Expedited Award for Novel Research: A Neural Network Model for Adaptive Control of Multiple Mobile Robots

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A neural network model for task allocation among multiple mobile robots is proposed. The goal of the system is to allocated tasks adaptively while optimizing an overall performance index and avoiding conflicts among the robots. Robots will be allocated to work locations according to the activation levels of corresponding units in the network. Adaptiveness to environmental or internal system changes will be achieved through a process of constraint propagation and relaxation. It is proposed to resolve potential collisions among the moving robots by analyzing a global path planning problem followed by a local path replanning problem. Structural changes in the connectionist network will provide the information for conflict resolution in replanning to avoid collisions. The usefulness of the framework for controlling mobile robots will be demonstrated by simulation on a TI Explorer Lisp machine, using an object-oriented simulation language being developed in PI's laboratory.