On social laws for artificial agent societies: off-line design
Artificial Intelligence - Special volume on computational research on interaction and agency, part 2
Globally Consistent Range Scan Alignment for Environment Mapping
Autonomous Robots
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A novel solution for maze traversal problems using artificial neural networks
Computers and Electrical Engineering
Virtual assembly with biologically inspired intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Neural network approaches to dynamic collision-free trajectorygeneration
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A neural network approach to complete coverage path planning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An efficient dynamic system for real-time robot-path planning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Real-Time Robot Path Planning via a Distance-Propagating Dynamic System with Obstacle Clearance
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Dynamical neural networks for planning and low-level robot control
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A Framework for Coordinated Control of Multiagent Systems and Its Applications
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Real-time collision-free motion planning of a mobile robot using a Neural Dynamics-based approach
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Real-time robot path planning based on a modified pulse-coupled neural network model
IEEE Transactions on Neural Networks
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Recently, a coordinated hybrid agent (CHA) framework was proposed for the control of multiagent systems (MASs). It was demonstrated that an intelligent planner can be designed for the CHA framework to automatically generate desired actions for multiple robots in an MAS. However, in previous studies, only static obstacles in the workspace were considered. In this paper, a neural-network-based approach is proposed for a multirobot system-with moving obstacles. A biologically inspired neural-network-based intelligent planner is designed for the coordination of MASs. A landscape of the neural activities for all neurons of a CHA agent contains information about the agent's local goal and moving obstacles. The proposed approach is able to plan the paths for multiple robots while avoiding moving obstacles. The proposed approach is simulated using both MATLAB and Vortex. The Vortex module executes control commands from the control system module, and provides the outputs describing the vehicle state and terrain information, which are, in turn, used in the control module to produce the control commands. Simulation results show that the developed intelligent planner of the CHA framework can control a large complex system so that coordination among agents can be achieved.