Indoor navigation of an inverse pendulum type autonomous mobile robot with adaptive stabilization control system

In the control of autonomous mobile robots there exist two types of control: global control and local control. The requirement to solve global and local tasks arises respectively. This chapter concentrates on local tasks and shows that robots can learn to cope with some local tasks within minutes. The main idea of the chapter is to show that, while creating intelligent control systems for autonomous mobile robots, the beginning is most important as we have to transfer as much as possible human knowledge and human expert-operator skills into the intelligent control system. Successful transfer ensures fast and good results. One of the most advanced techniques in robotics is an autonomous mobile robot on-line learning from the experts’ demonstrations. Further, the latter technique is briefly described in this chapter. As an example of local task the wall following is taken. The main goal of our experiment is to teach the autonomous mobile robot within 10 minutes to follow the wall of the maze as fast and as precisely as it is possible. This task also can be transformed to the obstacle circuit on the left or on the right. The main part of the suggested control system is a small Feed-Forward Artificial Neural Network. In some particular cases – critical situations – “If-Then” rules undertake the control, but our goal is to minimize possibility that these rules would start controlling the robot. The aim of the experiment is to implement the proposed technique on the real robot. This technique enables to reach desirable capabilities in control much faster than they would be reached using Evolutionary or Genetic Algorithms, or trying to create the control systems by hand using “If-Then” rules or Fuzzy Logic. In order to evaluate the quality of the intelligent control system to control an autonomous mobile robot we calculate objective function values and the percentage of the robot work loops when “If-Then” rules control the robot.

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Notice of Retraction: The Research of Intelligent Control System of Autonomous Mobile Robot

2009 International Conference on E-Learning, E-Business, Enterprise Information Systems, and E-Government ◽

10.1109/eeee.2009.30 ◽

2009 ◽

Author(s):

Sun Jie ◽

Zhang Jinqiang

Keyword(s):

Control System ◽

Mobile Robot ◽

Intelligent Control ◽

Autonomous Mobile Robot ◽

Intelligent Control System

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Behavior Based Complete Coverage Task of Unknown Area by an Autonomous Mobile Robot SCORPION with Static Obstacles in Environment

Solid State Phenomena ◽

10.4028/www.scientific.net/ssp.198.73 ◽

2013 ◽

Vol 198 ◽

pp. 73-78

Author(s):

Mariusz Dąbkowski ◽

Paweł Skrzek ◽

Grzegorz Redlarski

Keyword(s):

Control System ◽

Mobile Robot ◽

Behavioral Control ◽

Computer Application ◽

Main Task ◽

Autonomous Mobile Robot ◽

Complete Coverage ◽

Behavior Based ◽

The One ◽

Unknown Area

In the paper the behavior based control system of an autonomous mobile robot SCORPION is presented to execute the one of the most difficult navigation task, which is the complete coverage task of unknown area with static obstacles in the environment. The main principle assumed to design control system was that the robot should cover all area only once, if it possible, to optimize the length of path and energy consumption. All commercial robots like Roomba, Trilobite or IVO move using structured templates combined with random movement. Therefore the path of coverage is not optimal directions of movement are often chosen randomly, so robot covers the same area many times wasting time and energy. In paper the five main developed templates of movement were described to fulfill main task in ordered manner using primarily the way of the ox template of coverage [1, 2, 5, 1. The behavioral control system is implemented in a computer application written in Python [5]. In the paper the test methodology of the developed system on real mobile robot ERSP SCORPION equipped with IR sensors is presented. Graphical and quantitative results of tests of accomplishment of complete coverage task are given for 6 different configurations of obstacles in the robots environment. Conclusions are presented and discussed [5]. Ways to improve the quality indicators [1, of the task of complete coverage of a unknown area are also showed.

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