空地异构机器人系统协作巡逻路径规划方法
Cooperative patrol path planning method for air-ground heterogeneous robot system
摘要点击 677  全文点击 192  投稿时间:2020-12-23  修订日期:2021-04-06
查看全文  查看/发表评论  下载PDF阅读器
DOI编号  10.7641/CTA.2021.00918
  2022,39(1):48-58
中文关键词  路网约束  空地协作  持续巡逻  蚁群算法  遗传算法
英文关键词  road network constraints  air-ground collaboration  persistent patrol  ant colony algorithm  genetic algorithm
基金项目  国家自然科学基金项目(62073250, 62003249, 61573263, 62173262)资助.
作者单位E-mail
胡子峰 武汉科技大学 892341560@qq.com 
陈洋 武汉科技大学 chenyag@wust.edu.cn 
郑秀娟 武汉科技大学  
吴怀宇 武汉科技大学  
中文摘要
      空地异构机器人系统由空中无人机和地面无人车组成, 当两者协作执行持续巡逻任务时, 使用无人车充当 无人机的地面移动补给站能够解决无人机续航能力不足的问题. 运动受限于路网中的无人车必须在适当地点为无 人机补充能量, 这使得两者的路径高度耦合, 给空地协作路径规划带来了挑战. 针对此问题, 本文通过分析无人机能 量、路网、空地汇合时间、巡逻任务全覆盖等多种约束, 以无人机完成全部巡逻任务的总距离为代价, 建立了空地协 作巡逻路径规划模型. 该模型可推广至多架无人机与多辆无人车协作的情形. 然后, 采用遗传算法与蚁群算法相融 合的方法, 对无人机巡逻路径和无人车能量补给路径进行优化求解. 仿真实验表明, 本文的方法不仅可以得到很好 的路径规划结果, 而且较其他算法具有更优的收敛性和执行速度.
英文摘要
      The air-ground heterogeneous robot system consists of drones and unmanned ground vehicles (UGVs). When they cooperate on continuous patrol, using UGVs as mobile charging station for the drones can solve the problem of insufficient drone battery life. The UGVs which are restricted in the road network replenish energy for drones at appropriate locations, this makes the paths of the two vehicles highly coupled and brings challenges to air-ground cooperative path planning. To solve this problem, this paper analyzes the constraints of UAV energy, road network, air-ground convergence time, full coverage of patrol missions, and establishes an air-ground cooperative patrol path planning model with the total distance of the UAV completing all patrol missions as a cost function. This model can be extended to situations where multiple drones cooperate with multiple UGVs. Then, the combination of genetic algorithm and ant colony algorithm is used to optimize the patrol path of drones and the energy supply path of UGVs simultaneously. Simulation experiments show that the proposed method not only can obtain good path planning results, but also has better convergence and execution speed than other algorithms.