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EffMoP: Efficient Motion Planning Based on Heuristic-Guided Motion Primitives Pruning and Path Optimization With Sparse-Banded Structure
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  • Jian Wen ,
  • Xuebo Zhang ,
  • Haiming Gao ,
  • Jing Yuan ,
  • Yongchun Fang
Xuebo Zhang
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Haiming Gao
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Jing Yuan
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Yongchun Fang
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Abstract

To solve the autonomous navigation problem in complex environments, an efficient motion planning approach called EffMoP is presented in this paper. Considering the challenges from large-scale, partially unknown complex environments, a three-layer motion planning framework is elaborately designed, including global path planning, local path optimization, and time-optimal velocity planning. Compared with existing approaches, the novelty of this work is twofold: 1) a novel heuristic-guided pruning strategy of motion primitives is proposed and fully integrated into the state lattice-based global path planner to further improve the computational efficiency of graph search, and 2) a new soft-constrained local path optimization approach is proposed, wherein the sparse-banded system structure of the underlying optimization problem is fully exploited to efficiently solve the problem. We validate the safety, smoothness, flexibility, and efficiency of EffMoP in various complex simulation scenarios and challenging real-world tasks.
Oct 2022Published in IEEE Transactions on Automation Science and Engineering volume 19 issue 4 on pages 2762-2775. 10.1109/TASE.2021.3128521