基于适应度的多机器人任务分配策略

为提高多机器人任务分配方法的通用性和实用性，提出了一种分布式多机器人协作任务分配策略.根据多机器人任务特点，提出了多机器人任务分配必须满足的4个基本目标，并由目标制定相应的4条任务选择策略.按形式化思想为子任务建立了一个通用模型，模型包含了与任务选择相关的子任务基本属性和运行状态.结合子任务模型和任务选择策略，建立了一个子任务适应度模型，包含静态适应度、进度适应度、资源适应度和外部适应度4个分量，机器人根据适应度大小来选择子任务.仿真实验表明，应用该任务分配策略的协作异构多机器人系统对一类搬运任务具有很好的通用性，当任务发生变化和机器人发生故障时，机器人能够正确、及时地调整子任务，系统具有很好的实时性、灵活性和鲁棒性.系统能够实现机器人到子任务的最优映射，与其他任务分配方法相比明显缩短了任务执行时间.

Fitness-based task allocation strategy for multi-robot system

A distributed task allocation strategy for cooperative multi-robot system was provided to improve the generality and practicability of task allocation method for multiple-robot control.Based on the features of the mission including a couple of tasks,this strategy proposed four basic goals for task allocation and correspondingly built four task selecting policies.This strategy formalized tasks by constructing an abstract task model,which consisted of fundamental properties and running states of a task that had impacts on task selection.Based on the task model and the policies,a mathematically-modeled concept called fitness was proposed,and the fitness model included four sub-models: static fitness,progress fitness,resource fitness and external fitness.The robots selected tasks depending on their fitness.This strategy allows a team of heterogeneous robots to be general-purposed through many different missions and to be robust and flexible facing the situated application domains where unexpected environmental and task changes and modification in the robot team may occur.The strategy reduced the mission execution time by keeping task selecting consistent between different robots.The feasibility of the task allocation strategy was illustrated in the simulation missions of multi-robot team performing hazardous waste cleanup.