Real-time obstacle avoidance for manipulators and mobile robots
International Journal of Robotics Research
Genetic and evolutionary algorithms come of age
Communications of the ACM
A Unified Approach to Planning, Sensing and Navigation for Mobile Robots
The 3rd International Symposium on Experimental Robotics III
Genetic Algorithms for Adaptive Motion Planning of an Autonomous Mobile Robot
CIRA '97 Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation
Ant Colony Optimization
The Ant Algorithm for Solving Robot Path Planning Problem
ICITA '05 Proceedings of the Third International Conference on Information Technology and Applications (ICITA'05) Volume 2 - Volume 02
Introduction to Genetic Algorithms
Introduction to Genetic Algorithms
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Hi-index | 0.00 |
This paper presents a variation of Ant Colony System (ACS) algorithm utilized for Robot Path Planning (RPP) purposes. An accurate representation of heuristic and visibility equation of state transition rules is proposed to sustain the function of Ant Colony System (ACS) for solving RPP problem of finding the optimal path. This algorithm was applied within a global static map that consists of feasible free space nodes. The performance of algorithms in terms of computation time and number of iteration required to obtain an optimal path were evaluated by using a simulation approach. The results were discussed thoroughly and compared with other evolutionary approaches to verify the effectiveness of the proposed ACS.