An Intelligent Control System for Mobile Robot Navigation Tasks in Surveillance

Abstract

Abstract. In recent years, the autonomous mobile robot has found diverse applications such as home/health care system, surveillance system in civil and military applications and exhibition robot. For surveillance tasks such as moving target pursuit or following and patrol in a region using mobile robot, this paper presents a fuzzy Q-learning, as an intelligent control for cost-based navigation, for autonomous learning of suitable behaviors without the supervision or external human command. The Q-learning is used to select the appropriate rule of interval type-2 fuzzy rule base. The initial testing of the intelligent control is demonstrated by simulation as well as experiment of a simple wall-following based patrolling task of autonomous mobile robot.

Learning fuzzy controllers in mobile robotics with embedded preprocessing

“…The learning of controllers for autonomous robots has been dealt with by using different machine learning techniques. Among the most popular approaches can be found evolutionary algorithms [4,5], neural networks [6] and reinforcement learning [7,8]. Also hibridations of them, like evolutionary neural networks [9], reinforcement learning with evolutionary algorithms [10,11], the widely used genetic fuzzy systems [12,13,14,15,16,17,18], or even more uncommon combinations like ant colony optimization with reinforcement learning [19] or differential evolution [20] or evolutionary group based particle swarm optimization [21] have been successfully applied.…”

“…Also hibridations of them, like evolutionary neural networks [9], reinforcement learning with evolutionary algorithms [10,11], the widely used genetic fuzzy systems [12,13,14,15,16,17,18], or even more uncommon combinations like ant colony optimization with reinforcement learning [19] or differential evolution [20] or evolutionary group based particle swarm optimization [21] have been successfully applied. Furthermore, over the last few years, mobile robotic controllers have been getting some attention as a test case for the automatic design of type-2 fuzzy logic controllers [8,5,20].…”

A Real-Time Hydrodynamic-Based Obstacle Avoidance System for Non-holonomic Mobile Robots with Curvature Constraints

“…The integrated platforms at our disposal are intended for missions such as SAR (search and rescue), autonomous driver-less driving, manufacturing, and surveillance in cluttered environments . Along with an increasingly heavy interaction between human and robots, update-to-date but cost-effective implementation or prototyping of intelligent mobile robot systems to accomplish missions autonomously employing recent advances in localization, mapping, and navigation have been the focus of some endeavors put into the robotic systems, as shown, for instance, in References [39,[41][42][43].…”