发表机构
University of California, Berkeley（加州大学伯克利分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出OA-MPPI，一种遮挡感知的模型预测路径积分控制方法，通过提取三维遮挡边界并惩罚进入潜在隐藏代理可达区域的轨迹，提升无人机在杂乱环境中的飞行安全性，并在仿真和硬件实验中验证了有效性。
AI 中文摘要
自主无人机在杂乱且部分未知的环境中飞行，不仅需要考虑观测到的障碍物，还需要考虑传感器无法观测的遮挡区域。我们提出了OA-MPPI，这是模型预测路径积分（MPPI）控制的一种障碍物和遮挡感知扩展，用于四旋翼飞行，它考虑了可能从这些区域进入飞行路径的潜在移动代理。在每个规划步骤中，我们从在线占据地图中提取三维遮挡边界，并用它来建模隐藏代理在预测时域内可能到达的区域。我们在使用非线性四旋翼动力学并考虑单个旋翼推力限制生成的MPPI滚动中，对进入这些扩展区域的轨迹进行惩罚。我们在仿真和硬件飞行实验中验证了所提出的方法，完整流程在车辆上实时运行。结果表明，在两种设置下，与基线MPPI相比，与遮挡边界的间距增大，并且在仿真中成功避开了从遮挡中出现的代理。
英文摘要
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.
Comments8 pages, 6 figures
