An Approach in Designing Hierarchy of Fuzzy Behaviors for Mobile Robot Navigation

2007 ◽  
Vol 11 (3) ◽  
pp. 268-275
Author(s):  
Long Thanh Ngo ◽  
◽  
Long The Pham ◽  
Phuong Hoang Nguyen ◽  
Kaoru Hirota ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Fuzzy Controller ◽  
Robot Navigation ◽  
Individual Behavior ◽  
Mobile Robot Navigation ◽  
Simulation Results

We propose an approach to the design hierarchical behaviors for mobile robot navigation in which robot perceives information about the environment from sensor then computes fuzzy output for individual behavior. Each behavior involves a fuzzy controller with the same output. The behavior hierarchy combines commands from fuzzy behavior output and defuzzifies it to archive crisp values for controlling the direction in which robot moves. Simulation results and statistics demonstrate the feasibility of our proposal.

Designing the Fuzzy Controller in Mobile Robot Navigation with the Presence of Unknown Obstacles

2012 ◽  
Vol 3 (1) ◽  
pp. 45-62
Author(s):  
Maryam Refaee ◽  
Mohammad Hossein Kazemi ◽  
MohammadAli Nekoui ◽  
S. Amir Ghoreishi
Keyword(s):  
Mobile Robot ◽  
Fuzzy Controller ◽  
Robot Navigation ◽  
Mobile Robot Navigation

Behavior-based neuro-fuzzy controller for mobile robot navigation

2003 ◽  
Vol 52 (4) ◽  
pp. 1335-1340 ◽  
Author(s):  
P. Rusu ◽  
E.M. Petriu ◽  
T.E. Whalen ◽  
A. Cornell ◽  
H.J.W. Spoelder
Keyword(s):  
Mobile Robot ◽  
Fuzzy Controller ◽  
Robot Navigation ◽  
Mobile Robot Navigation ◽  
Neuro Fuzzy ◽  
Behavior Based

Virtual Reality based Mobile Robot Navigation in Greenhouse Environment

2017 ◽  
Vol 8 (2) ◽  
pp. 854-859
Author(s):  
M. Saiful Azimi ◽  
Z. A. Shukri ◽  
M. Zaharuddin
Keyword(s):  
Virtual Reality ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Virtual Environment ◽  
Robot Navigation ◽  
Tracking Error ◽  
Mobile Robot Navigation ◽  
Probabilistic Roadmap ◽  
Time Distance ◽  
Simulation Results

The difficulties of transporting heavy mobile robots limit robotic experiments in agriculture. Virtual reality however, offers an alternative to conduct experiments in agriculture. This paper presents an application of virtual reality in a robot navigational experiment using SolidWorks and simulated into MATLAB. Trajectories were initiated using Probabilistic Roadmap and compared based on travel time, distance and tracking error, and the efficiency was calculated. The simulation results showed that the proposed method was able to conduct the navigational experiment inside the virtual environment. U-turn trajectory was chosen as the best trajectory for crop inspection with 82.7% efficiency.

scholarly journals Using Ultrasonic Sensors and a Knowledge-Based Neural Fuzzy Controller for Mobile Robot Navigation Control

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 466
Author(s):  
Cheng-Hung Chen ◽  
Cheng-Jian Lin ◽  
Shiou-Yun Jeng ◽  
Hsueh-Yi Lin ◽  
Cheng-Yi Yu
Keyword(s):  
Mobile Robot ◽  
Differential Evolution ◽  
Obstacle Avoidance ◽  
Fuzzy Controller ◽  
Robot Navigation ◽  
Mobile Robot Navigation ◽  
Knowledge Based ◽  
Automatic Navigation ◽  
Navigation Control ◽  
Neural Fuzzy

This study proposes a knowledge-based neural fuzzy controller (KNFC) for mobile robot navigation control. An effective knowledge-based cultural multi-strategy differential evolution (KCMDE) is used for adjusting the parameters of KNFC. The KNFC is applied in PIONEER 3-DX mobile robots to achieve automatic navigation and obstacle avoidance capabilities. A novel escape approach is proposed to enable robots to autonomously avoid special environments. The angle between the obstacle and robot is used and two thresholds are set to determine whether the robot entries into the special landmarks and to modify the robot behavior for avoiding dead ends. The experimental results show that the proposed KNFC based on the KCMDE algorithm has improved the learning ability and system performance by 15.59% and 79.01%, respectively, compared with the various differential evolution (DE) methods. Finally, the automatic navigation and obstacle avoidance capabilities of robots in unknown environments were verified for achieving the objective of mobile robot control.

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