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An Improved Self-Organizing Map Method for Task Assignment and Path Planning of Multirobot in Obstacle Environment

机译：障碍环境下多机器人任务分配与路径规划的一种改进的自组织映射方法

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摘要

For an obstacle workspace, an integrated multirobot task assignment and path planning algorithm is proposed by combing the improved self-organizing map(SOM) neural network and the artificial potential filed algorithm. The goal is to ensure that all targets in the environment in the presence of obstacles can be visited by a desired number of robots. The competitive layer neurons converge to the input layer neurons along the direction of resultant force in the artificial potential field to avoid obstacles effectively. The resultant force, instead of the Euclidean distance, is regarded as the winner selection criteria to find a better task executor. A novel constraint function is proposed to restrict the self-organizing properties of the SOM network. It is capable of preventing the problem of misconvergence of the neurons in the competition layer. The simulation studies with different environments and comparison results with the traditional SOM algorithm demonstrate the effectiveness of the proposed approach.
机译：针对障碍物工作空间，结合改进的自组织映射神经网络和人工势场算法，提出了一种集成的多机器人任务分配与路径规划算法。目的是确保所需数量的机器人可以访问存在障碍物的环境中的所有目标。竞争层神经元在人工势场中沿着合力的方向收敛到输入层神经元，从而有效地避开了障碍。合力而不是欧几里得距离被视为选择更好的任务执行者的获胜者选择标准。提出了一种新颖的约束函数来约束SOM网络的自组织特性。它能够防止竞争层中神经元的失会集问题。在不同环境下的仿真研究以及与传统SOM算法的比较结果证明了该方法的有效性。

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