UAV Path Planning for Reconnaissance and Look-Ahead Coverage Support for Mobile Ground Vehicles

Path planning of unmanned aerial vehicles (UAVs) for reconnaissance and look-ahead coverage support mobile ground (MGVs) is a challenging task due to many unknowns being imposed by the MGVs’ variable velocity profiles, change in heading, structural differences between air environments. Few path techniques have been reported literature multirotor UAVs that autonomously follow MGVs missions. These formulate problem as tracking utilizing gimbal sensors overcome complexities. Despite their lack considering additional objectives such dynamic environments, they retain several drawbacks, including high computational requirements, hardware dependency, low performance when MGV has varying velocities. In this study, novel 3D technique presented, enhanced artificial potential field (ED-APF), where formulated both cover with nongimbal sensors. The proposed adopts vertical sinusoidal UAV adapts relative MGV’s position velocity, guided heading exploration areas routes ahead beyond sensors’ range, thus extending capabilities. amplitude frequency are determined maximize required visual quality terms pixel density quantity pertaining area covered. ED-APF was tested validated against general various simulation scenarios using Robot Operating System (ROS) Gazebo-supported PX4-SITL. It demonstrated superior showed its suitability obstacle-populated