Probabilistic motion planning for non-Euclidean and multi-vehicle problems

被引:3
|
作者
Lukyanenko, Anton [1 ]
Soudbakhsh, Damoon [2 ]
机构
[1] George Mason Univ, Math Dept, Fairfax, VA 22030 USA
[2] Temple Univ, Dept Mech Engn, Philadelphia, PA 19122 USA
关键词
Nonholonomic motion planning; Motion and trajectory planning; Trajectory planning for multiple mobile~robots; Cooperative robots and multi-robot; systems; RRT; TRAJECTORIES; ALGORITHM; ROADMAPS; PATHS;
D O I
10.1016/j.robot.2023.104487
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Trajectory planning tasks for non-holonomic or collaborative systems are naturally modeled by state spaces with non-Euclidean metrics. However, existing proofs of convergence for sample-based motion planners only consider the setting of Euclidean state spaces. We resolve this issue by formulating a flexible framework and set of assumptions for which the widely-used PRM*, RRT, and RRT* algorithms remain asymptotically optimal in the non-Euclidean setting. The framework is compatible with collaborative trajectory planning: given a fleet of robotic systems that individually satisfy our assumptions, we show that the corresponding collaborative system again satisfies the assumptions and therefore has guaranteed convergence for the trajectory-finding methods. Our joint state space construction builds in a coupling parameter 1 <= p <= 8, which interpolates between a preference for minimizing total energy at one extreme and a preference for minimizing the travel time at the opposite extreme. We illustrate our theory with trajectory planning for simple coupled systems, fleets of Reeds-Shepp vehicles, and a highly non-Euclidean fractal space. (c) 2023 Elsevier B.V. All rights reserved.
引用
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页数:11
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