Autonomous Robotic Assembly Using Collaborative Behavior Based Agents

This work studies the design of a robotic assembly system as a multiagent system. Any multidevice system, or any system whose performance is naturally decomposable, can be interpreted as a corporation of agents. Such a scheme comprises the ability of creating a collaborative technical system which can provide the achieving of a social intelligence. Social behavior is the highest form of intelligence that can provide the solving of very complex problems, autonomous creation of new procedures, and efficient adaptation to new tasks. The presented multiagent model is based on processing units, which include the recognition networks, problem solving algorithms, and learning engines. It integrates perception, recognition, learning, and communication capabilities. The reinforcement learning method is used here to evaluate robot behavior and to induce new, or improve existing, knowledge. The acquired action (task) plan is stored as experience which can be used in solving similar future problems. To provide the recognition of problem similarities, the Adaptive Fuzzy Shadowed (AFS) neural network is applied.