemented proactively to vary charge current and voltage to dramatically increase battery lifespan.

While end users can purchase and deploy the wireless chargers, WiBotic has focused on building relationships with OEMs and helping to integrate their mobile robot controllers to the WiBotic power controller. The WiBotic controller can also work with contact chargers, providing charge management options for any mobile robot power need.

“We [primarily] work with two types of customers,” said Matt Carlson, vice president of business development at WiBotic. “We work with the OEMs offering the robots as a service, but they’re the company who builds the robot, and then they provide it to the end customer. So with those customers, we work with their engineering teams very closely.”

“In some cases, we work with the end customer, where they might operate multiple robots or different types of carts,” he added. “And for one reason or another, charging is a huge problem for them. It’s a big cost because at the end of every day, some employee has to charge all the batteries on all of the robots, to prepare for the next day.”

The Wibotic Commander software is a ‘single pane of glass’ for managing the charging of a robotic fleet. | Credit: WiBotic

Higher-frequency charging helps prevent inductive heating

Another key feature of WiBotic’s system, according to Waters, is that it uses higher radio frequencies for energy transfer than some of its competitors.

“Some of these inductive systems operate at a lower frequency, typically between 50 and 200 kilohertz,” he told The Robot Report. “WiBotic uses 6.78 megahertz, which is still way below all your Wi-Fi [communication].”

WiBotic said its platform reduces safety risks from electrical shorts and fire risk from sparking across contacts, making it suitable for environments where dirt, dust, water or corrosion can lead to failed charging cycles.

“But there are some important technical advantages, especially at the higher power levels,” said Waters. “One is that we don’t heat foreign objects. Inductive heating systems are also in this range, so if there is some loose metal around, it can heat up [as a side effect]. A lot of [our competitors] have very robust foreign object detection, which is great. This will prevent metal objects from heating up, but it’s going to stop charging.”

“So operating at the higher frequency and adding in some of the things that we do around our adaptive tuning [means] we don’t have this phenomenon of heating metal objects,” he said.

WiBotic plans to demonstrate its new 1kW system at Booth 4087 near the ARM Demo Area at Automate in Chicago next week.

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