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

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...

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Bibliographic Details
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
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Summary: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.
ISSN:1083-4435
1941-014X
DOI:10.1109/TMECH.2018.2816963