Abstract
M.Ing.
The potential continuous variations in tasks and relative locations of manufacturing equipment (machine tools, robots, automated guided vehicles etc.) resulting from the need to restructure the production environments, require an intelligent system for optimal performance. The work presented, makes use of a systematic biologically inspired approach, based on attraction and repulsion fields generated by the respective equipment. It incorporates an on-line learning capability adapted to the dynamic environment. The equipment, tasks and decision-making interrelations are described. The potential applications of the system in an uncertain and reconfigurable environment, is explained. Simulated and verified results are presented. Specific cases are included.