Vision Challenges for Autonomous Mobile Robots
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To function properly, an autonomous mobile robot (AMR) must navigate safely through known and unknown environments while carrying out its assigned task. The robot needs to build a model or map, estimate its current position, and navigate to target points. Mapping, localization, and navigation are classic problems for mobile robots. Finding a good solution to these problems is needed as robots become totally autonomous, adaptable to changing circumstances, and expand their range of application.
The increase of navigation autonomy in mobile robots creates benefits for companies that use them. But such a system has its challenges. Developing these systems with off-the-shelf hardware and sensors is essential to their growth. Many robotic developers want to create their own autonomous systems. But what stands in their way?
Dynamic environments like airports, malls, and warehouses make it especially difficult to create good software. These environments have tight spaces and continuously changing obstacles. Therefore, complex routes must be created. End-users demand that the software involves minimal training, no environmental setup, and single shot learning.
Much of the problem that stands in the way of autonomous robots is that edge cases don’t come up until the robot is being used in a live environment. Sometimes it’s easier for a robot to navigate in a crowded environment with a lot of features than a big wide-open space. In that case, there’s nothing to link to a map.
Programmers can’t anticipate every case. Customers have to take time to troubleshoot issues in real time to improve efficiency. For example, a robot can mistake light from a reflection for a physical object; or infrared heaters may disrupt the robot’s path. The only way to solve this problem is with data collection.
Human detection is key for end-user applications. One can’t very well have a robot running into people. But a robot also can’t be overly cautious. If the robot constantly believes it’s seeing people, it will check, pause, and analyze over and over again. Seeing the robot do this can actually make people more nervous around it. In these cases, it appears as though the machine is confused, making humans unsure of what it’s going to do next. Data from real-world scenarios and virtual environments must be used to reduce false-positives.
If a mobile robot solution is going to be scalable, installation must be simple. Today’s robots often require an engineer for installation in a new environment. Simple systems now ask users to do only one of two things: choose a route or teach a route. The system has the user navigate with a machine the way they always have. While they do so, the robot creates a map and records the route. And it’s ready for the user to playback the route next time.
Autonomous mobile robots are becoming ubiquitous in a number of commercial, retail, healthcare and industrial applications. As the technology advances, programmers are introducing innovative ways to facilitate easy implementation and ways for the robot to successfully navigate different environments.
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