2019/10/01 by David Fridovich-Keil, Vicenc Rubies-Royo, Fridovich-Keil, David +4
Computer Science · Engineering · Mathematics · #Advanced Control Systems Optimization #Computer Science and Game Theory (cs.GT) #FOS: Computer and information sciences #FOS: Electrical engineering #Multiagent Systems (cs.MA) #Numerical methods for differential equations #Reinforcement Learning in Robotics #Robotics (cs.RO) #Systems and Control (eess.SY) #cs.GT #cs.MA #cs.RO #cs.SY #eess.SY #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1910.00681
7 pages, 5 figures, accepted to IEEE International Conference on Robotics and Automation (2020)
openalex publication_date 2019/10/01 · arxiv created 2020/03/19 · arxiv updated 2020/03/20 · openalex created_date 2022/07/24 · openalex updated_date 2026/07/28
Iterative linear-quadratic (ILQ) methods are widely used in the nonlinear optimal control community. Recent work has applied similar methodology in the setting of multiplayer general-sum differential games. Here, ILQ methods are capable of finding local equilibria in interactive motion planning problems in real-time. As in most iterative procedures, however, this approach can be sensitive to initial conditions and hyperparameter choices, which can result in poor computational performance or even unsafe trajectories. In this paper, we focus our attention on a broad class of dynamical systems which are feedback linearizable, and exploit this structure to improve both algorithmic reliability and runtime. We showcase our new algorithm in three distinct traffic scenarios, and observe that in practice our method converges significantly more often and more quickly than was possible without exploiting the feedback linearizable structure.