Dual-sensor fusion for obstacle avoidance in indoor environment

2015 ◽  
Author(s):  
Jiann-Der Lee ◽  
Zih-Yang Dang
Keyword(s):  
Obstacle Avoidance ◽  
Sensor Fusion ◽  
Indoor Environment ◽  
Dual Sensor

Multi-Sensor Fusion Based Effective Obstacle Avoidance and Path-Following Technology

2014 ◽  
Vol 20 (10) ◽  
pp. 1751-1756 ◽  
Author(s):  
Byambaa Dorj ◽  
Doopalam Tuvshinjargal ◽  
KilTo Chong ◽  
Dong Pyo Hong ◽  
Deok Jin Lee
Keyword(s):  
Obstacle Avoidance ◽  
Sensor Fusion ◽  
Path Following

Multi-Sensor Fusion for Robot Positioning Based on Dempster-Shafer Methods

2000 ◽  
Author(s):  
Weidong Qu ◽  
Zhongliang Jing ◽  
Yugeng Xi
Keyword(s):  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Sensor Fusion ◽  
High Performance ◽  
Positioning System ◽  
Laser Rangefinder ◽  
Ultrasonic Sensors ◽  
Robot Positioning ◽  
Video Sensors

Abstract This paper presents a high-performance positioning module for the mobile robot. The mobile robot is equipped with the internal and external sensors. Internal sensors are the acceleration gyroscopes. The positioning system makes use of the features of the external sensors to improve its performance: laser rangefinder ultrasonic sensors and video sensors, which were originally designed for mapping and obstacle avoidance modules, the positioning modules has demonstrated the system to work successfully in complex and uncertainty environments.

Localizing Assets in an Indoor Environment Using Sensor Fusion

2018 ◽  
Author(s):  
Ritwik Murali ◽  
Dhivya Nachimuthu ◽  
Dhansri Varsha SenthilKumar ◽  
Malarvizhi Shanmuga Pandian ◽  
Dhareni Krishnen
Keyword(s):  
Sensor Fusion ◽  
Indoor Environment

scholarly journals Fuzzy Obstacle Avoidance for the Mobile System of Service Robots

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Shih-Pang Tseng ◽  
Che-Wen Chen ◽  
Ta-Wen Kuan ◽  
Yao-Tsung Hsu ◽  
Jhing-Fa Wang
Keyword(s):  
Fuzzy Logic ◽  
Power System ◽  
Obstacle Avoidance ◽  
Indoor Environment ◽  
Service Robots ◽  
Human Tracking ◽  
Mobile System ◽  
Service Environments

This study implements Fuzzy logic-based obstacle avoidance and human tracking on an omnidirectional mobile system for service robots. The mobile system could be separated and combined with the robot which can be controlled remotely and switched to go forward and avoid obstacles in an indoor environment automatically. The system is able to track and go to the user according to the user’s position. The omnidirectional wheel was adapted in the power system to perform translating and spinning movements. The translating movement enables the robot to avoid obstacles faster and flexibly in paths. With the spinning movement, the robot can quickly find the direction of the object. Finally, the experiments show that the proposed system has good performance in service environments.

scholarly journals Sensor Fusion based Obstacle Avoidance for Terrain-Adaptive Mobile Robot

2007 ◽  
Vol 13 (2) ◽  
pp. 93-100 ◽  
Author(s):  
Gyung-Hwan Yuk ◽  
Hyun-Seok Yang ◽  
Noh-Chul Park ◽  
Sang-Won Lee
Keyword(s):  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Sensor Fusion

scholarly journals MOBILE ROBOT LOCALIZATION USING WLAN, ODOMETRY AND GYROSCOPE DATA

2010 ◽  
pp. 22-30
Author(s):  
Julian Lategahn ◽  
Frank Kuenemund ◽  
Christof Roehrig
Keyword(s):  
Kalman Filter ◽  
Mobile Robot ◽  
Sensor Fusion ◽  
Indoor Environment ◽  
Signal Strength ◽  
Office Building ◽  
Robot Localization ◽  
Mobile Robot Localization ◽  
Radio Map ◽  
Made In

In this paper a method for estimation of position and motion of a mobile robot in an indoor environment is introduced. The proposed method uses WLAN signal strength to estimate the global position of a mobile robot in an office building. Thus signal strengths of the received access points are stored in the radio map in calibration phase. In localization phase the stored values are compared with actually measured one’s. Therefore a fingerprinting algorithm, that was introduced before, is used. The improvement of the presented work is the multi sensor fusion using Kalman filter, which enhances the accuracy of fingerprinting algorithms and tracking of the robot. For this reason odometric and gyroscopic sensors of the robot are fused with the estimated position of the fingerprinting algorithm. The paper presents the experimental results of measurements made in an office building.

Sign in / Sign up

Export Citation Format

Share Document