Integrating just-in-time expansion primitives and an adaptive variable-step-size mechanism for feasible path planning of fixed-wing UAVs

Path planning is crucial for autonomous flight of fixed-wing Unmanned Aerial Vehicles (UAVs). However, due to the high-speed flight and complex control of fixed-wing UAVs, ensuring the feasibility and safety of planned paths in complex environments is challenging. This paper proposes a feasible path planning algorithm named Closed-loop Radial Ray A* (CL-RaA*). The core components of the CL-RaA* include an adaptive variable-step-size path search and a just-in-time expansion primitive. The former enables fast path search in complex environments, while the latter ensures the feasibility of the generated paths. By integrating these two components and conducting safety checks on the trajectories to be expanded, the CL-RaA* can rapidly generate safe and feasible paths that satisfy the differential constraints that comprehensively consider the dynamics and control characteristics of six-degree-of-freedom fixed-wing UAVs. The final performance tests and simulation validations demonstrate that the CL-RaA* can generate safe and feasible paths in various environments. Compared to feasible path planning algorithms that use the rapidly-exploring random trees, the CL-RaA* not only ensures deterministic planning results in the same scenarios but also generates smoother feasible paths for fixed-wing UAVs more efficiently. In environments with dense grid obstacles, the feasible paths generated by the CL-RaA* are more conducive to UAV tracking compared to those planned using Dubins curves.