scholarly journals Comparison of Classification Techniques for Wall Following Robot Navigation and Improvements to the KNN Algorithm

2019 ◽  
Author(s):  
Sarah Madi ◽  
Riadh Baba-Ali
Keyword(s):  
Robot Navigation ◽  
Classification Techniques ◽  
Wall Following

PCA-based method to fuse behaviors from place characterization for robot navigation

Robotica ◽  
2015 ◽  
Vol 35 (2) ◽  
pp. 254-270
Author(s):  
Alberto Poncela
Keyword(s):  
Principal Component Analysis ◽  
Autonomous Navigation ◽  
Feature Vector ◽  
Robot Navigation ◽  
Principal Component ◽  
Component Analysis ◽  
Time Performance ◽  
Sonar Sensors ◽  
Local Grid ◽  
Wall Following

SUMMARYThis paper presents a method to calculate the fusing rule among three reactive behaviors, Wall Following, Corridor Following and Door Crossing, from place characterization for robot navigation. The technique is supported by a local grid of the closest area to the robot, which is built from sonar readings. The contour of this grid is extracted, represented by itsFFTand, finally, it is reduced to a short feature vector with a principal component analysis (PCA). This feature vector is used to decide the fusing rule among the three behaviors. The algorithm is very fast in terms of its time performance, being then valid to be used in robot navigation, since the robot would rapidly react to new situations. It has also been successfully tested in simulated and real environments, with a Pioneer robot equipped with eight frontal sonar sensors, both in manually driven tasks and autonomous navigation tasks, proving its feasibility and effectiveness.

scholarly journals AN APPROACH DESIGNING AUTONOMOUS ROBOT NAVIGATION SYSTEM BASED ON BEHAVIOR COORDINATION

2017 ◽  
Vol 24 (4) ◽  
pp. 353-367
Author(s):  
Long Thanh Ngo ◽  
Long The Pham ◽  
Phuong Hoang Nguyen
Keyword(s):  
Navigation System ◽  
Fuzzy Inference ◽  
Robot Navigation ◽  
Autonomous Robot ◽  
Inference System ◽  
Unstructured Environment ◽  
Individual Task ◽  
Simulation Results ◽  
Autonomous Robot Navigation ◽  
Wall Following

Robot navigation using fuzzy behavior is suited in unknown and unstructured environment in which each behavior have an individual task. This paper deals with an approach designing autonomous robot navigation system based on fuzzy behaviors including collision avoidance, wall-following, go-to-target. The proposed hierarchy of fuzzy behaviors is used to fuse the command in which each behavior is a fuzzy inference system and its outputs are fuzzy sets. Its inputs are information fused from sensors using fuzzy directional relationship. The simulation results with some statistics show that the system works correctly. 

scholarly journals Object Following Design for a Mobile Robot using Neural Network

2017 ◽  
Vol 6 (1) ◽  
pp. 5-14 ◽  
Author(s):  
Neta Larasati ◽  
Tresna Dewi ◽  
Yurni Oktarina
Keyword(s):  
Mobile Robot ◽  
Robot Navigation ◽  
Kinematics And Dynamics ◽  
Robot Design ◽  
Safe Distance ◽  
Robot System ◽  
Intelligence System ◽  
Mathematical Equations ◽  
Applied Artificial Intelligence ◽  
Wall Following

Deciding the best method for robot navigation is the most important tasks in mobile robot design, defined as the robot's ability to reach the target or/and move around its environment safely using the installed sensors and/or predefined map. To achieve this objective, wall or object detection can be considered. It is common to derive kinematics and dynamics to design the controls system of the robot, however by giving intelligence system to the robot, the control system will provide better performance for robot navigation. One of the most applied artificial intelligence is neural networks, a good approach for sensors of mobile robot system that is difficult to be modeled with an accurate mathematical equations. Mostly discussed basic navigation of a mobile robot is wall following. Wall following robot has been used for many application not only in industrial as a transport robot but also in domestic or hospital. Two behaviors are designed in this paper, wall following and object following. Object following behavior is developed from wall following by utilizing data from 4 installed distance sensors. The leader robot as the target for the follower robot, therefore the follower robot will keep on trying reaching for the leader in a safe distance. The novelty of this research is in the sense of the simplicity of proposed method. The feasibility of our proposed design is proven by simulation where all the results shows the effectiveness of the proposed method.

Classification-based learning by particle swarm optimization for wall-following robot navigation

2013 ◽  
Vol 113 ◽  
pp. 27-35 ◽  
Author(s):  
Yen-Lun Chen ◽  
Jun Cheng ◽  
Chuan Lin ◽  
Xinyu Wu ◽  
Yongsheng Ou ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Robot Navigation ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Wall Following
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