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Journal article
RDA: An Accelerated Collision-free Motion Planner for Autonomous Navigation in Cluttered Environments
IEEE Robotics and Automation Letters, Vol.8(3), pp.1715 -1722
03/Feb/2023
Abstract
Autonomous motion planning is challenging in multi-obstacle environments due to nonconvex collision avoidance constraints. Directly applying numerical solvers to these nonconvex formulations fails to exploit the constraint structures, resulting in excessive computation time. In this letter, we present an accelerated collision-free motion planner, namely regularized dual alternating direction method of multipliers (RDADMM or RDA for short), for the model predictive control (MPC) based motion planning problem. The proposed RDA addresses nonconvex motion planning via solving a smooth biconvex reformulation via duality and allows the collision avoidance constraints to be computed in parallel for each obstacle to reduce computation time significantly. We validate the performance of the RDA planner through path-tracking experiments with car-like robots in both simulation and real-world settings. Experimental results show that the proposed method generates smooth collision-free trajectories with less computation time compared with other benchmarks and performs robustly in cluttered environments.
Details
- Title
- RDA: An Accelerated Collision-free Motion Planner for Autonomous Navigation in Cluttered Environments
- Creators
- Ruihua Han - Southern University of Science and TechnologyShuai Wang - Chinese Academy of SciencesShuaijun Wang - Harbin Institute of TechnologyZeqing Zhang - University of Hong KongQianru Zhang - University of Hong KongYonina C Eldar - 972WIS_INST___83Qi Hao (Corresponding Author) - Southern University of Science and TechnologyJia Pan (Corresponding Author) - University of Hong Kong
- Resource Type
- Journal article
- Publication Details
- IEEE Robotics and Automation Letters, Vol.8(3), pp.1715 -1722; 03/Feb/2023
- Number of pages
- 8
- Publisher
- IEEE
- Language
- English
- DOI
- https://doi.org/10.1109/LRA.2023.3242138
- Grant note
- This letter was recommended for publication by Associate Editor C. I. Vasile and Editor L. Pallottino upon evaluation of the reviewers’ comments. This work was supported in part by Hong Kong SAR Research Grants Council (RGC) General Research Fund (GRF) under Grants 11202119 and 11207818, in part by Innovation and Hong Kong SAR Technology Commission under the InnoHK initiative, in part by the Science and Technology Innovation Committee of Shenzhen City under Grant JCYJ20200109141622964, and in part by the National Natural Science Foundation of China under Grants 62261160654 and 62001203.
- Record Identifier
- 993403282803596
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