A Decision Algorithm for Motion Planning of Car-Like Robots in Dynamic Environments
Elias K. Xidias · Cybernetics & Systems · 2021
Motion planning in dynamic environments is essential for many applications such as in search and rescue missions and in servicing tasks. In this paper, I present a new approach for motion planning for an autonomous mobile robot which is requested to operate in a dynamic environment. The robot’s working environment is cluttered with static obstacles with a priori knowledge of their shapes and positions and with moving obstacles with unknown geometry and motion. In order to ensure a safe motion for the robot I propose a two-stage approach. First, using the Bump-Surface concept I construct a path by considering only the static obstacles of the environment. Then, the robot starts its motion on the given path. When the robot detects a potential danger situation (i.e., collision), a decision algorithm tries to evaluate the risk of collision and simultaneously to find the safest action for the robot. The proposed approach is evaluated in randomly generated simulated scenarios.