Self-organizing behavior of a multi-robot system by a neural network approach

In this paper, a novel neural network approach to self-organizing behavior of a multi-robot system is proposed, which is capable of controlling a group of mobile robots to achieve multiple tasks at several different locations, such that the desired number of robots will arrive at every target location from any arbitrary initial robot locations. The proposed model is based on a self-organizing map (SOM) neural network. Unlike some conventional approaches to multi-robot path planning for multiple tasks where the task assignment and path planning are handled separately, this model combines the robot task requirement and motion planning together, such that the robots can start to move once the total tasks are set. The robot navigation can be dynamically adjusted to guarantee each target location will have the desired number of robots, even under unexpected uncertainties, such as one robot breaks down. In addition, unlike the conventional models that are suitable for static environment only, the proposed approach is also capable of dealing with changing environment. The effectiveness of the proposed approach is demonstrated by simulation studies.