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Horizon-based lazy optimal RRT for fast, efficient replanning in dynamic environment

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Abstract

Planning of collision-free trajectory for robot motion under hard constraints and unpredictable environment is a difficult issue. To cope with this problem, this paper presents a novel replanning method based on receding horizon control and asymptotically optimal single-query motion planning. The approach, called the horizon-based lazy optimal rapidly-exploring random tree algorithm, enables real-time replanning in both static and dynamic environment with updated information. Previous feasible solutions are fully considered to generate new plans. Contributions include lazy steering and lazy collision checking search tree, forward tree pruning and sampling distribution online learning. The techniques are proven to be efficient, near optimal and fast responsive to change. Moreover, three experiments are performed to test the properties of the proposed algorithm numerically.

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Authors and Affiliations

  1. School of Astronautics, Harbin Institute of Technology, Harbin, Heilongjiang, People’s Republic of China

    Ying Chen, Zhen He & Shunli Li

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  1. Ying Chen
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  2. Zhen He
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  3. Shunli Li
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Correspondence to Shunli Li.

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Chen, Y., He, Z. & Li, S. Horizon-based lazy optimal RRT for fast, efficient replanning in dynamic environment. Auton Robot 43, 2271–2292 (2019). https://doi.org/10.1007/s10514-019-09879-8

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