A New Path Planning Architecture to Consider Motion Uncertainty in Natural Environment

This paper proposes a new path planning algorithm to consider motion uncertainty for wheeled robots in rough environments. The proposed method uses particles to express the uncertainty propagation in complicated environments constructed with various types of terrain. Also, RRT (Rapidly-exploring Random Tree) is expanded based on the uncertainty of each node in order to prevent increasing the accumulated position uncertainty. As a result, the generated path reduces the times of path-following and re-planning based on inaccurate localization information. The effectiveness of the proposed method is evaluated in simulation using motion uncertainty models obtained by experiments. The results show that the proposed method decreases the position uncertainty while keeping the probability to avoid collisions and to reach the goal area compared with conventional approaches.