Affiliations: Intelligent Autonomous Systems Laboratory, Department
of Information Engineering (DEI), Faculty of Engineering, University of Padua,
Padova, Italy | Institute of Intelligent Systems for Automation,
National Research Council, Bari, Italy | Department of Adaptive Machine Systems, Osaka
University, Suita, Osaka, 565-0871 Japan | Institute of Biomedical Engineering of the National
Research Council (ISIB-CNR), Padova, Italy
Abstract: In this paper an omnidirectional Distributed Vision System (DVS) is
presented. The presented DVS is able to learn to navigate a mobile robot in its
working environment without any prior knowledge about calibration parameters of
the cameras or the control law of the robot (this is an important feature if we
want to apply this system to existing camera networks). The DVS consists of
different Vision Agents (VAs) implemented by omnidirectional cameras. The main
contribution of the work is the explicit distribution of the acquired knowledge
in the DVS. The aim is to develop a totally autonomous system able not only to
learn control policies by on-line learning, but also to deal with a changing
environment and to improve its performance during lifetime. Once an initial
knowledge is acquired by one Vision Agent, this knowledge can be transferred to
other Vision Agents in order to exploit what was already learned. In this
paper, first we investigate how the Vision Agent learns the knowledge, then we
evaluate its performance and test the knowledge propagation on three different
VAs. Experiments are reported both using a system simulator and using a
prototype of the Distributed Vision System in a real environment demonstrating
the feasibility of the approach.
