A low-risk approach to mobile robot path planning
This paper presents a self-organizing approach for mobile robot path planning problems in dynamic environments by using case-based reasoning together with a more conventional method of grid-map based path planning. The map-based path planner is used to suggest new innovative solutions for a particular path planning problem. The case-base is used to store the paths and evaluate their traversability. While planning the route those paths are preferred which, according to former experience, are least risky. As the environment changes, the exploration as well as the evaluation of the paths will allow the system to self-organize by forming a set of low-risk paths that are safest to follow. The experiments in a simulated environment show that the robot is able to adapt in a dynamic environment and learns to use the least risky paths.
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