scholarly journals Benchmark Framework for Mobile Robots Navigation Algorithms

2014 ◽  
Vol 23 (36) ◽  
pp. 65
Author(s):  
Nelson David Muñoz-Ceballos ◽  
Jaime Alejandro Valencia-Velásquez
Keyword(s):  
Energy Consumption ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Performance Metrics ◽  
Autonomous Mobile Robot ◽  
Educational Tool ◽  
Science Technology ◽  
Energy Consumption Optimization ◽  
Navigation Algorithms ◽  
Robot Systems

<p>Despite the wide variety of studies and research on mobile robot systems, performance metrics are not often examined. This makes difficult to establish an objective comparison of achievements. In this paper, the navigation of an autonomous mobile robot is evaluated. Several metrics are described. These metrics, collectively, provide an indication of navigation quality, useful for comparing and analyzing navigation algorithms of mobile robots. This method is suggested as an educational tool, which allows the student to optimize the algorithms quality, relating to important aspects<br />of science, technology and engineering teaching, as energy consumption, optimization and design.</p>

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.

A Task Based Approach to Mechatronic Systems Education

Volume 3 ◽  
2004 ◽  
Author(s):  
Kevin Firth ◽  
Brian Surgenor ◽  
Peter Wild
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Systems Engineering ◽  
Educational Tool ◽  
Electronic Components ◽  
Mechatronic Systems ◽  
Student Workload ◽  
Hands On ◽  
Hands On Learning ◽  
Elective Course

This paper describes an elective course in mechatronic systems engineering that is project based and team-oriented with hands-on learning. Working in small teams, students add electronic components to a mobile robot base and write the programs required to make the robot perform a series of tasks. Although the application of mobile robots as an educational tool in a mechatronics course is becoming the norm at many universities, the task based organization of the Queen’s mechatronics course is believed to have a number of novel features. The paper will review the pedagogy of the course, including aspects of the student workload, the interplay between teams, and the task based approach to marking and organization of the laboratories.

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.

Development of Autonomous Mobile Robot “MML-05” Based on i-Cart Mini for Tsukuba Challenge 2015

2016 ◽  
Vol 28 (4) ◽  
pp. 461-469 ◽  
Author(s):  
Tomoyoshi Eda ◽  
◽  
Tadahiro Hasegawa ◽  
Shingo Nakamura ◽  
Shin’ichi Yuta
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
The Self ◽  
Autonomous Mobile Robots ◽  
Autonomous Mobile Robot ◽  
Localization Method ◽  
Occupancy Grid ◽  
Grid Map ◽  
Local Grid ◽  
Occupancy Grid Map

[abstFig src='/00280004/04.jpg' width='300' text='Autonomous mobile robots entered in the Tsukuba Challenge 2015' ] This paper describes a self-localization method for autonomous mobile robots entered in the Tsukuba Challenge 2015. One of the important issues in autonomous mobile robots is accurately estimating self-localization. An occupancy grid map, created manually before self-localization has typically been utilized to estimate the self-localization of autonomous mobile robots. However, it is difficult to create an accurate map of complex courses. We created an occupancy grid map combining local grid maps built using a leaser range finder (LRF) and wheel odometry. In addition, the self-localization of a mobile robot was calculated by integrating self-localization estimated by a map and matching it to wheel odometry information. The experimental results in the final run of the Tsukuba Challenge 2015 showed that the mobile robot traveled autonomously until the 600 m point of the course, where the occupancy grid map ended.

scholarly journals Modeling and Analysis of the Obstacle-Avoidance Strategies for a Mobile Robot in a Dynamic Environment

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Rui Wang ◽  
Ming Wang ◽  
Yong Guan ◽  
Xiaojuan Li
Keyword(s):  
Energy Consumption ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Obstacle Avoidance ◽  
Decision Process ◽  
Dynamic Environment ◽  
Formal Models ◽  
Modeling And Analysis ◽  
Avoidance Strategies ◽  
Markov Decision

Obstacle avoidance is a key performance of mobile robots. However, its experimental verification is rather difficult, due to the probabilistic behaviors of both the robots and the obstacles. This paper presents the Markov Decision Process based probabilistic formal models for three obstacle-avoidance strategies of a mobile robot in an uncertain dynamic environment. The models are employed to make analyses in PRISM, and the correctness of the analysis results is verified by MATLAB simulations. Finally, the minimum time and the energy consumption are determined by further analyses in PRISM, which prove to be useful in finding the optimal strategy. The present work provides a foundation for the probabilistic formal verification of more complicated obstacle-avoidance strategies.

Development of tethered autonomous mobile robot systems for field works

2001 ◽  
Vol 15 (4) ◽  
pp. 481-496 ◽  
Author(s):  
Edwardo F. Fukushima ◽  
Noriyuki Kitamura ◽  
Shigeo Hirose
Keyword(s):  
Mobile Robot ◽  
Autonomous Mobile Robot ◽  
Robot Systems ◽  
Field Works

scholarly journals Fuzzy Logic Based Control for Autonomous Mobile Robot Navigation

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Hajer Omrane ◽  
Mohamed Slim Masmoudi ◽  
Mohamed Masmoudi
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Fuzzy Controller ◽  
Mobile Robot Navigation ◽  
Infrared Range ◽  
Indoor Environments ◽  
Autonomous Mobile Robot ◽  
Ir Sensors ◽  
Navigation Algorithms ◽  
Trajectory Tracking Controller

This paper describes the design and the implementation of a trajectory tracking controller using fuzzy logic for mobile robot to navigate in indoor environments. Most of the previous works used two independent controllers for navigation and avoiding obstacles. The main contribution of the paper can be summarized in the fact that we use only one fuzzy controller for navigation and obstacle avoidance. The used mobile robot is equipped with DC motor, nine infrared range (IR) sensors to measure the distance to obstacles, and two optical encoders to provide the actual position and speeds. To evaluate the performances of the intelligent navigation algorithms, different trajectories are used and simulated using MATLAB software and SIMIAM navigation platform. Simulation results show the performances of the intelligent navigation algorithms in terms of simulation times and travelled path.

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