Soft computing algorithms for intelligent control of a mobile robot for service use

1997 ◽  
Vol 1 (2) ◽  
pp. 88-98 ◽  
Author(s):  
T. Tanaka ◽  
J. Ohwi ◽  
L. V. Litvintseva ◽  
K. Yamafuji ◽  
S. V. Ulyanov
Keyword(s):  
Mobile Robot ◽  
Soft Computing ◽  
Intelligent Control ◽  
Service Use

Soft computing algorithms for intelligent control of a mobile robot for service use

1997 ◽  
Vol 1 (2) ◽  
pp. 99-106 ◽  
Author(s):  
T. Tanaka ◽  
J. Ohwi ◽  
L. V. Litvintseva ◽  
K. Yamafuji ◽  
S. V. Ulyanov
Keyword(s):  
Mobile Robot ◽  
Soft Computing ◽  
Intelligent Control ◽  
Service Use

scholarly journals Intelligent control of mobile robot with redundant manipulator & stereovision: quantum / soft computing toolkit

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Kirill V Koshelev ◽  
Alena V Nikolaeva ◽  
Andrey G Reshetnikov ◽  
Sergey Victorovich Ulyanov
Keyword(s):  
Quantum Computing ◽  
Mobile Robot ◽  
Knowledge Base ◽  
Soft Computing ◽  
Intelligent Control ◽  
Computational Intelligence ◽  
Control Object ◽  
Knowledge Bases ◽  
Self Organization ◽  
Redundant Manipulator

The task of an intelligent control system design applying soft and quantum computational intelligence technologies discussed. An example of a control object as a mobile robot with redundant robotic manipulator and stereovision introduced. Design of robust knowledge bases is performed using a developed computational intelligence – quantum / soft computing toolkit (QC/SCOptKBTM). The knowledge base self-organization process of fuzzy homogeneous regulators through the application of end-to-end IT of quantum computing described. The coordination control between the mobile robot and redundant manipulator with stereovision based on soft computing described. The general design methodology of a generalizing control unit based on the physical laws of quantum computing (quantum information-thermodynamic trade-off of control quality distribution and knowledge base self-organization goal) is considered. The modernization of the pattern recognition system based on stereo vision technology presented. The effectiveness of the proposed methodology is demonstrated in comparison with the structures of control systems based on soft computing for unforeseen control situations with sensor system.

Intelligent Control of a Mobile Robot for Service Use in Office Buildings and Its Soft Computing Algorithms

1996 ◽  
Vol 8 (6) ◽  
pp. 538-554 ◽  
Author(s):  
Takayuki Tanaka ◽  
◽  
Junji Ohwi ◽  
Ludmila V. Litvintseva ◽  
Kazuo Yamafuji ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Soft Computing ◽  
Service Use ◽  
Fuzzy Controller ◽  
Experimental Investigations ◽  
New Approach ◽  
Control Framework ◽  
Fuzzy Neural ◽  
Cognitive Graphics ◽  
Robot Communication

The arrangement principles and design methodology on soft computing for complex control framework of AI control system are introduced. The basis of this methodology is computer simulation of dynamics for mechanical robotic system with the help of qualitative physics and search for possible solutions by genetic algorithms (GA). On fuzzy neural network (FNN) optimal solutions for navigation with avoidance of obstacles and technological operations as opening of door with a manipulator are obtained and knowledge base (KB) for fuzzy controller is formed. Fuzzy qualitative simulation, GA and hierarchical node map (HN), and FNN have demonstrated their effectiveness for path planning of a mobile robot for service use. New approach for direct human-robot communication with natural language and cognitive graphics is introduced. The results of fuzzy robot control simulation, monitoring, and experimental investigations are presented.

Intelligent Control of Mobile Robot when Tracking a Moving Object

2019 ◽  
Vol 51 (11) ◽  
pp. 50-62
Author(s):  
Olga N. Sukhoruchkina ◽  
Nikolay V. Progonnyi
Keyword(s):  
Mobile Robot ◽  
Intelligent Control ◽  
Moving Object

scholarly journals Fuzzy Logic for Intelligent Control System Using Soft Computing Applications

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2617
Author(s):  
Catalin Dumitrescu ◽  
Petrica Ciotirnae ◽  
Constantin Vizitiu
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Soft Computing ◽  
Intelligent Control ◽  
Fuzzy Controller ◽  
Control Technique ◽  
Distributed Intelligence ◽  
High Level ◽  
The Impact ◽  
Fuzzy Subsets

When considering the concept of distributed intelligent control, three types of components can be defined: (i) fuzzy sensors which provide a representation of measurements as fuzzy subsets, (ii) fuzzy actuators which can operate in the real world based on the fuzzy subsets they receive, and, (iii) the fuzzy components of the inference. As a result, these elements generate new fuzzy subsets from the fuzzy elements that were previously used. The purpose of this article is to define the elements of an interoperable technology Fuzzy Applied Cell Control-soft computing language for the development of fuzzy components with distributed intelligence implemented on the DSP target. The cells in the network are configured using the operations of symbolic fusion, symbolic inference and fuzzy–real symbolic transformation, which are based on the concepts of fuzzy meaning and fuzzy description. The two applications presented in the article, Agent-based modeling and fuzzy logic for simulating pedestrian crowds in panic decision-making situations and Fuzzy controller for mobile robot, are both timely. The increasing occurrence of panic moments during mass events prompted the investigation of the impact of panic on crowd dynamics and the simulation of pedestrian flows in panic situations. Based on the research presented in the article, we propose a Fuzzy controller-based system for determining pedestrian flows and calculating the shortest evacuation distance in panic situations. Fuzzy logic, one of the representation techniques in artificial intelligence, is a well-known method in soft computing that allows the treatment of strong constraints caused by the inaccuracy of the data obtained from the robot’s sensors. Based on this motivation, the second application proposed in the article creates an intelligent control technique based on Fuzzy Logic Control (FLC), a feature of intelligent control systems that can be used as an alternative to traditional control techniques for mobile robots. This method allows you to simulate the experience of a human expert. The benefits of using a network of fuzzy components are not limited to those provided distributed systems. Fuzzy cells are simple to configure while also providing high-level functions such as mergers and decision-making processes.

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.

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