自律システムのための勾配不要なマルチドメイン最適化
Gradient-free Multi-domain Optimization for Autonomous Systems
自動運転車のハードウェアとソフトウェアを勾配不要最適化で同時に最適化し、6種の最適化手法を3環境で比較評価した研究。
著者: Hongrui Zheng, Johannes Betz, Rahul Mangharam
分類: cs.RO, cs.SE
原文アブストラクト
Autonomous systems are composed of several subsystems such as mechanical, propulsion, perception, planning and control. These are traditionally designed separately which makes performance optimization of the integrated system a significant challenge. In this paper, we study the problem of using gradient-free optimization methods to jointly optimize the multiple domains of an autonomous system to find the set of optimal architectures for both hardware and software. We specifically perform multi-domain, multi-parameter optimization on an autonomous vehicle to find the best decision-making process, motion planning and control algorithms, and the physical parameters for autonomous racing. We detail the multi-domain optimization scheme, benchmark with different core components, and provide insights for generalization to new autonomous systems. In addition, this paper provides a benchmark of the performances of six different gradient-free optimizers in three different operating environments. Our approach is validated with a case study where we describe the autonomous vehicle system architecture, optimization methods, and finally, provide an argument on gradient-free optimization being a powerful choice to improve the performance of autonomous systems in an integrated manner.
