Closer To Optimal Angle-Constrained Path Planning
2019 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)(2019)
摘要
Planning on grids and planning via sampling are the two classical mainstreams of path planning for intelligent agents, whose respective representatives are A* and RRT, including their variants, Theta* and RRT*. However, in the nonholonomic path planning, such us being under angle constraints, Theta* and Lazy Theta* may fail to generate a feasible path because the line-of-sight check (LoS-Check) will modify the original orientation of a state, which makes the planning process incomplete (cannot visit all possible states). Then, we propose a more delayed evaluation algorithm called Late LoS-Check A* (LLA*) to relax the angle constraints. Due to the nature of random sampling, RRT* is asymptotically optimal but still not optimal, then we propose LoS-Check RRT* (LoS-RRT*). In order to solve the problems caused by improper settings of the planning resolution, we propose the LoS-Slider (LoSS) smoothing method. Through experimental comparison, it can be found that angle-constrained versions of LLA* and LoS-RRT* can both generate the near-optimal paths. Meanwhile, the experiment result shows that LLA* performs better than Theta* and Lazy Theta* under angle constraints. The planned path will be even closer to the optimal (shortest) solution after the smoothing of LoSS algorithm.
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关键词
path planning, line-of-sight, angle constraint, near-optimal
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