Faster RRT-based Nonholonomic Path Planning in 2D Building Environments Using Skeleton-constrained Path Biasing
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This paper presents a faster RRT-based path planning approach for regular 2-dimensional (2D) building environments. To minimize the planning time, we adopt the idea of biasing the RRT tree-growth in more focused ways. We propose to calculate the skeleton of the 2D environment first, then connect a geometrical path on the skeleton, and grow the RRT tree via the seeds generated locally along this path. We conduct batched simulations to find the universal parameters in manipulating the seeds generation. We show that the proposed skeleton-biased locally-seeded RRT (skilled-RRT) is faster than the other baseline planners (RRT, RRT*, A*-RRT, Theta*-RRT, and MARRT) through experimental tests using different vehicles in different 2D building environments. Given mild assumptions of the 2D environments, we prove that the proposed approach is probabilistically complete. We also present an application of the skilled-RRT for unmanned ground vehicle. Compared to the other baseline algorithms (Theta*-RRT and MARRT), we show the applicability and fast planning of the skilled-RRT in real environment.
KeywordsPath planning Rapidly-exploring random tree (RRT) Skilled-RRT Unmanned ground vehicle
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The research was partially supported by the ST Engineering-NTU Corporate Lab through the NRF corporate lab@university scheme. The authors are also indebted to Mr. Mohhamadali Askari Hemmat from Department of Mechanical and Industrial Engineering in Concordia University Canada for the discussion on this idea, and Mr. Reinaldo Maslim from School of Mechanical and Aerospace Engineering in Nanyang Technological University for the real test.
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