Coverage Path Planning In Belief Space

For safety reasons, robotic lawn mowers and similar devices are required to stay within a predefined working area. Keeping the robot within its workspace is typically achieved by special safeguards such as a wire installed in the ground. In the case of robotic lawn mowers, this causes a certain customer reluctance. It is more desirable to fulfill those safety-critical tasks by safe navigation and path planning. In this paper, we tackle the problem of planning a coverage path composed of parallel lanes that maximizes robot safety under the constraints of cheap, low range sensors and thus substantial uncertainty in the robot's belief and ability to execute actions. Our approach uses a map of the environment to estimate localizability at all locations, and it uses these estimates to search for an uncertainty-aware coverage path while avoiding collisions. We implemented our approach using C++ and ROS and thoroughly tested it on real garden data. The experiment shows that our approach leads to safer meander patterns for the lawn mower and takes expected localizability information into account.