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.

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.

Person Searching Through an Omnidirectional Camera Using CNN in the Tsukuba Challenge

2018 ◽  
Vol 30 (4) ◽  
pp. 540-551 ◽  
Author(s):  
Shingo Nakamura ◽  
◽  
Tadahiro Hasegawa ◽  
Tsubasa Hiraoka ◽  
Yoshinori Ochiai ◽  
...  
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Convolutional Neural Network ◽  
Autonomous Mobile Robots ◽  
Laser Sensor ◽  
Autonomous Mobile Robot ◽  
Omnidirectional Camera ◽  
Public Road

The Tsukuba Challenge is a competition, in which autonomous mobile robots run on a route set on a public road under a real environment. Their task includes not only simple running but also finding multiple specific persons at the same time. This study proposes a method that would realize person searching. While many person-searching algorithms use a laser sensor and a camera in combination, our method only uses an omnidirectional camera. The search target is detected using a convolutional neural network (CNN) that performs a classification of the search target. Training a CNN requires a great amount of data for which pseudo images created by composition are used. Our method is implemented in an autonomous mobile robot, and its performance has been verified in the Tsukuba Challenge 2017.

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>

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.

scholarly journals Bluetooth Transceivers for Full Duplex Communications in Mobile Robots

2012 ◽  
Author(s):  
Choo S. H. ◽  
Shamsudin H M. Amin ◽  
N. Fisal ◽  
C. F. Yeong ◽  
J. Abu Bakar
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Key Words ◽  
Control Unit ◽  
Full Duplex ◽  
Communication Mode ◽  
And Control

Projek ini mengeksplotasi penggunaan Teknologi Bluetooth dalam robot mudah alih. Robot mudah alih mempunyai kebolehan untuk bergerak secara automasi menggunakan algoritma yang rumit dan canggih. Algoritma disimpan dalam sebuah komputer sebagai tuan dan juga “server”. Segala bacaan penderia daripada robot mudah alih akan dihantar kepada tuan dan diproses. Kemudian, arahan untuk langkah seterusnya akan dihantar dari “server” kepada robot mudah alih dalam mode komunikasi dua hala dan dupleks penuh. Maka, “otak” utama berada di "server" dan bukannya pada robot mudah alih. Kertas ini akan memfokus pada perantaraan muka antara Bluetooth transceiver dan Handy Board MC68HC11 mikro pengawal pada robot mudah alih. Untuk kes biasa, satu penerima dan penghantar diperlukan untuk setiap alat (server dan client) masing-masing, tetapi dengan Teknologi Bluetooth, hanya dua Bluetooth transceiver diperlukan untuk mencapai perhubungan dupleks penuh. Projek ini telah menghasilkan robot mudah alih dengan kebolehan Bluetooth. Robot tersebut boleh dikawal secara “wirelessly” melalui Bluetooth transceiver. Kata kunci: Teknologi Bluetooth; dua hala; duplex penuh; automasi; Handy Board This work explores the implementation of Bluetooth technology in mobile robots. The mobile robot has the capability to move around autonomously using complicated and powerful algorithm. The algorithms are stored in the master as the server. All sensor readings from the mobile robot will be transmitted to the master and processed. Then, command or instruction for further action is transmitted from the server to the mobile robot in a bi-directional full duplex communication mode. Hence, the main “brain” is in the server instead of the mobile robot. This paper will focus on the interfacing between Bluetooth tranceiver and Handy Board MC68HC11 micro-controller of mobile robot. For common case, a receiver and transmitter are needed for each device (robot and control unit), but with Bluetooth technology, only two Bluetooth transceivers are needed to achieve full duplex connection. This project has provided a Bluetooth enabled mobile robot. The mobile robot can be controled wirelessly via Bluetooth transceiver. Key words: Bluetooth Technology; bi-directional; full duplex; autonomously; Handy Board

scholarly journals Navigation Based on Vision and DGPS Information for Mobile Robots

1999 ◽  
Vol 11 (1) ◽  
pp. 45-53 ◽  
Author(s):  
Shinji Kotani ◽  
◽  
Ken’ichi Kaneko ◽  
Tatsuya Shinoda ◽  
Hideo Mori ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Navigation System ◽  
Vision System ◽  
Dead Reckoning ◽  
Initial Position ◽  
Map Matching ◽  
Autonomous Mobile Robot ◽  
Environmental Model ◽  
Position And Orientation

This paper describes a navigation system for an autonomous mobile robot in outdoors. The robot uses vision to detect landmarks and DGPS information to determine its initial position and orientation. The vision system detects landmarks in the environment by referring to an environmental model. As the robot moves, it calculates its position by conventional dead reckoning, and matches landmarks to the environmental model to reduce error in position calculation. The robot's initial position and orientation are calculated from coordinates of the first and second locations acquired by DGPS. Subsequent orientations and positions are derived by map matching. We implemented the system on a mobile robot, Harunobu 6. Experiments in real environments verified the effectiveness of our proposed navigation.

Study on Improved APF Algorithm for Autonomous Mobile Robot

2014 ◽  
Vol 519-520 ◽  
pp. 1337-1341 ◽  
Author(s):  
Xiao Meng Shu ◽  
Da Ming Jiang ◽  
Lian Dai
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Local Minimum ◽  
Minimum Point ◽  
Autonomous Mobile Robot ◽  
Simulation Environment ◽  
Local Minimum Point

In algorithms of obstacle avoidance for autonomous mobile robot, APF algorithm is simple, real-time and smooth, but has some limitations for solving problems. For example, the local minimum point may trap mobile robots before reaching its goal. Even though many improved APF algorithms have been put forward, few articles describe the process in detail to show how these algorithms are applied. Considering above factors, this paper focuses on embodiment of abstract improved theory for APF algorithm by showing some changes with formulas and parameters. The whole work has been done in simulation environment. According to the results this paper draws a conclusion.

Autonomous Mobile Robots and Development of Vision Based Automotive Assistance Systems

2011 ◽  
Vol 121-126 ◽  
pp. 2416-2420 ◽  
Author(s):  
Yan Fen Mao ◽  
Hans Wiedmann ◽  
Ming Chen
Keyword(s):  
Image Processing ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Autonomous Mobile Robots ◽  
Autonomous Mobile Robot ◽  
Automotive Engineering ◽  
Research Platform ◽  
Assistance Systems ◽  
Tongji University

The paper describes an autonomous mobile robot named Robotino®, and how it is used for education of Bachelor-students in the majors AES (Automotive Engineering & Service) as well as in MT (Mechatronics) in CDHAW, Tongji University. A fine positioning project using image processing is introduced, and vision-based functions from Robotino®View are presented. It is sketched out how this system also can be used as a research platform for automotive assistance systems.

scholarly journals Local Path Planning of Mobile Robot Using Critical-PointBug Algorithm Avoiding Static Obstacles

2016 ◽  
Vol 5 (3) ◽  
pp. 182
Author(s):  
SUBIR KUMAR DAS
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Autonomous Mobile Robot ◽  
Limited Information ◽  
Surrounding Environment ◽  
Local Path Planning ◽  
Planning Algorithms ◽  
Local Path ◽  
Complete Path

<p align="left">Path planning is an essential task for the navigation of Autonomous Mobile Robot. This is one of the basic problems in robotics. Path planning algorithms are classified as global or local, depending on the knowledge of surrounding environment. In local path planning, the environment is unknown to the robot, and sensors are used to detect the obstacles and to avoid collision. Bug algorithms are one of the frequently used path planning algorithms where a mobile robot moves to the target by detecting the nearest obstacle and avoiding it with limited information about the environment. This proposed Critical-PointBug algorithm, is a new Bug algorithm for path planning of mobile robots. This algorithm tries to minimize traversal of obstacle border by searching few important points on the boundary of obstacle area as a rotation point to goal and end with a complete path from source to goal.</p>

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