基于多蚁群协同搜索算法的多AUV路径规划

Path Planning of Multi-AUVs Based on Multi-ant Colony Cooperative Search Algorithm

针对未知环境下多自主水下航行器(AUVs)在不考虑声呐探测距离,优化指标单一情况下的协同搜索问题,综合探测距离对目标发现概率的影响,以及AUV转向和避碰威胁等影响因素,提出一种基于先验信息的多蚁群协同路径规划算法。首先,根据目标分布的先验信息建立基于搜索区域栅格化的目标概率分布图;然后,按照目标概率分布初始化信息素浓度,利用先验信息指导各种群蚂蚁搜索,并根据目标概率大小设计状态转移规则,使得目标发现概率最大化;最后,按照搜索路径解的优劣来更新信息素浓度。仿真验证了文中搜索策略的有效性。

To solve the cooperative search problem of multiple autonomous undersea vehicles(AUVs)in an unknown environment without considering the sonar detection distance and single optimization index,a collaborative path planning algorithm for multi-ant colonies based on prior information is proposed based on the comprehensive effect of detection distance on target discovery probability,AUV turning,and collision avoidance threat.First,based on the prior information of the target distribution,a target probability distribution map based on the grid of the search area is established.Subsequently,the pheromone concentration is initialized based on the probability distribution of the target,and the prior information is used to guide ant searches.State transition rules are designed based on the target probability size to maximize the probability of finding the target.Finally,the pheromone concentration is updated based on the advantages and disadvantages of the search path solution,and the effectiveness of the search strategy is verified by simulation.