歩行者の未来移動を確率的に予測する群衆内先読みナビゲーション
Anticipatory Navigation in Crowds by Probabilistic Prediction of Pedestrian Future Movements
歩行者の連続時間確率過程を学習して将来位置を予測し、衝突確率制約付きの制御で群衆内を安全にナビゲーションするフレームワークSPANを提案した。
著者: Weiming Zhi, Tin Lai, Lionel Ott, Fabio Ramos
分類: cs.RO, cs.LG
原文アブストラクト
Critical for the coexistence of humans and robots in dynamic environments is the capability for agents to understand each other's actions, and anticipate their movements. This paper presents Stochastic Process Anticipatory Navigation (SPAN), a framework that enables nonholonomic robots to navigate in environments with crowds, while anticipating and accounting for the motion patterns of pedestrians. To this end, we learn a predictive model to predict continuous-time stochastic processes to model future movement of pedestrians. Anticipated pedestrian positions are used to conduct chance constrained collision-checking, and are incorporated into a time-to-collision control problem. An occupancy map is also integrated to allow for probabilistic collision-checking with static obstacles. We demonstrate the capability of SPAN in crowded simulation environments, as well as with a real-world pedestrian dataset.
