遮蔽領域を考慮したUAV飛行のためのモデル予測パス積分制御
OA-MPPI: Occlusion-Aware Model Predictive Path Integral Control for UAV Flight
オンライン占有マップから3次元の遮蔽境界を抽出し、隠れた移動体が到達しうる領域を予測してMPPIの軌道生成でペナルティを与えることで、遮蔽物から急に現れる障害物を回避するUAV制御手法を提案し、シミュレーションと実機で有効性を示した。
著者: Vittorio Palladino, Teaya Yang, Ruiqi Zhang, Mark W. Mueller
分類: cs.RO
原文アブストラクト
Autonomous UAV flight through cluttered and partially unknown environments requires reasoning not only about observed obstacles but also about occluded regions that the sensor cannot observe. We present OA-MPPI, an obstacle- and occlusion-aware extension of Model Predictive Path Integral (MPPI) control for quadrotor flight that accounts for potential moving agents emerging from these regions into the vehicle's path. At every planning step, we extract a 3D occlusion boundary from the online occupancy map and use it to model the regions that hidden agents could reach over the prediction horizon. We penalize trajectories that enter these expanding regions within MPPI rollouts generated using nonlinear quadrotor dynamics and accounting for individual rotor thrust limits. We validate the proposed approach in simulation and hardware flight experiments, with the complete pipeline running onboard the vehicle in real time. Results show increased clearance from occlusion boundaries compared to baseline MPPI in both settings, as well as avoidance of an agent emerging from occlusion in simulation.