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http://dx.doi.org/10.7746/jkros.2012.7.3.182

Affordance Perception And Behavior Planning Based on Analytic Hierarchy Process  

Lee, Geun-Ho (School of Information Science, JAIST (Japan Advanced Institute of Science and Technology))
Kwon, Chul-Min (School of Information Science, JAIST (Japan Advanced Institute of Science and Technology))
Ikeda, Akihiro (School of Information Science, JAIST (Japan Advanced Institute of Science and Technology))
Chong, Nak-Young (School of Information Science, JAIST (Japan Advanced Institute of Science and Technology))
Publication Information
The Journal of Korea Robotics Society / v.7, no.3, 2012 , pp. 182-193 More about this Journal
Abstract
This paper presents a new behavior planning scheme for autonomous robots, allowing them to handle various objects used in our daily lives. The key idea underlying the proposed scheme is to use affordance concepts that provide a robot with action possibilities triggered by a relation between the robot and objects around it. Specifically, the robot attempts to find the affordances and to determine the most adequate action among them. Through a series of the perception processes, robot motions can be planned and performed to complete assigned tasks. What is of particular importance from the practical point of view is a decision making capability to determine the best choice by comparing the human's body characteristics and behavioral patterns as criteria with action possibilities as alternatives. For this, the analytic hierarchy process (AHP) technique is employed to systematically evaluate the correlation between the criteria and the alternatives. Moreover, the alternatives arranged in order of priority through the decision making process enable the robot to have redundant solutions for the assigned task, resulting in flexible motion generation. The effectiveness and validity of the proposed scheme are verified by performing extensive simulations using objects of our daily use.
Keywords
autonomous robot; affordance; affordance perception; action possibilities; AHP;
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