離散バリア状態を用いた安全埋め込み型微分動的計画法
Safety Embedded Differential Dynamic Programming Using Discrete Barrier States
連続時間系向けのバリア状態を離散時間系に拡張し、安全性をモデルに埋め込んだDDPで安全な最適軌道計画を実現した。車輪ロボットのナビゲーションやクアッドロータの追従タスクで有効性を示した。
著者: Hassan Almubarak, Kyle Stachowicz, Nader Sadegh, Evangelos A. Theodorou
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Certified safe control is a growing challenge in robotics, especially when performance and safety objectives must be concurrently achieved. In this work, we extend the barrier state (BaS) concept, recently proposed for safe stabilization of continuous time systems, to safety embedded trajectory optimization for discrete time systems using discrete barrier states (DBaS). The constructed DBaS is embedded into the discrete model of the safety-critical system integrating safety objectives into the system's dynamics and performance objectives. Thereby, the control policy is directly supplied by safety-critical information through the barrier state. This allows us to employ the DBaS with differential dynamic programming (DDP) to plan and execute safe optimal trajectories. The proposed algorithm is leveraged on various safety-critical control and planning problems including a differential wheeled robot safe navigation in randomized and complex environments and on a quadrotor to safely perform reaching and tracking tasks. The DBaS-based DDP (DBaS-DDP) is shown to consistently outperform penalty methods commonly used to approximate constrained DDP problems as well as CBF-based safety filters.
