RDA: An Accelerated Collision Free Motion Planner for Autonomous Navigation in Cluttered Environments

Ruihua Han1; Shuai Wang2; Shuaijun Wang3; Zeqing Zhang1; Qianru Zhang1; Yonina C. Eldar4; Qi Hao5; Jia Pan1

2023

10.1109/LRA.2023.3242138

IEEE Robotics and Automation Letters   影响因子和分区

2377-3774

2377-3766

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.

Collision avoidance constrained motion planning optimization and optimal control

SCI ; EI

英语

其他

Hong Kong SAR Research Grants Council (RGC) General Research Fund (GRF)["11202119","11207818"] ; Science and Technology Innovation Committee of Shenzhen City[JCYJ20200109141622964] ; National Natural Science Foundation of China["62261160654","62001203"]

Robotics

Robotics

WOS:000935281600007

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

20230813624701

Constrained optimization ; Mobile robots ; Model predictive control ; Motion planning ; Predictive control systems ; Robot programming

Computer Programming:723.1 ; Control Systems:731.1 ; Robotics:731.5 ; Accidents and Accident Prevention:914.1 ; Systems Science:961

2-s2.0-85148455314

IEEE

1.Department of Computer Science, The University of Hong Kong, Hong Kong
2.Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
3.Department of Computer Science and Engineering, Harbin Institute of Technology, Shenzhen, Guangdong, China
4.Weizmann Institute of Science, Rehovot, Israel
5.Department of Computer Science and Engineering, the Shenzhen Key Laboratory of Robotics and Computer Vision, and the Sifakis Research Institute for Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, Guangdong, China

Ruihua Han,Shuai Wang,Shuaijun Wang,et al. RDA: An Accelerated Collision Free Motion Planner for Autonomous Navigation in Cluttered Environments[J]. IEEE Robotics and Automation Letters,2023,PP(99):1-8.

Ruihua Han.,Shuai Wang.,Shuaijun Wang.,Zeqing Zhang.,Qianru Zhang.,...&Jia Pan.(2023).RDA: An Accelerated Collision Free Motion Planner for Autonomous Navigation in Cluttered Environments.IEEE Robotics and Automation Letters,PP(99),1-8.

Ruihua Han,et al."RDA: An Accelerated Collision Free Motion Planner for Autonomous Navigation in Cluttered Environments".IEEE Robotics and Automation Letters PP.99(2023):1-8.