Zhu Anmin, Yang Simon X
Advanced Robotics and Intelligent Systems (ARIS) Laboratory, School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada.
IEEE Trans Neural Netw. 2006 Sep;17(5):1278-87. doi: 10.1109/TNN.2006.875994.
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.
本文提出了一种基于自组织映射(SOM)的神经网络任务分配方法,用于处于动态环境且存在不确定性的多机器人系统。它能够动态控制一组移动机器人在不同位置完成多项任务,从而使所需数量的机器人从任意初始位置到达每个目标位置。在所提出的方法中,机器人运动规划与任务分配相结合,因此一旦给出总体任务,机器人就开始移动。即使在诸如一些机器人发生故障等不确定性情况下,机器人导航也能动态调整,以确保每个目标位置有所需数量的机器人。所提出的方法能够应对不断变化的环境。仿真研究证明了所提方法的有效性和效率。