Abstract
In this work, a method for finding the optimal path from an initial point to a final one in a previously defined static search map is presented, based on Ant Colony Optimization Meta-Heuristic (ACO-MH). The proposed algorithm supports the avoidance of dynamic obstacles; that is, once the optimal path is found and the robot starts navigating, if the robot’s route is interrupted by a new obstacle that was sensed at time t, it will recalculate an alternative optimal path from the actual robot position in order to surround this blocking object and reach the goal.
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García, M.A.P., Montiel, O., Castillo, O., Sepúlveda, R. (2007). Optimal Path Planning for Autonomous Mobile Robot Navigation Using Ant Colony Optimization and a Fuzzy Cost Function Evaluation. In: Melin, P., Castillo, O., Ramírez, E.G., Kacprzyk, J., Pedrycz, W. (eds) Analysis and Design of Intelligent Systems using Soft Computing Techniques. Advances in Soft Computing, vol 41. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72432-2_79
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DOI: https://doi.org/10.1007/978-3-540-72432-2_79
Publisher Name: Springer, Berlin, Heidelberg
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Online ISBN: 978-3-540-72432-2
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