Lars Kuhnert, Duong V. Nguyen, Stefan Thamke, and Klaus D. Kuhnert
autonomous mobile outdoor robot, exploration, sampling-based path planning
We present a novel approach to explorative path planning for an autonomous mobile outdoor robot. The focus of the proposed method lies on the robust generation of a set of paths that allow the efficient exploration of a previously unknown unstructured outdoor environment. This is achieved by application of a randomized sampling-based path planning approach based on the concept of Rapidly Exploring Random Trees (RRT) used in conjunction with a post-planning analysis of the generated path tree. We introduce the innovative concept of exploration nodes as extension of the basic RRT algorithm. The outcome of the proposed method is a tree of kinematically feasible paths which includes a set of exploration paths that lead to unknown parts of a mobile robot's local environment. This approach is fundamentally different to previously developed RRT-based methods as the principal goal is practical exploration of the unknown instead of planning towards a single local goal. For a complete understanding of the proposed method, we cover the sub-tasks of local map building, path tree generation and path tree analysis within this work. Finally, we demonstrate results of the proposed algorithms from experiments conducted on our autonomous mobile outdoor robot AMOR.
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