Real-time map building for fast mobile robot obstacle avoidance

1991 ◽  
Author(s):  
Johann Borenstein ◽  
Yoram Koren
Keyword(s):  
Mobile Robot ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Map Building ◽  
Time Map

Real-time map building for a mobile robot

1998 ◽  
Author(s):  
Guoyin Zhang ◽  
Rubo Zhang ◽  
Guochang Gu
Keyword(s):  
Mobile Robot ◽  
Real Time ◽  
Map Building ◽  
Time Map

scholarly journals Complete Coverage Path Planning in an Unknown Underwater Environment Based on D-S Data Fusion Real-Time Map Building

2012 ◽  
Vol 8 (10) ◽  
pp. 567959 ◽  
Author(s):  
Mingzhong Yan ◽  
Daqi Zhu ◽  
Simon X. Yang
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Autonomous Underwater Vehicle ◽  
Evidence Theory ◽  
Sensor Data ◽  
Map Building ◽  
Complete Coverage ◽  
Coverage Path Planning ◽  
Underwater Environment ◽  
Time Map

A real-time map-building system is proposed for an autonomous underwater vehicle (AUV) to build a map of an unknown underwater environment. The system, using the AUV's onboard sensor information, includes a neurodynamics model proposed for complete coverage path planning and an evidence theoretic method proposed for map building. The complete coverage of the environment guarantees that the AUV can acquire adequate environment information. The evidence theory is used to handle the noise and uncertainty of the sensor data. The AUV dynamically plans its path with obstacle avoidance through the landscape of neural activity. Concurrently, real-time sensor data are “fused” into a two-dimensional (2D) occupancy grid map of the environment using evidence inference rule based on the Dempster-Shafer theory. Simulation results show a good quality of map-building capabilities and path-planning behaviors of the AUV.

Research on Navigation and Obstacle Avoidance Algorithm for Autonomous Mobile Robot in Narrow Area

2011 ◽  
Vol 464 ◽  
pp. 204-207
Author(s):  
Huan Xun Li ◽  
Jun Jie Shen ◽  
Shuai Guo
Keyword(s):  
Mobile Robot ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Extraction Method ◽  
Field Method ◽  
Autonomous Mobile Robot ◽  
Environment Modeling ◽  
Feature Extraction Method ◽  
Potential Field Method ◽  
Narrow Area

In order to improve the accuracy and security when autonomous mobile robot moves in narrow area, a real-time navigation and obstacle avoidance algorithm is put forward. The feature extraction method is used to search for the path points, and the angle potential field method is used to search for the target angle. Based on the two methods more accurate environment modeling and navigation for mobile robot in narrow area is realized. The algorithm has been used successfully in the household robot, and the experiment results show it’s accurate and real-time.

Development of Autonomous Mobile Robot for Obstacle Avoidance

1993 ◽  
Vol 5 (5) ◽  
pp. 481-486 ◽  
Author(s):  
Masafumi Uchida ◽  
◽  
Syuichi Yokoyama ◽  
Hideto Ide ◽  
Keyword(s):  
Mobile Robot ◽  
Motion Planning ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Potential Method ◽  
Working Environment ◽  
Planning Problem ◽  
Autonomous Mobile Robot ◽  
The Real

The potential method is superior for solving the problem of motion planning; however, it must address the problem of the real-time generation of potential field. Obstacle avoidance is a motion planning problem. In a previous study, we investigated the real-time generation of potential field. Based on parallel processing with element group, we proposed the system by Sensory Point Moving (SPM) method. As a result of computer simulation, it was confirmed that the SPM method is effective for generating an obstacle avoidance path in 2-D and a more complex working environment like a 3-D one. In this paper, we discuss the development of autonomous mobile robot for obstacle avoidance based on the SPM method.

Sign in / Sign up

Export Citation Format

Share Document