Four-Dimensional Trajectory Planning with Cooperative Co-Evolutionary for Multiple UAVs
Mingming Xiao, Chen Hong · Unmanned Systems · 2025
Strategic four-dimensional (4D) trajectory planning with the largest amount of available information for multiple unmanned aerial vehicles (UAVs) is significant for integrating upcoming tremendous UAVs into complicated urban environments. It concerns high operational efficiency, provides a strategic layer of conflict avoidance, and reduces the computational burden during flight. This paper develops a systematic approach based on cooperative co-evolutionary paradigm, namely spatial route and time-slot cooperative co-evolutionary algorithm (STCCE), to solve the trajectory planning problem with multiple UAVs during the pre-flight phase quickly. First, the cooperative co-evolutionary algorithm is based on the multiple populations for multiple UAVs, space-time cooperative and conflict-free which are considered simultaneously. Second, with the considerations of the problem-related knowledge, a heuristic-based route constructor is proposed, and a time-slot allocator is applied to generate optimal arrival time and trace the designed spatial path. Third, to speed up the convergence, especially for large-scale conflict-free 4D trajectories search instance, two cooperation mechanisms are designed to evaluate and learn among multiple populations, respectively. Exhaustive simulations are conducted on both complicated and randomly generated instances with different problem scales. The results show that STCCE is an effective and efficient algorithm with respect to four-dimensional trajectory planning tasks.