Open Experiment of Autonomous Navigation of Mobile Robots in the City: Tsukuba Challenge 2014 and the Results

2015 ◽  
Vol 27 (4) ◽  
pp. 318-326 ◽  
Author(s):  
Shin'ichi Yuta ◽  
◽  
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Autonomous Navigation ◽  
Mobile Robotics ◽  
Autonomous Mobile Robot ◽  
City Environment ◽  
The City ◽  
Autonomous Mobile Robotics

<div class=""abs_img""> <img src=""[disp_template_path]/JRM/abst-image/00270004/01.jpg"" width=""300"" /> Autonomous mobile robot in RWRC 2014</div> The Tsukuba Challenge, an open experiment for autonomous mobile robotics researchers, lets mobile robots travel in a real – and populated – city environment. Following the challenge in 2013, the mobile robots must navigate autonomously to their destination while, as the task of Tsukuba Challenge 2014, looking for and finding specific persons sitting in the environment. Total 48 teams (54 robots) seeking success in this complex challenge. </span>

Tsukuba Challenge: Open Experiments for Autonomous Navigation of Mobile Robots in the City – Activities and Results of the First and Second Stages –

2018 ◽  
Vol 30 (4) ◽  
pp. 504-512 ◽  
Author(s):  
Shin’ichi Yuta ◽  
◽  
Keyword(s):  
Mobile Robots ◽  
Basic Concept ◽  
Autonomous Navigation ◽  
Mobile Robotics ◽  
The Self ◽  
Second Stage ◽  
History Of ◽  
The Given ◽  
The City ◽  
Autonomous Mobile Robotics

The Tsukuba Challenge is an open experiment for autonomous mobile robotics researchers who want to build small mobile robots capable of autonomously moving through real and populated pedestrian environments. The Tsukuba Challenge started in 2007 and has been run every year since then. Each year, the self-contained mobile robots of participated team are tasked with autonomously navigating more than 1 km of a given pedestrian pathway through the city. As of 2017, the final year of the second stage, a total of over 500 teams have taken a part in this challenge, by trying to develop their own robot hardware and software to complete the given task. In this paper, the basic concept and the history of Tsukuba Challenge are first explained, and then what has and has not achieved is discussed.

Navigation of a wheeled mobile robotic agent using modified grey wolf optimization controller

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chittaranjan Paital ◽  
Saroj Kumar ◽  
Manoj Kumar Muni ◽  
Dayal R. Parhi ◽  
Prasant Ranjan Dhal
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Real Time ◽  
Autonomous Navigation ◽  
Simulation Software ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Content Type ◽  
Cluttered Environment

PurposeSmooth and autonomous navigation of mobile robot in a cluttered environment is the main purpose of proposed technique. That includes localization and path planning of mobile robot. These are important aspects of the mobile robot during autonomous navigation in any workspace. Navigation of mobile robots includes reaching the target from the start point by avoiding obstacles in a static or dynamic environment. Several techniques have already been proposed by the researchers concerning navigational problems of the mobile robot still no one confirms the navigating path is optimal.Design/methodology/approachTherefore, the modified grey wolf optimization (GWO) controller is designed for autonomous navigation, which is one of the intelligent techniques for autonomous navigation of wheeled mobile robot (WMR). GWO is a nature-inspired algorithm, which mainly mimics the social hierarchy and hunting behavior of wolf in nature. It is modified to define the optimal positions and better control over the robot. The motion from the source to target in the highly cluttered environment by negotiating obstacles. The controller is authenticated by the approach of V-REP simulation software platform coupled with real-time experiment in the laboratory by using Khepera-III robot.FindingsDuring experiments, it is observed that the proposed technique is much efficient in motion control and path planning as the robot reaches its target position without any collision during its movement. Further the simulation through V-REP and real-time experimental results are recorded and compared against each corresponding results, and it can be seen that the results have good agreement as the deviation in the results is approximately 5% which is an acceptable range of deviation in motion planning. Both the results such as path length and time taken to reach the target is recorded and shown in respective tables.Originality/valueAfter literature survey, it may be said that most of the approach is implemented on either mathematical convergence or in mobile robot, but real-time experimental authentication is not obtained. With a lack of clear evidence regarding use of MGWO (modified grey wolf optimization) controller for navigation of mobile robots in both the environment, such as in simulation platform and real-time experimental platforms, this work would serve as a guiding link for use of similar approaches in other forms of robots.

Integrating Odor Sensing in Robotics

2018 ◽  
pp. 224-243
Author(s):  
Alauddin Yousif Al-Omary
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Chemical Sensors ◽  
Mobile Robotics ◽  
Future Research ◽  
Research Directions ◽  
The Future ◽  
The Status ◽  
Future Research Directions ◽  
Technical Challenges

In this chapter, the benefit of equipping the robot with odor sensors is investigated. The chapter addresses the types of tasks the mobile robots can accomplish with the help of olfactory sensing capabilities, the technical challenges in mobile robot olfaction, the status of mobile robot olfaction. The chapter also addresses simple and complex electronic olfaction sensors used in mobile robotics, the challenge of using chemical sensors, the use of many types of algorithms for robot olfaction, and the future research directions in the field of mobile robot olfaction.

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.

scholarly journals A Taxonomy for Mobile Robots: Types, Applications, Capabilities, Implementations, Requirements, and Challenges

Robotics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 109
Author(s):  
Uwe Jahn ◽  
Daniel Heß ◽  
Merlin Stampa ◽  
Andreas Sutorma ◽  
Christof Röhrig ◽  
...  
Keyword(s):  
Literature Review ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Systematic Literature Review ◽  
Mobile Robotics ◽  
Limiting Factors ◽  
Reference Systems ◽  
Limited Energy ◽  
Definition Of

Mobile robotics is a widespread field of research, whose differentiation from general robotics is often based only on the ability to move. However, mobile robots need unique capabilities, such as the function of navigation. Also, there are limiting factors, such as the typically limited energy, which must be considered when developing a mobile robot. This article deals with the definition of an archetypal robot, which is represented in the form of a taxonomy. Types and fields of application are defined. A systematic literature review is carried out for the definition of typical capabilities and implementations, where reference systems, textbooks, and literature references are considered.

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.

Localization and Path Planning for an Autonomous Mobile Robot Equipped with Sonar Sensor

2015 ◽  
Vol 772 ◽  
pp. 494-499 ◽  
Author(s):  
Corina Monica Pop ◽  
Gheorghe Leonte Mogan ◽  
Mihail Neagu
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Mobile Robotics ◽  
Planning Process ◽  
Autonomous Mobile Robot ◽  
Sonar Sensors ◽  
Sonar Sensor ◽  
Simulation Results ◽  
Effectiveness And Efficiency ◽  
Global Trajectory

In the field of mobile robotics, the process of robot localization and global trajectory planning in robot operating scenes, that are completely or partially known, represents one of the main issues that are essential for providing the desired robot functionality. This paper introduces the basic elements of path planning for an autonomous mobile robot equipped with sonar sensors, operating in a static environment. The path planning process is initially performed by using a known map. Next, the sonar sensors are used to localize the robot, based on obstacle avoidance techniques. The effectiveness and efficiency of the algorithm proposed in this paper is demonstrated by the simulation results.

Modelling and Analysis of Agent Behaviour

2011 ◽  
pp. 186-212
Author(s):  
Ulrich Nehmzow
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Chaos Theory ◽  
Mobile Robotics ◽  
Distinct Advantage ◽  
Environment Interaction ◽  
Quantitative Measurements

Mobile robotics can be a useful tool for the life scientist in that they combine perception, computation and action, and are therefore comparable to living beings. They have, however, the distinct advantage that their behaviour can be manipulated by changing their programs and/or their hardware. In this chapter, quantitative measurements of mobile robot behaviour and a theory of robot-environment interaction that can easily be applied to the analysis of behaviour of mobile robots and animals is presented. Interestingly such an analysis is based on chaos theory.

Android based autonomous mobile robot

2015 ◽  
Vol 2 (1-2.) ◽  
Author(s):  
Gergely Nagymáté
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Control Unit ◽  
Autonomous Mobile Robot ◽  
Expensive Equipment ◽  
Small Series ◽  
Human Faces

The spreading of mobile robots is getting more significant nowadays. This is due to their ability to perform tasks that are dangerous, uncomfortable or impossible to people. The mobile robot must be endowed with a wide variety of sensors (cameras, microphones, proximity sensors, etc.) and processing units that makes them able to navigate in their environment. This generally carried out with unique, small series produced and thus expensive equipment. This paper describes the concept of a mobile robot with a control unit integrating the processing and the main sensor functionalities into one mass produced device, an Android smartphone. The robot is able to perform tasks such as tracking colored objects or human faces and orient itself. In the meantime, it avoids obstacles and keeps the distance between the target and itself. It is able to verbally communicate wit.

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