Multilevel multi-agent based team decision fusion for mobile robot behavior control

Multilevel fusion is a key issue for developing the decision-making kernel of multi-agent systems. This article presents a formulation of predictive decision-making algorithm for multilevel multi-agent based team decision-making system with the I/O mode characterizations of features in decision methods. The sequential data fusion is conducted through a dynamic behavior modeling method capable of estimating the observed system parameters from the raw sensory measurements over a period of time. The method is implemented for an autonomous tracking system that consists of a target tracking agent whose inputs are visual and ultrasonic range measurements, and a collision avoidance agent whose inputs are ultrasonic range measurements. The experimental results conducted by a mobile robot and intelligent electrical wheelchair demonstrate the feasibility, accuracy and robustness of the system based on the multisensor fusion method.