Obstacle Avoidance: Reliable and Safe Navigation for AMRs
Autonomous Mobile Robots (AMRs) use maps of your warehouse to find their way around, but maps like these only include obstacles that are permanent or semi-permanent, like walls and shelves. There’s a lot going on in your warehouse every day that isn’t included on these maps, ranging from boxes and pallets, to workstations that move around occasionally, to obstacles that are in constant motion, like forklifts or people. These are all obstacles that your AMRs will have to both reliably detect and safely avoid.
The primary navigation sensor that most AMRs use is LiDAR— a laser that’s able to detect objects out to a distance of several tens of meters, usually with a very wide field of view. LiDAR offers very long range and very high accuracy, but on most AMRs, it only operates in a single fixed plane, meaning that it can see a narrow slice of the world at approximately the height of your shins, but nothing above or below that. For navigating, this isn’t much of a problem, but it can make obstacle avoidance difficult because the LiDAR can’t see obstacles that are close to the ground (like pallets or feet) or anything that’s hanging above the ground. To take just one example, a LiDAR sensor would have no trouble detecting the four legs of a table, but it would have no way of detecting the top of the table, and might try to pass underneath while carrying a load. Obstacles like these can pose a risk for AMRs that use only LiDAR for obstacle avoidance.
Pallet Jack moving in front of robot equipped with only LiDAR
One solution is to use what’s called sensor fusion: combining the data from multiple sensors to get a much better picture of the world. LiDAR has great range and accuracy but it can only see in one plane, so you add another sensor that complements it, like a shorter range 3D camera that can see the areas both close to the ground and above the robot. Not all AMRs are equipped with multiple sensors, and some configurations of sensors work better than others, so it’s important to make sure that your AMR can detect all of the obstacles it’s likely to encounter in your warehouse.
Pallet Jack moving in front of robot equipped with LiDAR and 3D camera
Obstacle detection is only half of the obstacle avoidance problem: once the AMR recognizes an obstacle, it needs to make the correct decision about what to do next. For obstacles that aren’t moving, this isn’t very difficult, and most AMRs have no trouble planning a safe path around them. However, when an AMR detects a moving obstacle, like a forklift, the problem becomes more complicated, because treating a moving obstacle like a static obstacle could lead to a collision. The robot needs to understand where the forklift is headed, making an intelligent prediction about its motion in order to avoid it effectively.
No matter how many sensors an AMR has, or how well it’s able to avoid static and dynamic obstacles, there will always be situations that prove to be particularly challenging for an autonomous robot. For example, some sensors may at times be blocked by one obstacle, preventing the AMR from detecting a different obstacle. It’s critical that the AMR be able to handle situations like these, by properly assessing what it does and doesn’t know about the environment around it and reacting safely when it’s missing information.
Avoiding obstacles is one of the things that makes autonomous mobile robots so much more versatile and valuable than the previous generation of autonomous ground vehicles: rather than having to adjust your workflow around the robots, investing in the right AMR means that you can have the confidence that your robots will be able to robustly adapt to your existing warehouse environment. Make sure that you carefully evaluate your AMR options, and that the AMR you choose has the hardware and software necessary to make navigating your warehouse both reliable and safe.
Learn More About Fetch Robotics
Learn more about how Fetch Robotics’ solutions improve throughput, efficiency, and productivity while working alongside people