To address the issue of low efficiency in large-scale scenario exploration for Unmanned Aerial Vehicle (UAV) equipped with fixed-tilt angle Light Detection and Ranging (LiDAR), an autonomous exploration motion planning method for UAV with controllable LiDAR tilt angle is proposed. First, the method generates viewpoints containing desired LiDAR tilt angle. Then, it performs path planning using the viewpoints information. In the viewpoint generation stage, firstly, viewpoints positions are generated based on frontier normal vector to reduce computational time. Subsequently, the corresponding LiDAR tilt angle is calculated according to the relative position between the frontier and the viewpoints, so as to increase the number of observable frontier voxels from viewpoints. When getting the viewpoint access sequence in the global path planning stage, an equivalent viewpoints method is employed to coordinate adjustments of the LiDAR tilt angle and the UAV yaw angle, thereby reducing the time cost of UAV movement between viewpoints. Simulation results show that, compared to the Fast UAV Exploration (FUEL) method, the proposed method reduces the average exploration time and path length by approximately 10% in a typical factory scenario. In a multi-story structured mall scenario, it achieves a reduction of over 20% in average exploration time and over 10% in average exploration path length.

Unmanned Aerial Vehicles (UAVs) can achieve tracking of moving targets in obstacle environments through real-time environmental perception and planning. However, for sharp-turning and large-maneuvering target tracking in dense-obstacle environment, there are still some problems, such as response lag, target occlusion and high risk of obstacle collision. To address this issue, a hierarchical trajectory planning method for visibility-enhanced tracking of sharp-turning and large-maneuvering target is proposed. The front-end path planning constructs a heuristic function that integrates the target's historical and real-time trajectories to incorporate the target's real-time motion state and improve the response speed of tracking UAV. The back-end trajectory optimization incorporates visibility constraints of the target and obstacles to achieve stable tracking. At the back-end, a sequence of ellipsoids is used for modeling Field of View (FOV) to improve detection accuracy of occlusion. And the tracking distance is adaptively adjusted according to the seen obstacle density to enhance target visibility. Besides, the angle between the UAV's line of sight and UAV's motion direction is constrained to improve continuous visibility of obstacles for reducing collision risk. Simulation tests show that the proposed method improves the tracking success rate compared with the methods of vis-planner and fast-tracker, and it can achieve a success rate of over 80% for tracking of sharp-turning and large-maneuvering target in dense-obstacle environment.