安全規則違反を最小化する漸進的サンプリング型動作計画
Incremental Sampling-based Algorithm for Minimum-violation Motion Planning
サンプリングベースの動作計画と重み付きオートマトンを組み合わせ、安全規則の違反を最小にしつつ目標到達を保証する制御則を漸進的に求める手法を提案し、漸近最適性を証明した。
著者: Luis I. Reyes Castro, Pratik Chaudhari, Jana Tumova, Sertac Karaman, Emilio Frazzoli, Daniela Rus
分類: cs.RO
原文アブストラクト
This paper studies the problem of control strategy synthesis for dynamical systems with differential constraints to fulfill a given reachability goal while satisfying a set of safety rules. Particular attention is devoted to goals that become feasible only if a subset of the safety rules are violated. The proposed algorithm computes a control law, that minimizes the level of unsafety while the desired goal is guaranteed to be reached. This problem is motivated by an autonomous car navigating an urban environment while following rules of the road such as "always travel in right lane'' and "do not change lanes frequently''. Ideas behind sampling based motion-planning algorithms, such as Probabilistic Road Maps (PRMs) and Rapidly-exploring Random Trees (RRTs), are employed to incrementally construct a finite concretization of the dynamics as a durational Kripke structure. In conjunction with this, a weighted finite automaton that captures the safety rules is used in order to find an optimal trajectory that minimizes the violation of safety rules. We prove that the proposed algorithm guarantees asymptotic optimality, i.e., almost-sure convergence to optimal solutions. We present results of simulation experiments and an implementation on an autonomous urban mobility-on-demand system.
