engleski

Simulation of robotic learning in assembly process

Intelligent robot behavior in assembly processes is a fundamental issue in the development of intelligent and flexible assembly systems. Intelligent robot behavior comprises the robot's capability to act in unpredictable and chaotic situations, which require not just a change but the innovation of the robot's working actions. Planning of intelligent robot behavior addresses three main issues: finding task solutions in unknown situations, learning from experience and recognizing the similarity of problem paradigms. The paper presents a planning system which integrates the reinforcement learning method and a neural network approach with the aim to ensure autonomous robot behavior in unpredictable working conditions. The intelligent planning system is simulated using object-oriented techniques and verified on planned and random examples, proving the main advantages of the proposed approach: autonomous learning, which is invariant with regard to the order of training samples, and enables single iteration learning progress.

intelligent planning; learning; mobile robots; automatic assembly

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