Simulation of intelligent robot behavior based on reinforcement learning and neural network approach

Abstract

This paper is concerned with the designing of an intelligent planning system, particularly with the planning of intelligent robot behavior in assembly processes. This 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 assumption is that the robot is a tabula rasa and has no knowledge of the work space structure. Initially, it has just basic strategic knowledge of searching for solutions, based on random attempts, and a built-in learning system. It explores the work space by a simple...