Autonomous Vehicle Control: A Nonconvex Approach for Obstacle Avoidance

被引:112
|
作者
Rosolia, Ugo [1 ]
De Bruyne, Stijn [2 ]
Alleyne, Andrew G. [3 ]
机构
[1] Univ Calif Berkeley, Mech Engn, Berkeley, CA 94704 USA
[2] McKinsey & Co Inc, B-1050 Brussels, Belgium
[3] Univ Illinois, Mech Sci & Engn, Urbana, IL 61801 USA
关键词
Autonomous vehicle; collision avoidance; nonconvex optimization; nonlinear model predictive control (NLMPC); path following; real-time optimization; CONTINUATION/GMRES METHOD; NONLINEAR MPC; ALGORITHM;
D O I
10.1109/TCST.2016.2569468
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper develops a two-stage nonlinear nonconvex control approach for autonomous vehicle driving during highway cruise conditions. The goal of the controller is to track the centerline of the roadway while avoiding obstacles. An outer-loop nonlinear model predictive control is adopted for generating the collision-free trajectory with the resultant input based on a simplified vehicle model. The optimization is solved through the generalized minimal residual method augmented with a continuation method. A sufficient condition to overcome limitations associated with continuation methods is introduced. The inner loop is a simple linear feedback controller based on an optimal preview distance. Simulation results illustrate the effectiveness of the approach. These are bolstered by scaled-vehicle experimental results.
引用
收藏
页码:469 / 484
页数:16
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