検証可能なリアルタイム障害物運動予測による動的衝突回避
Toward Verifiable Real-Time Obstacle Motion Prediction for Dynamic Collision Avoidance
最近の観測のみからLSTMをオンライン学習して障害物の動きを予測し、確率的な速度選択で衝突を回避する手法を提案。統計的モデル検査で予測誤り確率を評価し、衝突確率の上限を証明した。
著者: Vincent Kurtz, Hai Lin
分類: cs.RO
原文アブストラクト
Next generation Unmanned Aerial Vehicles (UAVs) must reliably avoid moving obstacles. Existing dynamic collision avoidance methods are effective where obstacle trajectories are linear or known, but such restrictions are not accurate to many real-world UAV applications. We propose an efficient method of predicting an obstacle's motion based only on recent observations, via online training of an LSTM neural network. Given such predictions, we define a Nonlinear Probabilistic Velocity Obstacle (NPVO), which can be used select a velocity that is collision free with a given probability. We take a step towards formal verification of our approach, using statistical model checking to approximate the probability that our system will mispredict an obstacle's motion. Given such a probability, we prove upper bounds on the probability of collision in multi-agent and reciprocal collision avoidance scenarios. Furthermore, we demonstrate in simulation that our method avoids collisions where state-of-the-art methods fail.
