Path planning, a pivotal constituent of Unmanned Aerial Vehicle (UAV) systems, has emerged as a prominent domain of inquiry, with research demonstrating its tangible applicability within the rotary-wing UAV domain. In contrast to their rotary-wing counterparts, fixed-wing UAVs offer a broader spectrum of utility owing to their heightened speed, extended range, and augmented payload capacity, facilitating applications spanning reconnaissance, surveillance, target tracking, aviation logistics, and aerial photography, among others. For the coverage problem in these tasks, current research has significantly improved mission completion efficiency. However, for fixed-wing UAVs, the inherent nonlinearity of their dynamic models and the complexity of their control mechanisms make ensuring the feasibility of the planned path a critical challenge.
Recently, an autonomous decision-making UAV team led by Yaoming Zhou from Beihang University, China, has reported a feasible path planning algorithm for fixed-wing UAVs termed Closed-loop Radial Ray A* (CL-RaA*). This work first proposes the Radial Ray A* (RaA*), a fast search algorithm based on an adaptive variable-step-size mechanism that guarantees rapid path searching even in environments with large concave obstacles. Additionally, to account for the dynamics and control characteristics of fixed-wing UAVs, a new expansion method, the Just-in-Time Expansion Primitive (JITEP), is introduced. By integrating JITEP with RaA* and under the constraint of safety checks, the CL-RaA* is further proposed. Finally, following comparative experiments with mainstream algorithms, a simulation platform for fixed-wing UAVs was constructed to validate the CL-RaA* algorithm in complex environments.
The team published their work in Chinese Journal of Aeronautics on May 3, 2025.
"In this work, a fast path search algorithm is first proposed, ensuring deterministic results while efficiently accommodating the UAV's motion characteristics across grid maps of different resolutions. Subsequently, a novel expansion method is introduced, which comprehensively accounts for the dynamics and control characteristics of a six-DOF UAV. By integrating these approaches and incorporating safety checks, we present the CL-RaA*, which is designed to generate safe and feasible paths more efficiently,"said Yaoming Zhou, the corresponding author of the paper, a professor in the School of Aeronautic Science and Engineering at Beihang University (China).
By properly setting the search step size, the RaA* algorithm can effectively balance the path length and run-ning time. Even in environments with large concave obstacles, increasing the step size can accelerate the search. In the eight characteristic environments, when the step size is set to 2, the average path length of RaA* increases by only 0.21% compared to the optimal path, while the search speed improves by a factor of 3.32. In the environment with random grid obstacles, the search speed of RaA* is approximately 5.76 times that of Goal-bias RRT and 28.75 times that of JPS. Compared to RRT-Dubins, RRT-JITEP, and RaA*-Dubins, the CL-RaA* achieves more effective feasible path planning for fixed-wing UAVs. Forty random tests in densely populated obstacle environments show that the CL-RaA* achieves an average trajectory length that is 42.19%, 22.01%, and 3.31% shorter than those of RRT-Dubins, RRT-JITEP, and RaA*-Dubins, respectively, and an average planning time that is reduced by 62.81%, 62.21%, and 4.76%, respectively. Compared to feasible trajectories generated using Dubins curves, those generated by the CL-RaA* algorithm are more conducive to UAV tracking. Test results in two random environments show that when tracking the feasible trajectories planned by CL-RaA* at a desired flight speed of 165 m/s, the maximum position error does not exceed 10 m, with a root-mean-square error of less than 3 m.
The next step is to integrate CL-RaA* with real-time onboard systems and extend it to cooperative path planning for UAV swarms.
Other contributors include Hui Gao, Yuhong Jia, Qingyang Qin and Liwen Xu from the School of Aeronautic Science and Engineering at Beihang University in Beijing, China.
Original Source
Hui GAO, Yuhong JIA, Qingyang QIN, Liwen XU, Yaoming ZHOU. Integrating just-in-time expansion primitives and an adaptive variable-step-size mechanism for feasible path planning of fixed-wing UAVs [J]. Chinese Journal of Aeronautics, 2025, https://doi.org/10.1016/j.cja.2025.103566.
About Chinese Journal of Aeronautics
Chinese Journal of Aeronautics (CJA) is an open access, peer-reviewed international journal covering all aspects of aerospace engineering, monthly published by Elsevier. The Journal reports the scientific and technological achievements and frontiers in aeronautic engineering and astronautic engineering, in both theory and practice. CJA is indexed in SCI (IF = 5.7, Q1), EI, IAA, AJ, CSA, Scopus.
