Homogeneous neurolike structures in control systems of intelligent mobile robots

2002 ◽  
Author(s):  
I.A. Kaliaev
Keyword(s):  
Mobile Robots ◽  
Control Systems

scholarly journals Self Control and Server-Supervisory Control for Multiple Mobile Robots and its Applicability to Intelligent DNC System

Robotics ◽  
2013 ◽  
pp. 375-390
Author(s):  
F. Nagata ◽  
T. Yamashiro ◽  
N. Kitahara ◽  
A. Otsuka ◽  
K. Watanabe ◽  
...  
Keyword(s):  
Mobile Robots ◽  
Control Systems ◽  
Supervisory Control ◽  
Low Cost ◽  
Self Control ◽  
Control Mode ◽  
Actual System ◽  
Multiple Mobile Robots ◽  
Collective Behaviors ◽  
Nc Machine

Multiple mobile robots with six PSD (Position Sensitive Detector) sensors are designed for experimentally evaluating the performance of two control systems. They are self-control mode and server-supervisory control mode. The control systems are considered to realize swarm behaviors such as Ligia exotica. This is done by using only information of PSD sensors. Experimental results show basic but important behaviors for multiple mobile robots. They are following, avoidance, and schooling behaviors. The collective behaviors such as following, avoidance, and schooling emerge from the local interactions among the robots and/or between the robots and the environment. The objective of the study is to design an actual system for multiple mobile robots, to systematically simulate the behaviors of various creatures who form groups such as a school of fish or a swarm of insect. Further, the applicability of the server-supervisory control scheme to an intelligent DNC (Direct Numerical Control) system is briefly considered for future development. DNC system is an important peripheral apparatus, which can directly control NC machine tools. However, conventional DNC systems can neither deal with various information transmitted from different kinds of sensors through wireless communication nor output suitable G-codes by analyzing the sensors information in real time. The intelligent DNC system proposed at the end of the chapter aims to realize such a novel and flexible function with low cost.

Expert Guided Autonomous Mobile Robot Learning

2011 ◽  
pp. 216-231
Author(s):  
Gintautas Narvydas ◽  
Vidas Raudonis ◽  
Rimvydas Simutis
Keyword(s):  
Control System ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Control Systems ◽  
Intelligent Control ◽  
Main Idea ◽  
Autonomous Mobile Robots ◽  
Autonomous Mobile Robot ◽  
Intelligent Control System ◽  
Objective Function Values

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.

Application of Mobile Robots to Linear Control Theory Education

2020 ◽  
Vol 1 (3) ◽  
Author(s):  
Satoko Yamakawa
Keyword(s):  
Control Theory ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Control Systems ◽  
Linearization Method ◽  
Linear Control ◽  
Linear Control Systems ◽  
Control Engineering ◽  
Linear Control Theory ◽  
Dc Motors

Abstract The knowledge of control engineering for mechanical engineers seems to become more important with the continuous development of automated technologies. To cultivate this knowledge, many experimental devices have been proposed and used. Devices with direct current (DC) motors are widely used because the DC motors can be controlled with sufficient accuracy based on the classical linear control theory. Mobile robots are used as educational platforms attracting the attention of students in various problem-based learning subjects. However, they have been hardly used to teach linear control theory because of the nonlinearity. This paper shows an experimental curriculum to learn control theory using a mobile robot instead of a motor. Although the model of the mobile robot is nonlinear, a strict linearization method makes it possible to adjust the control gains using the linear control theory. By applying the method, the characteristics of linear control systems are explicitly observed in the traveling paths of the mobile robot, so an experimental curriculum to learn the basic linear control theory can be realized using an inexpensive mobile robot. The proposed experimental curriculum was carried out in a class of a mechanical engineering course, and its results are discussed in this paper.

Hierarchical Control Approach for Autonomous Mobile Robots

1970 ◽  
Vol 110 (4) ◽  
pp. 101-104 ◽  
Author(s):  
T. Proscevicius ◽  
A. Bukis ◽  
V. Raudonis ◽  
M. Eidukeviciute
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Artificial Neural Networks ◽  
Mobile Robots ◽  
Control Systems ◽  
Hierarchical Control ◽  
Autonomous Mobile Robots ◽  
Hierarchical Systems ◽  
Control Approach ◽  
Specific Management

Methods for intelligent mobile robots control which are based on principles of hierarchical control systems will be reviewed in this article. Hierarchical intelligent mobile robots are new direction for development of robotics, which have wide application perspectives. Despite increasing progress in technologies, the main problem of autonomous mobile robots development is that, they are ineffective in their control. In each of the hierarchical control levels (movement in space, problems solving and signal processing sets) will define by specific management of objectives, goals and rules. Communication and management between hierarchies are implemented by higher level of hierarchy using obtained information about the environment and lover level of hierarchy. Studies have shown that artificial neural networks, fuzzy logic are widely used for the development of the hierarchical systems. The main focus of the work is on communications in hierarchy levels, since the robot must be controlled in real time. Ill. 4, bibl. 13 (in English; abstracts in English and Lithuanian).http://dx.doi.org/10.5755/j01.eee.110.4.298

scholarly journals DESIGN OF THE CONTROL SYSTEM BY MOVEMENT OF ROBOTS MOBILE GROUP IN CONDITIONS OF UNCERTAINTY

2018 ◽  
Vol 22 (4) ◽  
pp. 112-122
Author(s):  
A. R. Gaiduk ◽  
I. A. Kalyaev ◽  
S. G. Kapustyan ◽  
I. O. Shapovalov
Keyword(s):  
Adaptive Control ◽  
Control System ◽  
Mathematical Models ◽  
Mobile Robots ◽  
Control Systems ◽  
Transfer Functions ◽  
Digital Processing ◽  
Suggested Method ◽  
Analytical Design ◽  
Adaptive Control Systems

Many controlled plants, in particular mobile robots, solve various tasks in a priori uncertain conditions. In this connection their mathematical models necessary for creation of qualitative control systems are unknown. Therefore development of design methods of adaptive control systems is actuality. The big uncertainty of this control problem makes application of adaptive systems with identification by the most expedient. In article the new analytical design method of adaptive control systems by movement of mobile robots group in the uncertainty conditions is offered. This method is focused on the decision of a task of identification of the current mathematical models of robots with the subsequent design of a control system by movement of each robot. The suggested method can be realized automatically as required. It is developed on a basis of the markov method of identification, the method of analytical design of systems with control on output and impacts, and also the standard normalized transfer functions are used. As a whole this method allows to design of the adaptive control systems with desirable qualitative properties. Trial step functions of the small intensity and the original method of digital processing of the information are used at identification. Property of system invariancy of the markov parameters and their direct connection with factors of the discrete dynamic systems transfer functions are a basis of the method of digital processing of the information. It is supposed, that the mobile robots are full or can be stabilized at all possible values of their order and parameters. The suggested method can be used for creation of control systems by the various technical plants functioning in conditions of uncertainty.

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