ARTIFICIAL INTELLIGENCE AND ADVANCED CONTROL FOR ROBOTICS

TRADITIONAL APPROACHES TO THE FEEDBACK CONTROL OF ROBOTS HAVE DEPENDED EXCLUSIVELY ON OPEN LOOP TRAJECTORY COMMANDS. HIGHER LEVEL DECISION MAKING FUNCTIONS WILL BE NECESSARY REQUIRING THE APPLICATION OF ARTIFICIAL INTELLIGENCE (AI) TECHNIQUES. THE PROPOSED RESEARCH AIMS AT THE INTERFACE BETWEEN ROBOT CONTROL DESIGN BY ANALYTICAL OPTIMIZATION AND AI SO THAT BOTH ARE USED MOST EFFECTIVELY. THE KEY CAE TOOLS TO SPECIFY THE INTERFACE BETWEEN THE TWO DIFFERENT METHODODOLOGIES FOR THEIR EFFECTIVE INTEGRATION WILL BE DESIGNED. THE PROPOSED RESEARCH WILL DEVELOP A NEW METHODOLOGY IN WHICH AI METHODS ARE USED IN PARALLEL WITH OPTIMIZATION ALGORITHMS FOR MACHINE LEARNING, ADAPTIVE CONTROL, AND MACHINE VISION. A SIMPLE ROBOT SIMULATION TEST-BED WILL BE USED TO TEST THE CONCEPTS. PHASE I RESEARCH WILL OUTLINE FUTURE RESEARCH DIRECTIONS AND POTENTIAL BENEFITS.