针对多机器人系统动态任务分配中存在的优化问题,在使用合同网初始任务分配的基础上提出了一种使用帕累托改进的任务二次分配算法.多机器人系统并行执行救火任务时,首先通过初始化任务分配将多机器人划分为若干子群;然后,每个子群承包某一救火任务,子群在执行任务的同时与就近子群进行帕累托改进确定需要迁移的机器人,实现两子群之间帕累托最优;最后,使用后序二叉树遍历对所有子群进行帕累托改进实现全局帕累托最优.理论分析和仿真结果表明,相较于强化学习算法和蚁群算法,所提算法的救火任务时间分别减少26.18％和37.04％;相较于传统合同网方法,所提算法在时间方面能够高效完成救火任务,在系统收益方面也具有明显优势.%In order to solve the optimization problem of dynamic task allocation in multi-robot system,a new quadratic task allocation algorithm based on Pareto improvement was proposed based on the initial task allocation of contract net.When the fire fighting task was performed by the multi-robot system in parallel,firstly,the multiple robots were divided into several sub-group through the initialization of task allocation.Then,a fire fighting task was undertook by a subgroup,and the robots needed to be migrated were determined by the Pareto improvement of the subgroup and its nearest subgroup while the subgroup performing the task to achieve the Pareto optimality between the two subgroups.Finally,the global Pareto optimality was achieved by the Pareto improvement of all subgroups with posterior binary tree traversal.The theoretical analysis and simulation results show that,compared with reinforcement learning algorithm and ant colony algorithm,the fire fighting time of the proposed algorithm is reduced by 26.18％ and 37.04％ respectively.And compared with the traditional contract net method,the proposed algorithm can perform the fire fighting task efficiently in time,and also has the obvious advantage in system revenue.
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