Path Planning for Mobile Robots Based on Ant Colony Algorithm Integrated with Time Warping Algorithm

Junqi Wu; Bo Huang; Chongshan Zhou

doi:10.54097/cepeda82

Authors

Junqi Wu
Bo Huang
Chongshan Zhou

DOI:

https://doi.org/10.54097/cepeda82

Keywords:

Ant Colony Algorithm; Path Planning; Heuristic Function; Pheromone Improvement; B-Spline Curve

Abstract

To address the slow convergence and tendency to fall into local optima of traditional ant colony algorithms in complex indoor environments, a new method integrating ant colony algorithm and time warping algorithm is proposed. This method improves convergence speed by adding a dynamic decay function to the heuristic function; prevents the algorithm from getting stuck in local optima by introducing a random disturbance function to dynamically adjust pheromones; enhances exploration capability by using the Dynamic Time Warping (DTW) algorithm to evaluate path similarity and assign "novelty" to planned paths; improves global search ability by introducing crossover operations from genetic algorithms; and finally smooths the output path using B-spline curves. Simulation in MATLAB shows that the GACOMR algorithm converges in 7 iterations with an optimal path length of 58.84m, outperforming other algorithms. Physical experiments with a mobile robot further confirm its feasibility.

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