Mobile Robot Path Planning Based on Ant Colony Algorithm With A Heuristic Method.

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ID: 80323
2019
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Ranked #60 of 124 articles by views in frontiers in neurorobotics

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Abstract
This paper proposes an improved ant colony algorithm to achieve efficient searching capabilities of path planning in complicated maps for mobile robot. The improved ant colony algorithm uses the characteristics of A algorithm and MAX-MIN Ant system. Firstly, the grid environment model is constructed. The evaluation function of A algorithm and the bending suppression operator are introduced to improve the heuristic information of the Ant colony algorithm, which can accelerate the convergence speed and increase the smoothness of the global path. Secondly, the retraction mechanism is introduced to solve the deadlock problem. Then the MAX-MIN ant system is transformed into local diffusion pheromone and only the best solution from iteration trials can be added to pheromone update. And, strengths of the pheromone trails are effectively limited for avoiding premature convergence of search. This gives an effective improvement and high performance to ACO in complex tunnel, trough and baffle maps and gives a better result as compare to traditional versions of ACO. The simulation results show that the improved ant colony algorithm is more effective and faster.
Reference Key
dai2019mobilefrontiers Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Dai, Xiaolin;Long, Shuai;Zhang, Zhiwen;Gong, Dawei;
Journal frontiers in neurorobotics
Year 2019
DOI
10.3389/fnbot.2019.00015
URL
Keywords
path planning a* algorithm ant colony algorithm bending suppression retraction mechanism

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