Collision-Free Navigation of Autonomous Vehicles Using Convex Quadratic Programming-Based Model Predictive Control

• Published in: IEEE/ASME transactions on mechatronics 2018-06, Vol.23 (3), p.1103-1113
• Main Authors: Wang, Zhuping, Li, Gangbin, Jiang, Houjie, Chen, Qijun, Zhang, Hao
• Format: Article
• Language: English
• Subjects: Autonomous vehicles; Collision-free navigation; convex quadratic programming (CQP); model predictive control (MPC); Navigation; Optimization; real time; Real-time systems; Shape; Trajectory; Trajectory tracking; vehicle shape
• ISSN: 1083-4435; 1941-014X
• DOI: 10.1109/TMECH.2018.2816963

Collision-free navigation of autonomous vehicles by means of convex quadratic programming (CQP) based model predictive control (MPC) is considered in this paper. A new collision-free navigation function is designed for real-time collision avoidance of an autonomous vehicle in both static and dynamic environments. Furthermore, vehicle shape is taken into consideration during trajectory generation as a convex polygonal region defined by linear constraints rather than a single point. Then, the MPC optimization problem with the vehicle shape is solved as a CQP-based MPC scheme in the sense of path planning. Compared to the previous MPC, which can only be reduced to a nonlinear programming problem, the control sequences of CQP-based MPC can be obtained quickly with improved real-time system performance. Simulations in diverse scenarios, including a real vehicle dataset, show the validity of the proposed approach.