基于 DLBSOM 的水下机器人集群任务分配与路径规划

Task Reassignment and Path Planning for MAUV Based on DLBSOM Algorithm

为保证多自主水下机器人(Multiple Autonomous Underwater Vehicle,MAUV)在多目标冲突条件下执行探测任务,提出一种双层生物自组织映射(Double Layer Bio-inspired Self-Organism Map,DLBSOM)算法完成自适应正向-反向初始任务分配。针对受洋流和单体AUV有限能耗影响的MAUV探测任务容易出现的无效任务分配问题,引入一种带有能量激活函数的任务重分配策略来优化任务。建立任务紧迫性生物启发神经网络(Task Urgency Bio-inspired Neural Network,TUBNN)模型描述受洋流影响的水下环境,引入基于模糊互补判断矩阵的距离和供能强度因子,以说明洋流和任务重分配轨迹距离的相互影响。当AUV进入以目标为中心的预警范围时,通过调整AUV的速度实现对目标的精细探测。结合AUV转弯半径数据和非线性运动学方程,对路径进行平滑处理,使其符合水下机器人的运动学约束。最终通过仿真试验验证该方法的可行性和有效性。

In order to ensure Multiple Autonomous Underwater Vehicle(MAUV)could carry out detection under the multi-targets conflict conditions,a Double Layer Bio-inspired Self-Organism Map(DLBSOM)algorithm is proposed to complete the adaptive forward-reverse initial task assignment.Because of invalid task assignments are prone to occur in the iterative process of detection affected by ocean currents and AUV individual energy consumption,a task reassignment strategy including energy activation function is proposed to optimize task assignment.A Task Urgency Bio-inspired Neural Network(TUBNN)model is built to describe the underwater environment under the influence of ocean currents,in which distance and energy-supply intensity factor based on fuzzy-complementary judgment matrix is introduced to illustrate the mutual influence of ocean currents and trajectory distance of task reassignment.When the sub-individual AUV moves into the warning range centered on the target,the speed of the AUV is adjusted to achieve fine detection.Combining the turning database and nonlinear kinematic equation of the AUV,the path is smoothed to conform to the kinematic constraints of the AUV.The simulation tests are carried out to verify the feasibility and effectiveness of the proposed algorithm.