A Vision-Based Perceptual Learning System for Autonomous Mobile Robot
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 work, 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 nonoverlapping 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. Learning plays a major part in this process. 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. The WMtk was developed by Joshua L. Phillips & David C. Noelle based on a neuron computational model of primate working memory system. The success of the whole system is demonstrated by its application to a navigation task.