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A Neural Network Approach to Dynamic Task Assignment of Multirobots

机译:神经网络的多机器人动态任务分配方法

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In this paper, a neural network approach to task assignment, based on a self-organizing map (SOM), is proposed for a multirobot system in dynamic environments subject to uncertainties. It is capable of dynamically controlling a group of mobile robots to achieve multiple tasks at different locations, so that the desired number of robots will arrive at every target location from arbitrary initial locations. In the proposed approach, the robot motion planning is integrated with the task assignment, thus the robots start to move once the overall task is given. The robot navigation can be dynamically adjusted to guarantee that each target location has the desired number of robots, even under uncertainties such as when some robots break down. The proposed approach is capable of dealing with changing environments. The effectiveness and efficiency of the proposed approach are demonstrated by simulation studies.
机译:本文针对具有不确定性的动态环境中的多机器人系统,提出了一种基于自组织映射(SOM)的神经网络任务分配方法。它能够动态控制一组移动机器人,以在不同位置完成多项任务,从而使所需数量的机器人将从任意初始位置到达每个目标位置。在提出的方法中,机器人运动计划与任务分配集成在一起,因此一旦给出了总体任务,机器人便开始移动。可以动态调整机器人导航,以确保每个目标位置都具有所需数量的机器人,即使在不确定情况下(例如某些机器人崩溃时)也是如此。所提出的方法能够应对不断变化的环境。仿真研究证明了该方法的有效性和效率。

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