A Mobile Robot Platform with DSP-based Controller and Omnidirectional Vision System

2005 ◽  
Author(s):  
Jizhong Xiao ◽  
A. Calle ◽  
Jing Ye ◽  
Zhigang Zhu
Keyword(s):  
Mobile Robot ◽  
Vision System ◽  
Omnidirectional Vision ◽  
Robot Platform

Sign Detection Vision Based Mobile Robot Platform

2017 ◽  
Vol 7 (2) ◽  
pp. 524
Author(s):  
Hairol Nizam Mohd Shah ◽  
Mohd Zamzuri Ab Rashid ◽  
Zalina Kamis ◽  
Mohd Shahrieel Mohd Aras ◽  
Nursabillilah Mohd Ali ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Graphical User Interface ◽  
Vision System ◽  
Electrical Power ◽  
Harris Corner ◽  
Sign Detection ◽  
Result Show ◽  
Point Detection ◽  
Comfortable Ride ◽  
Robot Platform

<p>Vision system applied in electrical power generated mobile robot to provide a comfortable ride while providing comfort to tourist to interact with visitors. The camera is placed in front of the mobile robot to snap the images along in pathways. The system can recognized the sign which are right, left and up by using Harris corner algorithms and will be display in Graphical User Interface (GUI). A sign can be determined from the vertex coordinates according to the degree to distinguish the direction of the sign. The system will be tested in term of percentage of success in Harris point detection and availability to detect sign with different range. The result show the even though not all Harris point in an image can be detected but most of the images possible to recognise it sign direction.</p>

Determining the Position of a Mobile Robot Using an Omnidirectional Vision System

2001 ◽  
Vol 34 (9) ◽  
pp. 339-344
Author(s):  
Jun-ichi Takiguchi ◽  
Akito Takeya ◽  
Ken'ichi Nishiguchi ◽  
Hiroshi Yano ◽  
Makoto Iyodam ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Vision System ◽  
Omnidirectional Vision

scholarly journals Mobile Robot Self-Localization Using Omnidirectional Vision with Feature Matching from Real and Virtual Spaces

2021 ◽  
Vol 11 (8) ◽  
pp. 3360
Author(s):  
Huei-Yung Lin ◽  
Chien-Hsing He
Keyword(s):  
Mobile Robot ◽  
Real World ◽  
Feature Matching ◽  
Vision System ◽  
Virtual Space ◽  
Image Feature ◽  
Omnidirectional Vision ◽  
Projection Model ◽  
Localization Technique ◽  
The Real

This paper presents a novel self-localization technique for mobile robots based on image feature matching from omnidirectional vision. The proposed method first constructs a virtual space with synthetic omnidirectional imaging to simulate a mobile robot equipped with an omnidirectional vision system in the real world. In the virtual space, a number of vertical and horizontal lines are generated according to the structure of the environment. They are imaged by the virtual omnidirectional camera using the catadioptric projection model. The omnidirectional images derived from the virtual and real environments are then used to match the synthetic lines and real scene edges. Finally, the pose and trajectory of the mobile robot in the real world are estimated by the efficient perspective-n-point (EPnP) algorithm based on the line feature matching. In our experiments, the effectiveness of the proposed self-localization technique was validated by the navigation of a mobile robot in a real world environment.

scholarly journals Multilayered Fuzzy Behavior Control for an Autonomous Mobile Robot with Multiple Omnidirectional Vision System: MOVIS

2007 ◽  
Vol 11 (1) ◽  
pp. 21-27
Author(s):  
Yoichiro Maeda ◽  
◽  
Wataru Shimizuhira ◽  
Keyword(s):  
Mobile Robot ◽  
Measurement Accuracy ◽  
Vision System ◽  
Dynamic Environment ◽  
Fuzzy Reasoning ◽  
Omnidirectional Vision ◽  
Autonomous Mobile Robot ◽  
Behavior Control ◽  
Soccer Robot ◽  
Absolute Coordinates

We propose a multiple omnidirectional vision system (MOVIS) with three omnidirectional cameras and calculation for measuring an object position and localization in an autonomous mobile robot. In identifying the robot’s location, we improved measurement accuracy by correcting the absolute location based on landmark measurement error in the origin of absolute coordinates. We propose omnidirectional behavior control for collision avoidance and object chasing using fuzzy reasoning in an autonomous mobile robot with MOVIS. We also report experimental results confirming the efficiency of our proposal using a RoboCup soccer robot in a dynamic environment.

scholarly journals Soccer-Assisted Training Robot Based on Image Recognition Omnidirectional Movement

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Bin Tan
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Image Recognition ◽  
Standard Error ◽  
Indoor Environment ◽  
Vision System ◽  
Recognition Task ◽  
Task Completion ◽  
Omnidirectional Vision ◽  
Omnidirectional Mobile Robot

With the continuous emergence and innovation of computer technology, mobile robots are a relatively hot topic in the field of artificial intelligence. It is an important research area of more and more scholars. The core of mobile robots is to be able to realize real-time perception of the surrounding environment and self-positioning and to conduct self-navigation through this information. It is the key to the robot’s autonomous movement and has strategic research significance. Among them, the goal recognition ability of the soccer robot vision system is the basis of robot path planning, motion control, and collaborative task completion. The main recognition task in the vision system is the omnidirectional vision system. Therefore, how to improve the accuracy of target recognition and the light adaptive ability of the robot omnidirectional vision system is the key issue of this paper. Completed the system construction and program debugging of the omnidirectional mobile robot platform, and tested its omnidirectional mobile function, positioning and map construction capabilities in the corridor and indoor environment, global navigation function in the indoor environment, and local obstacle avoidance function. How to use the local visual information of the robot more perfectly to obtain more available information, so that the “eyes” of the robot can be greatly improved by relying on image recognition technology, so that the robot can obtain more accurate environmental information by itself has always been domestic and foreign one of the goals of the joint efforts of scholars. Research shows that the standard error of the experimental group’s shooting and dribbling test scores before and the experimental group’s shooting and dribbling test results after the standard error level is 0.004, which is less than 0.05, which proves the use of soccer-assisted robot-assisted training. On the one hand, we tested the positioning and navigation functions of the omnidirectional mobile robot, and on the other hand, we verified the feasibility of positioning and navigation algorithms and multisensor fusion algorithms.

Autonomous mobile robot platform with multi-variant task-specific end-effector and voice activation

2016 ◽  
Author(s):  
Jonathan Tapia ◽  
Eric Wineman ◽  
Patrick Benavidez ◽  
Aldo Jaimes ◽  
Ethan Cobb ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Autonomous Mobile Robot ◽  
End Effector ◽  
Robot Platform

scholarly journals Practical Model for Energy Consumption Analysis of Omnidirectional Mobile Robot

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1800
Author(s):  
Linfei Hou ◽  
Fengyu Zhou ◽  
Kiwan Kim ◽  
Liang Zhang
Keyword(s):  
Energy Consumption ◽  
Power Consumption ◽  
Mobile Robot ◽  
Load Capacity ◽  
Working Environment ◽  
Energy Consumption Analysis ◽  
Mecanum Wheel ◽  
Save Energy ◽  
Energy Consumption Modeling ◽  
Robot Platform

The four-wheeled Mecanum robot is widely used in various industries due to its maneuverability and strong load capacity, which is suitable for performing precise transportation tasks in a narrow environment. While the Mecanum wheel robot has mobility, it also consumes more energy than ordinary robots. The power consumed by the Mecanum wheel mobile robot varies enormously depending on their operating regimes and environments. Therefore, only knowing the working environment of the robot and the accurate power consumption model can we accurately predict the power consumption of the robot. In order to increase the applicable scenarios of energy consumption modeling for Mecanum wheel robots and improve the accuracy of energy consumption modeling, this paper focuses on various factors that affect the energy consumption of the Mecanum wheel robot, such as motor temperature, terrain, the center of gravity position, etc. The model is derived from the kinematic and kinetic model combined with electrical engineering and energy flow principles. The model has been simulated in MATLAB and experimentally validated with the four-wheeled Mecanum robot platform in our lab. Experimental results show that the accuracy of the model reached 95%. The results of energy consumption modeling can help robots save energy by helping them to perform rational path planning and task planning.

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