Model Predictive Control of Autonomous Vehicles Using Sequence Convex Programming

A planner for autonomous vehicles is presented in this paper. The purpose of this algorithm is to plan a collision-avoidance trajectory with cheap computation. We consider model predictive control (MPC) of the vehicle in the presence of obstacles, in which the optimization usually admits nonlinear p...

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Vydáno v:	2022 37th Youth Academic Annual Conference of Chinese Association of Automation (YAC) s. 925 - 929
Hlavní autoři:	Wang, Haoyue, Sun, Zhongqi, Deng, Yunshan, Xia, Yuanqing
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 19.11.2022
Témata:	autonomous vehicles Computational complexity Iterative methods mode1 predictive control Numerical models Prediction algorithms Programming Real-time systems sequential convex planning Trajectory trajectory planning
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Popis
Shrnutí:	A planner for autonomous vehicles is presented in this paper. The purpose of this algorithm is to plan a collision-avoidance trajectory with cheap computation. We consider model predictive control (MPC) of the vehicle in the presence of obstacles, in which the optimization usually admits nonlinear programming. By introducing sequential programming (SCP), the nominal trajectory dependent by convexification is updated in time, then the non-convex optimization problem is approximately solved. The approach is validated through simulation compared with regular nonlinear MPC formulation solved by numeric solver.
DOI:	10.1109/YAC57282.2022.10023886