In order to land a fixed-wing Unmanned Aerial Vehicle (UAV) on a moving platform, serval problems should be addressed. For example, the landing platform moves rapidly, and the UAV has to maintain high speed to generate sufficient lift while it cannot brake in mid-air at will. A segmented guidance strategy is proposed based on action planning. The autonomous shipborne landing process is divided into two stages: transition guidance and approach guidance. In the transition guidance stage, a finite state machine is utilized to guide the fixed-wing UAV from any initial state to a specific distance behind the moving deck. In the approach guidance stage, the action sampling is used to adjust the trajectory in real-time to guide the UAV to touch the deck accurately. Numerical simulation and FlightGear based semi-physical simulation demonstrate that the proposed strategy can effectively guide the fixed-wing UAV from any initial state to the carrier deck with different moving speeds and turbulence, and the landing error is within 2 m.

Achieving smooth motion for multi-legged robots on complex terrains is a significant focus of research. When encountering high obstacles, robots often need to alter their motion direction to avoid them, increasing redundancy in their motion trajectories. To address this challenge, this paper proposes a method for planning the foot-end trajectory during the swing phase while considering obstacle avoidance without modifying the fuselage trajectory. The method combines the Global Optimal Path Search Tree (GOPST) algorithm and a prior path estimation method utilizing Graph Convolutional Network (GCN). The GOPST explores multiple global paths by conducting local path tree searches in each step, guided by an objective function. To enhance efficiency, redundant search branches with high intensity or high possibility of collision with obstacles or other feet, etc. are eliminated using an event-triggering mechanism based on expert constraints. Another objective function is also formulated to obtain an optimal path that offers a larger safety space and a shorter path length. The optimal path nodes and their environmental features are integrated into a GCN for training. Before the operation of the GOPST, the GCN network provides a preliminary path with fast estimation speed. If the estimated path falls outside the safety margin, the GOPST is reactivated to explore a reliable path. Numerical simulation results validate that the GOPST–GCN approach can rapidly generate a smooth trajectory within the safety space of the foot-end workspace. Furthermore, the search time for finding the optimal path in an untrained environment decreases as the number of tests increases. Experimental verification confirms that robots successfully avoid obstacles by employing foot-end swinging without altering the initial motion direction of the fuselage. The GOPST–GCN algorithm is publicly available at https://github.com/bjmyX/GOPST-GCN-a-foot-end-path-planning-method-.