文献类型：期刊文献

英文题名：Four-Dimensional Trajectory Planning with Cooperative Co-Evolutionary for Multiple UAVs

作者：Xiao, Mingming[1];Hong, Chen[2,3]

通讯作者：Hong, C[1];Hong, C[2]

机构：[1]Beijing Union Univ, Coll Smart City, Beijing 100101, Peoples R China;[2]Beijing Union Univ, Coll Robot, Beijing 100101, Peoples R China;[3]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China

第一机构：北京联合大学

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Robot, Beijing 100101, Peoples R China;[2]corresponding author), Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China.|[11417103]北京联合大学北京市信息服务工程重点实验室;[11417]北京联合大学;[1141739]北京联合大学机器人学院;

年份：2025

外文期刊名：UNMANNED SYSTEMS

收录：EI(收录号：20252618681432);Scopus(收录号：2-s2.0-105009044231);WOS:【ESCI(收录号:WOS:001512815100001)】；

基金：The research described in this paper was supported by the National Key R&D Program of China (Grant No. 2018AAA0100804), the State Key Laboratory of Air Traffic Management System (Grant No. SKLATM202201), the Academic Research Projects of Beijing Union University (Grant No. ZK30202304), and National Natural Science Foundation of China (Grant No. 62272049)

语种：英文

外文关键词：Trajectory planning; UAV; cooperative co-evolutionary; memetic computing

摘要：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.