Paper Title
A Vision-Based Perceptual Learning System For Autonomous Mobile Robot
Abstract
Autonomous robots are intelligent machines capable of performing tasks in the real world without explicit human
control for extended periods of time. A high degree of autonomy is particularly desirable in fields where robots can replace
human workers, such as state-of-the practice video surveillance system and space exploration. However, not having human’s
sophisticated sensing and control system, two broad open problems in autonomous robot systems are the perceptual
discrepancy problem, that is, there is no guarantee that the robot sensing system can recognize or detect objects defined by a
human designer, and the autonomous control problem, that is, how the robots can operate in unstructured environments
without continuous human guidance. As a result, autonomous robot systems should have their own ways to acquire percepts
and control by learning.In this paper, a computer vision system is used for visual percept acquisition and a working memory
toolkit is used for robot autonomous control. Natural images contain statistical regularities which can set objects apart from
each other and from random noise. For an object to be recognized in a given image, it is often necessary to segment the
image into non-overlapping but meaningful regions whose union is the entire image. Therefore, a biologically based percept
acquisition system is developed to build an efficient low-level abstraction of real-world data into percepts. Perception in
animals is strongly related to the type of behavior they perform. To solve how the robots can learn to autonomously control
their behavior based on percepts they’ve acquired, the computer vision system is integrated with a software package called
the Working Memory Toolkit (WMtk) for decision making and learning.
Keywords- Robotics; Perceptual Learning; Minimum Spanning Tree; Wmtk; Autonomous Robot