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Mobile Robot Navigation using Amended Ant Colony Optimization Algorithm


  • School of Computing, SRM University, Kancheepuram - 603203, Tamil Nadu, India
  • Computer Software, Monash University, Melbourne, Australia
  • Monash University, Lagoon Selatan, Bandar Sunway, Subang Jaya - 47500, Selangor, Malaysia


Objectives: This paper presents an amended Ant Colony Optimization (ACO) algorithm for a mobile robot navigation to find the most optimal path. Methods: A modified design and development of an improved Ant Colony Optimization algorithm based upon a prior research work done is proposed in this paper. The algorithm put forth is enhanced by simplifying the equations already proposed and enlarging the area of the simulation framework, extending the task capabilities of the robot, as well as testing the algorithm in real time on an autonomous mobile robot. Findings: The proposed algorithm has to calculate optimal trajectory for the mobile robot to traverse to perform the following tasks: target-searching, boundaryfollowing and obstacle avoidance. The total length of the path traversed determines the efficiency of path traced. This proposed method also enhances the utility of the ACO algorithm by designing and creating a feasible ACO graphical user interface. Further we carried out the research on the working of the ACO algorithm by performing systematic testing, simulations and real-time implementation. Improvements: Future work could involve the implementation of a positioning system that allows the robot to determine its actual real world position and then provide feedback to the ACO algorithm so that adjustments could be made. The basic ACO algorithm could be modified to model ants to move in eight directions. All simulations and real time implementations could be done in pre-known environments with static well defined obstacles. By including dynamic obstacle avoidance capabilities, the range of real life applications in which the algorithm could be implemented on would be greatly expanded.


ACO Algorithm, Known Environment, Mobile Robot, Machine Learning, Navigation Planning, Static Obstacles.

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  • Kose M. Ant Colony Optimization for the Boundaryfollowing Robot Problem. Eastern Mediterranean University, Computer Engineering Department. 2004.
  • Goss S, Aron S, Deneubourg JL, Pasteels JM. Self-organised shortcuts in the Argentine Ants, Naturwissenschaften.1989; 76:579–81.
  • Maniezzo V, Gambardella LM, Luigi F. Ant Colony Optimization. New Optimization Techniques in Engineering.Volume 141 of the series Studies in Fuzziness and Soft Computing. 2004; 101–21.
  • Wikipedia. Metaheuristics. Available from: http:// Date accessed: 09/ 2016.
  • Mataric MA. Distributed Model for Mobile Robot Environment-Learning and Navigation. MIT Artificial Intelligence Laboratory Technical Report.1990; AI-TR1228.
  • Dizaji ZA, Gharehchopogh FS. A Hybrid of Ant Colony Optimization and Chaos Optimization Algorithms Approach for Software Cost Estimation. Indian Journal of Science and Technology. 2015; 8(2):128–33.
  • Kanaka Vardhini K, Sitamahalakshmi T. A Review on Nature-based Swarm Intelligence Optimization Techniques and its Current Research Directions. Indian Journal of Science and Technology. 2016; 9(10):1–13.
  • Sakthipriya N, Kalaipriyan T. Variants of Ant Colony Optimization- A State of an Art. Indian Journal of Science and Technology. 2015; 8(31):1–15.
  • Koza JR. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, Massachusetts. 1993.
  • Borgstrom J. ARIA and Matlab Integration With Applications. Master’s Thesis Project. Department of Computing Science, Umea University. 2005.
  • Schwab B. AI Game Engine Programming. Charles River Media, inc. 2004.
  • Kose M, Acan A. Knowledge Incorporation into ACOBased Autonomous Mobile Robot Navigation. Eastern Mediterranean University, Computer Engineering Department. 2004.
  • Cicirello VA, Smith SF. Ant Colony Control for Autonomous Decentralized Shop Floor Routing. The Robotics Institute, Carnegie Mellon University. 2001.
  • Dorigo M, Stutzle T. Ant Colony Optimization. MIT Press, Massachusetts Institute of Technology. 2004.
  • Mobile Robots Inc official website. Available from: http:// (Accessed: September 2016)
  • Mobile Robots Inc. Team AmigoBot Operations Manual.2016.
  • Ross SJ, Daida JM, Doan CM, Bersano-Begey TF, McClain JJ. Variations in Evolution of Subsumption Architectures using Genetic Programming: The Wall Following Robot Revisited. Genetic Programming: Proceedings of the First Annual Conference, The MIT Press, Stanford University, 1996. p. 28–31.


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