Globally Guided Deep V-Network-Based Motion Planning Algorithm for Fixed-Wing Unmanned Aerial Vehicles

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 12; p. 3984
• Main Authors: Du, Hang, You, Ming, Zhao, Xinyi
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 19.06.2024; MDPI
• Subjects: Algorithms; deep V-Network; fixed-wing UAVs; globally path-guided; Kinematics; motion planning; Neural networks; Planning; Robotics; Unmanned aerial vehicles; Velocity; deep V-Network; motion planning; globally path-guided; fixed-wing UAVs
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24123984

Fixed-wing UAVs have shown great potential in both military and civilian applications. However, achieving safe and collision-free flight in complex obstacle environments is still a challenging problem. This paper proposed a hierarchical two-layer fixed-wing UAV motion planning algorithm based on a global planner and a local reinforcement learning (RL) planner in the presence of static obstacles and other UAVs. Considering the kinematic constraints, a global planner is designed to provide reference guidance for ego-UAV with respect to static obstacles. On this basis, a local RL planner is designed to accomplish kino-dynamic feasible and collision-free motion planning that incorporates dynamic obstacles within the sensing range. Finally, in the simulation training phase, a multi-stage, multi-scenario training strategy is adopted, and the simulation experimental results show that the performance of the proposed algorithm is significantly better than that of the baseline method.