An Integrated HMM-Based Intelligent Robotic Assembly System

Soft computing has aroused vast interests among the robotics community in which intelligent control for robot is targeted. Examples of soft computing including neural networks, genetic algorithms and fuzzy logic are widely applied in the field of robotic assembly, which is widely recognized as a complicated task in robotics. In order to achieve an intelligent robot, three issues should be considered in conjunction, namely, hardware design and robot model (fundamental), control algorithm (intermediate) and system integration (advanced). Hardware design involves motor, actuator and sensor designs as well as the design of an efficient computational architecture. Algorithmic implementation requires feedback control algorithms such as position, force and hybrid control to assist a robot to accomplish a task as accurate as possible. Finally, system integration, consisting of architecture and modeling, aims at integrating hardware, robot models and control algorithm designs efficiently and effectively to accomplish specific tasks intelligently and successfully. The paper starts with an overview of the intrinsic interrelationships between hardware design and robot modeling, control algorithm, and system integration for building an intelligent robotic system. A brief outline of the hardware design and robot model is given whereas control algorithms and system integration will be focused and discussed in some depth in this paper. It is noted that hardware design and robot model are mostly viewed as the first step of intelligent system. The principal theme of this paper is to introduce the use of hidden Markov models (HMMs) to act as state recognizers, which is the key component of the intelligent robotic assembly system. The state information is eventually sent to the control strategy generator (CSG), another major component of the proposed system, in which soft computing techniques are proposed as decision-makers to determine appropriate motion strategies based on these state information. The paper concludes with the proposal of a hierarchical intelligent framework that integrates the HMM-based state recognizer and control strategy generator. This framework is structured such that it forms a generic robotic system that has the remarkable feature of easily extensible to any other types of robot. Key-Words: Intelligent Robotic System, Force Control, Hidden Markov Models, Soft Computing