Obstacle Avoidance of Autonomous Vehicles with CQP-based Model Predictive Control

被引：0

作者：

Jiang, Houjie ^{[1
]}

Wang, Zhuping ^{[1
]}

Chen, Qijun ^{[1
]}

Zhu, Jin ^{[1
]}

机构：

[1] Tongji Univ, Coll Elect & Informat Engn, Shanghai, Peoples R China

来源：

2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) | 2016年

关键词：

CQP-based model predictive control; obstacle avoidance; autonomous vehicles; real time performance; FREE NAVIGATION; ENVIRONMENTS;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, an approach for real time obstacle avoidance of autonomous vehicles is presented. A model predictive control (MPC) scheme based on convex quadratic programming (CQP) is developed to generate safety trajectories. To reduce the computational burden in optimizing the performance index of MPC, linear time-varying MPC is adopted and a unique single dimension artificial potential fields (SDAPF) method to utilize the obstacle information is proposed. Autonomous vehicles with proposed method can track the desired path if there is no obstacle on it and avoid both static and dynamic obstacles if the path is occupied. Simulation results show the validity of the approach and its superior real time performance, which is critical to autonomous vehicles.

引用

页码：1668 / 1673

页数：6

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