| 陈志旺* **,岳会安* **,吕昌昊***,林悦* **,彭勇****.结合多目标优化的两步法陆空协同路径规划[J].高技术通讯(中文),2026,36(5):500~513 |
| 结合多目标优化的两步法陆空协同路径规划 |
| Two-step approach for solving air-ground collaborative path planning problem with multi-objective optimization |
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| DOI：10. 3772 / j. issn. 1002 - 0470. 2026. 05. 006 |
| 中文关键词: 协同路径规划； 多目标优化； 蚁群算法； NSGA-II算法； 张角拥挤控制； 优劣解距离法 |
| 英文关键词: collaborative path planning, multi-objective optimization, ant colony algorithm, nondominated sorting genetic algorithm Ⅱ, congestion control, TOPSIS |
| 基金项目: |
| 作者 | 单位 | | 陈志旺* ** | (*燕山大学智能控制系统与智能装备教育部工程研究中心秦皇岛 066004)
(**燕山大学河北省工业计算机控制工程重点实验室秦皇岛 066004)
(***燕山大学河北省电力电子节能与传动控制重点实验室秦皇岛 066004)
(****燕山大学电气工程学院秦皇岛 066004) | | 岳会安* ** | | | 吕昌昊*** | | | 林悦* ** | | | 彭勇**** | |
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| 中文摘要: |
| 无人机运动灵活但续航能力有限，地面车负载能力强但运动受路网约束。将地面车作为无人机的着陆平台可以充分结合各自优点。为了研究此类协同路径问题，本文考虑路网、无人机能量、空地汇合、任务目标等多种约束，以无人机航程、续航时间和任务总时间为多个优化目标，构建了多目标优化为主的陆空协同路径模型。为求解此模型，提出一种由单目标的蚁群算法和多目标NSGA-II(nondominated sorting genetic algorithm II)算法结合的两步法。使用蚁群算法求解地面车路径，多目标优化求解无人机路径，并通过信息素建立联系。为优化种群，将张角拥挤控制策略融入NSGA-Ⅱ中，并设计了一种优劣解距离法TOPSIS(technique for order preference by similarity to an ideal solution)从Pareto解中选择满足偏好的解。实验结果表明，两步算法配合紧密，同等条件下搜索到的无人机路径长度减少了8%。张角策略的加入使Pareto解在反世代距离（inverted-generational distance，IGD）和Spacing指标上分别提升了20%和25%。 |
| 英文摘要: |
| Drones exhibit agility but possess limited endurance, whereas ground vehicles offer high load capacity, albeit constrained by road networks. Employing ground vehicles as landing platforms for drones capitalizes on the strengths of both systems. This study explores collaborative path planning by accounting for constraints such as road networks, drone energy, and mission objectives, thereby developing a multi-objective optimization model for land-air coordination. A two-step solution approach is proposed, integrating a single-objective ant colony algorithm with the multi-objective nondominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ) algorithm. The ant colony algorithm determines ground vehicle paths, while drone paths are optimized through multi-objective criteria, facilitated by pheromone interaction. To enhance population diversity, a congestion control strategy based on open angle(CCSOA) is incorporated into NSGA-Ⅱ, accompanied by a TOPSIS (technique for order preference by similarity to an ideal solution) method to select preferred solutions from the Pareto front. Experimental results demonstrate the cohesive performance of the two-step algorithm, reducing drone path length by 8% under identical conditions. The integration of CCSOA resulted in a 20% and 25% improvement in terms of the inverted-generational distance (IGD) and the Spacing, respectively. |
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