Analysis of motion and internal loading of objects grasped by multiple cooperating manipulators
International Journal of Robotics Research
Neural network dynamics for path planning and obstacle avoidance
Neural Networks
Communication in reactive multiagent robotic systems
Autonomous Robots
A genetic algorithm for the generalised assignment problem
Computers and Operations Research
The impact of diversity on performance in multi-robot foraging
Proceedings of the third annual conference on Autonomous Agents
An Agent-Based Approach for Scheduling Multiple Machines
Applied Intelligence
Cooperative Mobile Robotics: Antecedents and Directions
Autonomous Robots
Multiagent Systems: A Survey from a Machine Learning Perspective
Autonomous Robots
Decentralized Fuzzy Control of Multiple Nonholonomic Vehicles
Journal of Intelligent and Robotic Systems
Analyzing the Multiple-target-multiple-agent Scenario Using Optimal Assignment Algorithms
Journal of Intelligent and Robotic Systems
Multi-Agent Based Distributed Control System for an Intelligent Robot
SCC '04 Proceedings of the 2004 IEEE International Conference on Services Computing
Real-time map building and navigation for autonomous robots inunknown environments
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Neural network approaches to dynamic collision-free trajectorygeneration
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Neural Information Processing
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In this paper, a self-organizing map (SOM)-based multi-agent architecture is proposed for multirobot systems. It is capable of controlling a group of mobile robots to complete multiple tasks simultaneously. By cooperative and competitive behaviours, the group of mobile robots can automatically arrange the total task, and dynamically adjust their motion whenever the environment is changed. As an implementation, it can control a group of mobile robots to complete multiple tasks at different locations, such that the desired number of robots will arrive at every target location from arbitrary initial locations. The proposed approach integrates the task requirement of robots and the robot motion planning, such that the robots can start to move before their destinations are finalized. The robot navigation can be dynamically adjusted to guarantee that each target location has the desired number of robots, even under unexpected uncertainties, such as when some robots break down, some robots and/or some tasks are added, or some tasks are changed. Unlike most conventional models that are suitable to static environments only, the proposed approach is capable of dealing with changing environments. In addition, the proposed algorithm can be applied to the path planning of multirobot systems, where a group of robots is coordinated to visit a set of depots. The effectiveness and efficiency of the proposed approach are demonstrated by simulation studies.