The Community for Technology Leaders
RSS Icon
Los Angeles, CA
March 31, 2009 to April 2, 2009
ISBN: 978-0-7695-3507-4
pp: 705-713
The pace of development and automation urge the need of robots controlling much of the work which used to be done mainly by humans. The modern technology has emphasized on the need to move a robot in an environment which is dynamically changing. An example of such an application may be the use of robots in industry to carry tools and other materials from one place to other. Since many robots would be working together, we need to ensure a collision free navigation plan for each of the robots.In this paper we find out the nearly most optimal path of the robot using Genetic, ANN and A* algorithms at each instant of time of robot travel. It may be used by the industry to send robots for surveys, data acquisition, doing specific work etc. The collision free movement of robot in a moving obstacle environment can be used to move robot in a world of robots.Results show that all 3 algorithms are able to move the robot without any collisions.
Robotic simulation, robotic navigation control, moving obstacle problem, A* algorithm, Genetic algorithm, Artificial Neural Network, Back propagation
Rahul Kala, Anupam Shukla, Ritu Tiwari, Sourabh Rungta, R.R. Janghel, "Mobile Robot Navigation Control in Moving Obstacle Environment Using Genetic Algorithm, Artificial Neural Networks and A* Algorithm", CSIE, 2009, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009, pp. 705-713, doi:10.1109/CSIE.2009.854
39 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool