Application of a Mobile Robot for Picking Berries Under Qualitative Uncertainty of Conditions

Abstract Over the last decade, the development of computer technology has progressed significantly, which allowed to automate many production processes through the use of intelligent control systems. The possibility of creating modern control systems that allow solving complex multi-factor tasks in conditions of uncertainty has significantly expanded the scope of application of robotics. The article considers an approach to solving one of the tasks of controlling an intelligent mobile robot for agricultural purposes – selecting the order of application of the robot for picking berries. The procedure for decision-making by an intelligent control system for a mobile robot under the qualitative uncertainty of berry picking conditions has been developed. This procedure can be implemented by developing software. The approach proposed by the authors to solving such problems can be used not only for control the intelligent mobile robot for picking berries, but also for other purposes. The article presents the results of applying the developed method for solving the task of control the agricultural mobile robot for picking strawberry in the climatic conditions of Central Russia.

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Expert Guided Autonomous Mobile Robot Learning

Knowledge-Based Intelligent System Advancements ◽

10.4018/978-1-61692-811-7.ch011 ◽

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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A SoC for Intelligent Control Systems

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.571.514 ◽

2012 ◽

Vol 571 ◽

pp. 514-517 ◽

Cited By ~ 1

Author(s):

Zheng Xing Zheng ◽

Guo Min Tang ◽

Li Min Liu

Keyword(s):

Control System ◽

Embedded System ◽

Control Systems ◽

Intelligent Control ◽

Industrial Automation ◽

Intelligent Controller ◽

Intelligent Control System ◽

Intelligent Control Systems ◽

Software And Hardware

Intelligent control is a new direction of industrial automation. An intelligent control system is composed of algorithm, software and hardware. SoC is one of the best embedded system hardware. SoC may get some new progress for intelligent control. In this paper, intelligent control, SoC and some intelligent controller based on SoC are discussed. The new controller can be the smaller and more reliable.

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Peculiarities of development intelligent control systems for the production of entomophages

Energy and automation ◽

10.31548/energiya2021.03.04 ◽

2021 ◽

pp. 42-50

Author(s):

V. Lysenko ◽

◽

I. Chernova ◽

Keyword(s):

Control System ◽

Knowledge Base ◽

Control Systems ◽

Air Temperature ◽

Intelligent Control ◽

Semantic Network ◽

Research Management ◽

Production Rules ◽

Intelligent Control System ◽

Intelligent Control Systems

The article reveals the features of the development of intelligent control systems for the production of entomophages, the relevance of the selected research area is determined. The aim of the study was to substantiate the feasibility of using intelligent control systems in the production of entomophages. Object of research – management process the cultivation of caterpillars Ephestia kuehniella in a specialized box in the production of entomophage Habrobracon hebetor. Research methods – systems approach, data mining, semantic modeling, situational management. A structural diagram of a hybrid intelligent system for controlling the air temperature of a box for growing entomocultures has been developed; knowledge representation model in relation to the construction of a hybrid intelligent control system in the form of a predicate semantic network. The production rules of the knowledge base of the intelligent control system have been developed, which allow to clarify the process of its creation. At the same time, attention is focused on: the sequence of formation of the training sample, which affects the accuracy of control of the air temperature of the box and, accordingly, the costs of electricity; use of ANFIS-editor MATLAB, which developed a self-learning neural network that automatically created a knowledge base regarding dependency the temperature setting of the meter-controller from the error of air temperature regulation. The expediency of using intelligent control systems in the production of entomophages has been scientifically substantiated. Formalized process of creating a hybrid intelligent box air temperature control system for growing entomocultures in the form of a predicate semantic network using production rules. The abovementioned makes it possible to systematize knowledge in relation to the processes of managing the production of entomophages, improve energy efficiency and the level of automation of the production process, which has a positive effect on both the state of the biological component of the object and the economy of the enterprise. The research results were introduced in the laboratory production of the entomophage Habrobrakon hebetor.

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Design of Sail-Assisted Unmanned Surface Vehicle Intelligent Control System

Mathematical Problems in Engineering ◽

10.1155/2016/2958240 ◽

2016 ◽

Vol 2016 ◽

pp. 1-13 ◽

Cited By ~ 6

Author(s):

Yong Ma ◽

Yujiao Zhao ◽

Jiantao Diao ◽

Langxiong Gan ◽

Huaxiong Bi ◽

...

Keyword(s):

Wind Energy ◽

Control Systems ◽

Intelligent Control ◽

Implementation Process ◽

Vital Role ◽

Design Problems ◽

Unmanned Surface Vehicle ◽

Intelligent Control System ◽

Design And Implementation ◽

Intelligent Control Systems

To achieve the wind sail-assisted function of the unmanned surface vehicle (USV), this work focuses on the design problems of the sail-assisted USV intelligent control systems (SUICS) and illustrates the implementation process of the SUICS. The SUICS consists of the communication system, the sensor system, the PC platform, and the lower machine platform. To make full use of the wind energy, in the SUICS, we propose the sail angle of attack automatic adjustment (Sail_4A) algorithm and present the realization flow for each subsystem of the SUICS. By using the test boat, the design and implementation of the SUICS are fulfilled systematically. Experiments verify the performance and effectiveness of our SUICS. The SUICS enhances the intelligent utility of sustainable wind energy for the sail-assisted USV significantly and plays a vital role in shipping energy-saving emission reduction requirements issued by International Maritime Organization (IMO).

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