Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation

Authors:
M.A. Porta Garcia;Oscar Montiel;Oscar Castillo;Roberto Sepúlveda;Patricia Melin
Affiliations:
Centro de Investigación y Desarrollo de Tecnología Digital del IPN (CITEDI), Av. del Parque 1310, Tijuana, B.C., Mexico;Centro de Investigación y Desarrollo de Tecnología Digital del IPN (CITEDI), Av. del Parque 1310, Tijuana, B.C., Mexico;Department of Computing Science, Tijuana Institute of Technology, P.O. Box 4207, Chula Vista, CA, 91909, USA;Centro de Investigación y Desarrollo de Tecnología Digital del IPN (CITEDI), Av. del Parque 1310, Tijuana, B.C., Mexico;Department of Computing Science, Tijuana Institute of Technology, P.O. Box 4207, Chula Vista, CA, 91909, USA
Venue:
Applied Soft Computing
Year:
2009

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Abstract

In the Motion Planning research field, heuristic methods have demonstrated to outperform classical approaches gaining popularity in the last 35 years. Several ideas have been proposed to overcome the complex nature of this NP-Complete problem. Ant Colony Optimization algorithms are heuristic methods that have been successfully used to deal with this kind of problems. This paper presents a novel proposal to solve the problem of path planning for mobile robots based on Simple Ant Colony Optimization Meta-Heuristic (SACO-MH). The new method was named SACOdm, where d stands for distance and m for memory. In SACOdm, the decision making process is influenced by the existing distance between the source and target nodes; moreover the ants can remember the visited nodes. The new added features give a speed up around 10 in many cases. The selection of the optimal path relies in the criterion of a Fuzzy Inference System, which is adjusted using a Simple Tuning Algorithm. The path planner application has two operating modes, one is for virtual environments, and the second one works with a real mobile robot using wireless communication. Both operating modes are global planners for plain terrain and support static and dynamic obstacle avoidance.