Goal-oriented Geometric Model Based Intelligent System Architecture for Adaptive Robotic Motion Generation in Dynamic Environment

  • Lee, Dong-Hun (School of Information and Communication Engineering SungKyunKwan University) ;
  • Hwang, Kyung-Hun (School of Information and Communication Engineering SungKyunKwan University) ;
  • Chung, Chae-Wook (Department of Electronic Communication Ansan College of Technology) ;
  • Kuc, Tae-Yong (School of Information and Communication Engineering SungKyunKwan University)
  • Published : 2005.06.02

Abstract

Control architecture of the action based robot engineering can be divided into two types of deliberate type - and reactive type- controller. Typical deliberate type, slow in reaction speed, is well suited for the realization of the higher intelligence with its capability to forecast on the basis of environmental model according to time flow, while reactive type is suitable for the lower intelligence as it fits to the realization of speedy reactive action by inputting the sensor without a complete environmental model. Looking at the environments in the application areas in which robots are actually used, we can see that they have been mostly covered by the uncertain and unknown dynamic changes depending on time and place, the previously known knowledge being existed though. It may cause, therefore, any deterioration of the robot performance as well as further happen such cases as the robots can not carry out their desired performances, when any one of these two types is solely engaged. Accordingly this paper aims at suggesting Goal-oriented Geometric Model(GGM) Based Intelligent System Architecture which leads the actions of the robots to perform their jobs under variously changing environment and applying the suggested system structure to the navigation issues of the robots. When the robots do perform navigation in human life changing in a various manner with time, they can appropriately respond to the changing environment by doing the action with the recognition of the state. Extending this concept to cover the highest hierarchy without sticking only to the actions of the robots can lead us to apply to the algorithm to perform various small jobs required for the carrying-out of a large main job.

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