INTEGRATING ARTIFICAL INTELLIGENCE AND TRAJECTORY GUIDANCE TO ACHIEVE ROBOTIC TASK PLANNING/CONTROL

LITTLE HAS BEEN DONE TO CONTROL ROBOTS IN AN INTELLIGENT MANNER WHICH TRULY INVOLVES FEEDBACK TRAJECTORY CONTROL LAWS. IT WILL NOT BE POSSIBLE TO ACHIEVE THIS FOR EVERY SUBTASK, BUT WHERE POSSIBLE, DRAMATIC PERFORMANCE IMPROVEMENTS WILL BE ACHIEVED. HEURISTIC RULEBASED APPROACHES WILL BE COHERENTLY INTEGRATED WITH THE OPTIMIZATION BASED TECHNIQUES. THE PROPPOSED RESEARCH WILL ANALYZE A WIDE ENOUGH RANGE OF SUBTASKS, SO THAT A COERENT CONTROL STRAGEGY FOR QUITE COMPLEX TASKS CAN BE SYNTHESIZED. CODE GENERATION TOOLS CURRENTLY NEARING COMPLETION AT INTEGRATED SYSTEM, INC. (ISI) WILL ENABLE THE SPECIFICATION OF THE CURRENT PROCESSES WHICH INTEGRATE TASK PLANNING AND CONTROL TIGHTLY TO BE AUTOMATICALLY GENERATED ND IMPLEMENTED IN PHASE II WITH MULTIPLE PROCESSORS. PHASE I WILL SPECIFY AND SOLVE TO THE EXTENT POSSIBLE SET OF BOTTOM LEVEL SUBTASK VIA MATHEMATICAL MODELING AND OPTIMIZATION WHERE POSSIBLE. THE PERFORMANCE OF THESE CONTROL LAWS WILL BE DEMONSTRATED AND EVALUATED WITH SIMULATION TOOLS CURRENTLY UNDER DEVELOPMENT. THE PROPOSED RESEARCH WILL DEVELOPMENT. THE PROPOSED RESEARCH WILL DEVELOP A FUNDAMENTAL INTEGRATION OF AI METHODS WITH FEEDBACK TRAJECTORY GUIDANCE SUCH THAT MUCH MORE COMPLEX TASKS CAN BE EFFICIENTLY PROGRAMMED AND SOLVED BY THE COMBINED ROBOT PLANNER AND CONTROL SYSTEM.