Motion Planning for Nonlinear Robotic System based on ADMM and Convex Feasible Set Algorithm

Ruishuang Chen; Zaiyue Yang; Jie Cheng; Zhihui Liang

10.1109/CDC51059.2022.9992594

2022

IEEE 61st Conference on Decision and Control (CDC)

0743-1546

2576-2370

978-1-6654-6762-9

2022 IEEE 61st Conference on Decision and Control (CDC)

7461-7466

6-9 Dec. 2022

Cancun, Mexico

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

In the motion planning of robot, nonlinear dynamic constraints and obstacle avoidance constraints make the problem highly non-convex and difficult to solve. In this paper, a general algorithmic framework for trajectory optimization is proposed based on alternating direction method of multipliers (ADMM) and convex feasible set algorithm (CFS). The ADMM framework decomposes the original problem into an optimal control sub-problem with only dynamic constraints and a sub-problem with only obstacle avoidance constraints, while the sub-problem with obstacle avoidance constraints is solved via CFS based on the geometric properties of the feasible region. The convergence of proposed algorithm is proven, and the simulation results in different cases verify the feasibility and effectiveness of proposed algorithm.

Heuristic algorithms Simulation Dynamics Convex functions Planning Nonlinear dynamical systems Collision avoidance

第一

英语

CPCI-S

National Key Research and Development Program of China[2019YFB1705401]

Automation & Control Systems ; Engineering

Automation & Control Systems ; Engineering, Electrical & Electronic

WOS:000948128106037

IEEE

Department of Mechanical and Energy Engineering, and the Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities, Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems, Southern University of Science and Technology, Shenzhen, China

Ruishuang Chen,Zaiyue Yang,Jie Cheng,et al. Motion Planning for Nonlinear Robotic System based on ADMM and Convex Feasible Set Algorithm[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2022:7461-7466.