We present a waypoint planning algorithm for an unmanned aerial vehicle (UAV) that is teamed with an unmanned ground vehicle (UGV) for the task of search and rescue in a subterranean environment. The UAV and UGV are teamed such that the localization of the UAV is conducted on the UGV via the multi-sensor fusion of a fish-eye camera, 3D LIDAR, ranging radio, and a laser altimeter. Likewise, the trajectory planning of the UAV is conducted on the UGV, which is assumed to have a 3D map of the environment (e.g., from Simultaneous Localization and Mapping). The goal of the planning algorithm is to satisfy the mission's exploration criteria while reducing the localization error of the UAV by evaluating the belief space for potential exploration routes. The presented algorithm is evaluated in a relevant simulation environment where the planning algorithm is shown to be effective at reducing the localization errors of the UAV.
翻译:同样,无人驾驶航空器(无人驾驶航空器)与无人驾驶地面飞行器(UGV)合作,在地下环境中进行搜索和救援任务,无人驾驶航空器(UGV)与无人驾驶地面飞行器(UGV)合在一起,使无人驾驶航空器(UGV)在UGV上进行定位,通过多传感器、三维LIDAR、测距无线电和激光测高仪的组合,在鱼眼照相机、3D LIDAR、测距无线电和激光测高仪的多个传感器中进行定位。同样,无人驾驶航空器的轨迹规划是在UGV上进行,假设该飞行器有环境的3D地图(例如,来自同声定位和绘图),规划算法的目标是满足飞行任务的勘探标准,同时通过评价潜在探索路线的信仰空间,减少无人驾驶飞行器的定位错误。在相关的模拟环境中,对所介绍的算法进行了评价,显示规划算法对减少无人驾驶飞行器的定位误差是有效的。