A Data-Driven Fuzzy Approach to Robot Navigation Among Moving Obstacles

2000 ◽  
pp. 109-115
Author(s):  
Al-Khatib Mohannad ◽  
Jean J. Saade
Keyword(s):  
Robot Navigation ◽  
Data Driven ◽  
Fuzzy Approach ◽  
Moving Obstacles

Data-driven inference modeling based on an on-line Wang-Mendel fuzzy approach

2021 ◽  
Vol 551 ◽  
pp. 113-127
Author(s):  
Yanwei Zhai ◽  
Zheng Lv ◽  
Jun Zhao ◽  
Wei Wang ◽  
Henry Leung
Keyword(s):  
Data Driven ◽  
Fuzzy Approach ◽  
On Line

Pruned Fast Learning Fuzzy Approach for Data-Driven Traffic Flow Prediction

2016 ◽  
Vol 20 (7) ◽  
pp. 1181-1191 ◽  
Author(s):  
Chengdong Li ◽  
◽  
Yisheng Lv ◽  
Jianqiang Yi ◽  
Guiqing Zhang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Intelligent Transportation Systems ◽  
Fuzzy Rule ◽  
Transportation Systems ◽  
Fuzzy Rules ◽  
Data Driven ◽  
Fuzzy Approach ◽  
Traffic Flow Prediction ◽  
Fast Learning ◽  
Flow Prediction

Traffic flow prediction plays an important role in intelligent transportation systems. With the rapid growth of traffic flow data, fast and accurate traffic flow prediction methods are now required. In this paper, we propose a novel fast learning data-driven fuzzy approach for the traffic flow prediction problem. In the proposed approach, to achieve fast learning, an extreme learning machine is utilized to optimize the consequent parameters of the fuzzy rules. Further, a fuzzy rule pruning strategy that involves measuring the firing levels of the fuzzy rules is presented to obtain reduced fuzzy inference systems. To evaluate the performance of the proposed approach, it was experimentally applied to traffic flow prediction and its results compared with those of widely used methods. The experimental results verify that the proposed approach can achieve satisfactory performance. The comparisons show that the proposed approach can obtain better (sometimes similar) performances, but with a simpler structure, fewer parameters, and much faster learning speed than the other methods.

Velocity Occupancy Space: Robot Navigation and Moving Obstacle Avoidance With Sensor Uncertainty

2009 ◽  
Author(s):  
Rachael Bis ◽  
Huei Peng ◽  
Galip Ulsoy
Keyword(s):  
Robot Navigation ◽  
Search Space ◽  
Sensor Data ◽  
Occupancy Grid ◽  
Moving Obstacles ◽  
Robotic Vehicle ◽  
Stationary Obstacles ◽  
Velocity Obstacle ◽  
Moving Obstacle ◽  
Limited Accuracy

In order to autonomously navigate in an unknown environment, a robotic vehicle must be able to sense obstacles, determine their velocities, and follow a clear path to a goal. However, the perceived location and motion of the obstacles will be uncertain due to the limited accuracy of the robot’s sensors. Thus, it is necessary to develop a system that can avoid moving obstacles using uncertain sensor data. The method proposed here is based on a certainty occupancy grid—which has been used to avoid stationary obstacles in an uncertain environment—in conjunction with the velocity obstacle concept—which allows a robot to avoid well-known moving obstacles. The combination of these two techniques leads to velocity occupancy space: a search space which allows the robot to avoid moving obstacles and navigate efficiently to a goal using uncertain sensor data.

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