Reactive Navigation and Online SLAM in Autonomous Frontier-Based Exploration

2013 ◽  
pp. 45-55 ◽  
Author(s):  
Raúl Arnau Prieto ◽  
José Manuel Cuadra-Troncoso ◽  
José Ramón Álvarez-Sánchez ◽  
Israel Navarro Santosjuanes
Keyword(s):  
Reactive Navigation

Reactive navigation of wheeled robots for border patrolling

2016 ◽  
pp. 63-111
Author(s):  
Alexey S. Matveev ◽  
Andrey V. Savkin ◽  
Michael Hoy ◽  
Chao Wang
Keyword(s):  
Reactive Navigation ◽  
Wheeled Robots

Reactive navigation among moving and deforming obstacles

2016 ◽  
pp. 185-227
Author(s):  
Alexey S. Matveev ◽  
Andrey V. Savkin ◽  
Michael Hoy ◽  
Chao Wang
Keyword(s):  
Reactive Navigation

Reactive Navigation based on Self-Organised Visual Information

2002 ◽  
pp. 193-204
Author(s):  
G. Tascini ◽  
A. Montesanto ◽  
P. Puliti ◽  
N. Rabascini
Keyword(s):  
Visual Information ◽  
Reactive Navigation

scholarly journals MODIFIED VIRTUAL SEMI-CIRCLE PATH PLANNING

2016 ◽  
Vol 78 (6-6) ◽  
Author(s):  
R. N. Farah ◽  
Amira Shahirah ◽  
N. Irwan ◽  
R. L. Zuraida
Keyword(s):  
Path Planning ◽  
Path Length ◽  
Optimal Path ◽  
Unmanned Ground Vehicle ◽  
Ground Vehicle ◽  
Reactive Navigation ◽  
Optimal Path Planning ◽  
Two Phases

The challenging part of path planning for an Unmanned Ground Vehicle (UGV) is to conduct a reactive navigation. Reactive navigation is implemented to the sensor based UGV. The UGV defined the environment by collecting the information to construct it path planning. The UGV in this research is known as Mobile Guard UGV-Truck for Surveillance (MG-TruckS). Modified Virtual Semi Circle (MVSC) helps the MG-TruckS to reach it predetermined goal point successfully without any collision. MVSC is divided into two phases which are obstacles detection phase and obstacles avoidance phase to compute an optimal path planning. MVSC produces shorter path length, smoothness of velocity and reach it predetermined goal point successfully.

Neural network and fuzzy logic techniques based collision avoidance for a mobile robot

Robotica ◽  
1997 ◽  
Vol 15 (6) ◽  
pp. 627-632 ◽  
Author(s):  
Minglu Zhang ◽  
Shangxian Peng ◽  
Qinghao Meng
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Mobile Robot ◽  
Fuzzy Controller ◽  
Perceptual Space ◽  
Space Partitioning ◽  
Reactive Navigation ◽  
3 Dimensional ◽  
Sensory Data ◽  
Cluttered Environment

This paper is concerned with a mobile robot reactive navigation in an unknown cluttered environment based on neural network and fuzzy logic. Reactive navigation is a mapping between sensory data and commands without planning. This article's task is to provide a steering command letting a mobile robot avoid a collision with obstacles. In this paper, the authors explain how to perform a currently perceptual space partitioning for a mobile robot by the use of an ART neural network, and then, how to build a 3-dimensional fuzzy controller for mobile robot reactive navigation. The results presented, whether experimented or simulation, show that our method is well adapted to this type of problem.

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