Skip to main content

AI

Autonomous Mobile Robots Emerge from the Factory Floor

Autonomous mobile robots

What once seemed liked science fiction is now becoming a reality. Autonomous mobile robots—AMRs—are gaining real traction in the manufacturing space these days. But they are also poised to explode into any number of other contexts—from hospitality to health care—getting more intelligent and more independent all the time. The idea is to reduce the burden on human workers who would otherwise be doing certain repetitive or hazardous tasks themselves, as well as to work alongside those humans.

Unsurprisingly, there’s a lot that goes into getting these robot systems to sense their environments, conduct operations, and implement orders. It requires high-intensity computing from the technology, and flexibility and scalability from the designers. Claire Liu, Product Marketing Manager at congatec, an embedded computer modules supplier; and Timo Kuehn, Systems Architect and Product Manager at Real-Time Systems, a provider of embedded and real-time solutions, explain this fast-changing industrial trend for us (Video 1).

Video 1. Congatec’s Claire Liu and Real-Time Systems Timo Kuehn discuss the key components necessary for successful autonomous mobile robot development and deployment. (Source: insight.tech)

What actually are autonomous mobile robots?

Claire Liu: Autonomous mobile robots are systems capable of operating independently, without direct human intervention. They are equipped with defense sensors, artificial intelligence algorithms, and a sophisticated control system that enables them to navigate autonomously, to perceive their environment, and to make decisions.

Autonomous mobile robots rely on a combination of technologies, such as various sensors—for example, LiDARs or 2D or 3D cameras—to perceive their environment. That sensor data is processed in real time by the computing platform to profile information about that environment. The robot can then use this information to create a map to localize and navigate itself within the environment.

The manufacturing industry is increasingly interested in autonomous mobile robots because they can do things like material-handling tasks—picking up and delivering raw material and in-process products on the production line. These are repetitive tasks that used to be executed manually and can pose a risk to workers’ health and safety. Now workers don’t have to waste their production time to do that manual work; they can focus on highly skilled and more value-added tasks instead.

Using autonomous mobile robots in the manufacturing environment streamlines the manufacturing process and can increase productivity, improve operational efficiency, and enhance workers’ safety.

Talk about the software architecture that goes into AMRs.

Timo Kuehn: A lot of software goes into AMRs, of course. There are various functionalities, like perception, as Claire mentioned. The robot has to perceive its environment in order to know what’s going on; it has to find out where it’s situated at any one moment; it needs to find out where to move to. The movement itself, the motion control, is very important: There’s obstacle avoidance, of course; there’s also interaction with humans, depending on the type of robot and the diagnostics.

Those software functions have to be mapped by corresponding software modules, and often they have very high requirements in terms of timing and resource usage—even competing requirements. For example, if one software module needs a lot of performance, while a different software module needs a deterministic response in a timely manner, you cannot just throw everything in and expect it to work. It is quite complex.

For motion control, especially, it can be quite challenging. It needs determinism: It needs to react to sensor signals within a predefined time frame. And the time frame depends on various factors like: Does it have wheels? Does it have axes? How many axes have to be controlled? What is the speed of the AMR? What precision is needed? Is the device moving in two dimensions or three dimensions? Is the load dynamically added or unloaded?

Typically, real-time operating systems are used in order to have party-based scheduling and to make sure that deadlines are never missed. Critical tasks, like perception or motion control, get higher priority so that they aren’t interrupted by lower-priority tasks. This resource allocation and optimization is provided by the operating system or the software architecture.

“Using #autonomous mobile #robots in the manufacturing environment streamlines the #manufacturing process and can increase productivity, improve operational efficiency, and enhance workers’ safety” — Claire Liu, @congatecAG via @insightdottech

Tell us more about taking a modular approach.

Claire Liu: The congatec computer modules seamlessly leverage Intel processor-technology scale—from low power to high-computing performance—enabling developers to develop their robots to work longer and smarter and to perform complex tasks with higher proficiency and efficiency.

The Intel® 13th Gen Core processors are an ideal solution with congatec’s computer modules because they combine power, efficiency, flexibility, and performance. MrCoMs can now benefit from these latest Intel processors to run more applications simultaneously and to run more workloads and more connected devices.

Developers can quickly and easily adapt to the latest Intel processor technologies through a simple module change, and they can add intelligence to their autonomous mobile robots even after years of operation. Additionally, there’s the Intel OpenVINO toolkit, which provides optimized AI influence models and comprehensive support for developers.

What other tools and technologies go into developing autonomous mobile robots?

Timo Kuehn: The development of AMRs requires a combination of hardware, software, and connectivity. In terms of hardware, there’s the computing platform, the chassis, the motor, the sensor power system, and, of course, whichever sensors are being used depending on the requirements of the application. The software side deals with perception, localization, path planning, motion control, and obstacle avoidance. Diagnostics and interaction with humans also play very important roles. So integrating and managing all of those functions can get quite complex.

AMRs are battery powered, so adding a lot of controllers doesn’t make sense. Those controllers need to be connected, which adds weight and increases the size, costs, and complexity. So multiple functions have to be consolidated on fewer processors.

And here is where an embedded real-time hypervisor can help a lot, integrating multiple workloads on a single processor. There are many advantages to that functionality—isolation and security, for example. So, say, perception and motion control can run securely separated from each other in their own virtual machines, making sure that when one VM needs a lot of load or creates a lot of load, the other one is not affected and can still meet its deadlines.

And this is really crucial. Imagine there’s a signal from a sensor and the reaction from the AMR or from the controller comes too late. This can lead to a crash—even to injuries, when humans are involved. It also helps with performance optimization and load balancing; every VM can get dedicated resources to meet timing and performance requirements.

What are some of the use cases you’re seeing with AMRs?

Claire Liu: Autonomous mobile robots have proven to be versatile in various industries. There’s the material handling in the manufacturing environment that I mentioned earlier, and even collaborative assembling as well. There’s logistics and fulfillment for e-commerce. During the pandemic, autonomous mobile robots were utilized for delivering medical supplies and medication and for assisting with patient care. And there are more and more applications in other areas like agriculture, hospitality, and retail. New use cases are consistently emerging.

Timo Kuehn: Environmental monitoring is a good use case for AMRs, in order to collect data on air quality, water quality, or soil conditions. Or in hazardous environments—for example, for inspection of power plants—which reduces the risk for human workers. They can be used in public places to provide real-time video feeds. Or in large facilities they can be used in last-mile delivery to transport packages. They can assist in material transportation, also in construction projects. There are really a lot of different use cases, and I agree with Claire that there will be even more in the future.

Where can we expect this field to go over the next couple of years?

Claire Liu: There will be new and exciting possibilities in the field of AMRs in the near future. Technological development will evolve rapidly in the robotics area, with a modular approach to the software-architecture design. Autonomous mobile robot companies will adapt to the fast-changing environment and bring this cutting-edge solution to life with great scalability.

Timo Kuehn: Of course, it’s hard to predict, but I’m sure there will be many advancements in the near future, especially in regard to Intel processors with integrated AI accelerators. This will lead to enhanced perception and object recognition, more intelligent path planning and optimization, and adaptive-learning capabilities. What we can also imagine is improved collaboration between humans and robots—things like the capability to make complex decisions in real time in order to assess situations and execute complicated tasks with only a little bit of human intervention.

To summarize: The combination of virtualization technology, real-time capabilities, and integrated AI accelerators has a high potential for completely new types of autonomous mobile robots. They will become more intelligent, adaptable, and capable of performing complex tasks with high precision and efficiency.

Related Content

To learn more about autonomous mobile robots, listen to Inside the Development of Autonomous Mobile Robots, and read IoT Virtualization Jump-Starts Collaborative Robots. For the latest innovations from congatec and Real-Time Systems, follow them on Twitter at @congatecAG and LinkedIn at congatec and Real-Time Systems GmbH.
 

This article was edited by Erin Noble, copy editor.

About the Author

Christina Cardoza is an Editorial Director for insight.tech. Previously, she was the News Editor of the software development magazine SD Times and IT operations online publication ITOps Times. She received her bachelor’s degree in journalism from Stony Brook University, and has been writing about software development and technology throughout her entire career.

Profile Photo of Christina Cardoza