Safely: 双対性を利用した制約付きセンシング下での安全な確率的運動計画
Safely: Safe Stochastic Motion Planning Under Constrained Sensing via Duality
センシング制約のある不確実環境で、軌道計画と各時刻に計測すべき障害物の選択を同時に最適化し、衝突確率を閾値以下に保つ運動計画手法を提案した。
著者: Michael Hibbard, Abraham P. Vinod, Jesse Quattrociocchi, Ufuk Topcu
分類: eess.SY, cs.RO, cs.SY
原文アブストラクト
Consider a robot operating in an uncertain environment with stochastic, dynamic obstacles. Despite the clear benefits for trajectory optimization, it is often hard to keep track of each obstacle at every time step due to sensing and hardware limitations. We introduce the Safely motion planner, a receding-horizon control framework, that simultaneously synthesizes both a trajectory for the robot to follow as well as a sensor selection strategy that prescribes trajectory-relevant obstacles to measure at each time step while respecting the sensing constraints of the robot. We perform the motion planning using sequential quadratic programming, and prescribe obstacles to sense based on the duality information associated with the convex subproblems. We guarantee safety by ensuring that the probability of the robot colliding with any of the obstacles is below a prescribed threshold at every time step of the planned robot trajectory. We demonstrate the efficacy of the Safely motion planner through software and hardware experiments.
