A hybrid CBR-neural adaptation algorithm for humanoid robot control based on kalman ball tracking
Keywords:
case-based reasoning, behaviour control, neural network, extended Kalman filter, ball tacking, RoboCup soccer, case adaptationAbstract
Controlling autonomous, humanoid robots in a dynamic, continuous, and real-time environment is a complex task, especially behavior control and robot ball tracking problems. This paper presents a hybrid Probabilistic CBR-Neural behavior controller for NAO Humanoid soccer. It extends our previously proposed Case-based behavior controller with neural network and Kalmn filter (EKF) for ball tracking in the 3D RoboCup soccer simulation scenario [11]. This is to solve the adaptation problem in CBR and to let the robot learn the Goal-scoring behavior cases that should be executed in real-time. The learned behavior depends highly on the ball tracking results, which is shown using different experiments presented. Our experiments are conducted on the Goal-Score behavior for adapting actions of an attacker humanoid robot. The integration of neural network module for case adaptation shows a very high performance accuracy that reaches average 92.3% by integrating the ball tracking module with Kalman filter. The algorithm modules, results and future research direction are discussed in this paper.
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