scholarly journals Obstacle Avoidance for Autonomous Mobile Robots Based on Position Prediction Using Fuzzy Inference

10.5772/24494 ◽  
2011 ◽  
Author(s):  
Takafumi Suzuki ◽  
Masaki Takahashi
Keyword(s):  
Mobile Robots ◽  
Obstacle Avoidance ◽  
Fuzzy Inference ◽  
Autonomous Mobile Robots ◽  
Position Prediction

STEREOVISION-BASED FUZZY OBSTACLE AVOIDANCE METHOD

2011 ◽  
Vol 08 (01) ◽  
pp. 169-183 ◽  
Author(s):  
LAZAROS NALPANTIDIS ◽  
ANTONIOS GASTERATOS
Keyword(s):  
Obstacle Avoidance ◽  
Efficient Solution ◽  
Fuzzy Inference ◽  
Autonomous Mobile Robots ◽  
Fuzzy Decision Making ◽  
Fuzzy Decision ◽  
Inference System ◽  
Depth Maps ◽  
Complex Processes ◽  
Stereo Algorithm

This work presents a stereovision-based obstacle avoidance method for autonomous mobile robots. The decision about the direction on each movement step is based on a fuzzy inference system. The proposed method provides an efficient solution that uses a minimum of sensors and avoids computationally complex processes. The only sensor required is a stereo camera. First, a custom stereo algorithm provides reliable depth maps of the environment in frame rates suitable for a robot to move autonomously. Then, a fuzzy decision making algorithm analyzes the depth maps and deduces the most appropriate direction for the robot to avoid any existing obstacles. The proposed methodology has been tested on a variety of self-captured outdoor images and the results are presented and discussed.

A Real-time Algorithm for Obstacle Avoidance of Autonomous Mobile Robots

Robotica ◽  
1992 ◽  
Vol 10 (3) ◽  
pp. 217-227 ◽  
Author(s):  
Huang Han-Pang ◽  
Lee Pei-Chien
Keyword(s):  
Mobile Robots ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Time Algorithm ◽  
Autonomous Mobile Robots ◽  
Computational Time ◽  
Avoidance Problem

SUMMARYA real-time obstacle avoidance algorithm is proposed for autonomous mobile robots. The algorithm is sensor-based and consists of a H-mode and T-mode. The algorithm can deal with a complicated obstacle environment, such as multiple concave and convex obstacles. It will be shown that the algorithm is more efficient and more robust than other sensor-based algorithms. In addition, the algorithm will guarantee a solution for the obstacle avoidance problem. Since the algorithm only takes up a small computational time, it can be implemented in real time.

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