Tracking Control of Mobile Robots Based on the BP Neural Network

2012 ◽  
Vol 522 ◽  
pp. 618-622
Author(s):  
Ying Xiong ◽  
Shi De Xiao ◽  
Shuang Jiang Lei ◽  
Feng Zha
Keyword(s):  
Neural Network ◽  
Control System ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Bp Neural Network ◽  
Tracking Control ◽  
High Reliability ◽  
Control Unit ◽  
Micro Control Unit ◽  
Guide Lines

An intelligent tracking control system based on the micro-control unit (MCU) has been developed to control the motors by sensing the change of black guide lines. After the training of the BP Neural Network, the MCU is able to make decisions quickly and accurately for various situations during robot moving. Using MCU technology to control the motors, the system is compatible for both manual and automatic control. The experiment shows that the mobile robot could follow the change of black guide lines accurately and quickly, and stillness and out-of-orbit were effectively inhibited during moving. The proposed tracking control system based on the BP Neural Network has been verified to have high reliability.

Ultrasonic sensor system for the uses in intelligent motion control system of a mobile robots group

2010 ◽  
Vol 7 ◽  
pp. 109-117
Author(s):  
O.V. Darintsev ◽  
A.B. Migranov ◽  
B.S. Yudintsev
Keyword(s):  
Control System ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Motion Control ◽  
High Speed ◽  
Ultrasonic Sensor ◽  
Sensor System ◽  
Motion Control System ◽  
Speed Sensor ◽  
Working Space

The article deals with the development of a high-speed sensor system for a mobile robot, used in conjunction with an intelligent method of planning trajectories in conditions of high dynamism of the working space.

scholarly journals Energy Modeling and Power Measurement for Mobile Robots

Energies ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 27 ◽  
Author(s):  
Linfei Hou ◽  
Liang Zhang ◽  
Jongwon Kim
Keyword(s):  
Control System ◽  
Energy Consumption ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Electrical Power ◽  
Energy Model ◽  
Energy Modeling ◽  
Power Measurement ◽  
Sensor System ◽  
System Control

To improve the energy efficiency of a mobile robot, a novel energy modeling method for mobile robots is proposed in this paper. The robot can calculate and predict energy consumption through the energy model, which provides a guide to facilitate energy-efficient strategies. The energy consumption of the mobile robot is first modeled by considering three major factors: the sensor system, control system, and motion system. The relationship between the three systems is elaborated by formulas. Then, the model is utilized and experimentally tested in a four-wheeled Mecanum mobile robot. Furthermore, the power measurement methods are discussed. The energy consumption of the sensor system and control system was at the milliwatt level, and a Monsoon power monitor was used to accurately measure the electrical power of the systems. The experimental results showed that the proposed energy model can be used to predict the energy consumption of the robot movement processes in addition to being able to efficiently support the analysis of the energy consumption characteristics of mobile robots.

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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