A Motion Planning Algorithm Based on Uncertainty Prediction
Tianyuan Gu, Song Ke-pu, Changxiu Miao · 2013
Considering the remarkable influence by uncertainty on a rapidly moving robot, a new motion planning algorithm is proposed. Firstly, an actual state-based trajectory uncertainty prediction method suitable for multiple blind areas is presented, which can effectively predict the deviation between actual trajectories to come and the planned trajectory. On this basis, the rapidly-exploring random trees algorithm is introduced to implement the motion planning under uncertainty. Simulations demonstrated the feasibility of the algorithm. This motion planning algorithm can effectively solve problems in the speed robot navigating through complex environment with multiple blind regions.