A new path planning strategy integrating improved ACO and DWA algorithms for mobile robots in dynamic environments

Baoye Song; Shumin Tang; Yao Li; Baoye Song; Shumin Tang; Yao Li

doi:10.3934/mbe.2024096

Mathematical Biosciences and Engineering

2024, Volume 21, Issue 2: 2189-2211. doi: 10.3934/mbe.2024096

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Research article

A new path planning strategy integrating improved ACO and DWA algorithms for mobile robots in dynamic environments

College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China

Academic Editor: Hamid Reza Karimi

Received: 29 November 2023 Revised: 21 December 2023 Accepted: 04 January 2024 Published: 11 January 2024

This article is concerned with the path planning of mobile robots in dynamic environments. A new path planning strategy is proposed by integrating the improved ant colony optimization (ACO) and dynamic window approach (DWA) algorithms. An improved ACO is developed to produce a globally optimal path for mobile robots in static environments. Through improvements in the initialization of pheromones, heuristic function, and updating of pheromones, the improved ACO can lead to a shorter path with fewer turning points in fewer iterations. Based on the globally optimal path, a modified DWA is presented for the path planning of mobile robots in dynamic environments. By deleting the redundant nodes, optimizing the initial orientation, and improving the evaluation function, the modified DWA can result in a more efficient path for mobile robots to avoid moving obstacles. Some simulations are conducted in different environments, which confirm the effectiveness and superiority of the proposed path planning algorithms.
- mobile robots,
- path planning,
- ant colony optimization,
- dynamic window approach,
- ACO,
- DWA
Citation: Baoye Song, Shumin Tang, Yao Li. A new path planning strategy integrating improved ACO and DWA algorithms for mobile robots in dynamic environments[J]. Mathematical Biosciences and Engineering, 2024, 21(2): 2189-2211. doi: 10.3934/mbe.2024096

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Abstract

This article is concerned with the path planning of mobile robots in dynamic environments. A new path planning strategy is proposed by integrating the improved ant colony optimization (ACO) and dynamic window approach (DWA) algorithms. An improved ACO is developed to produce a globally optimal path for mobile robots in static environments. Through improvements in the initialization of pheromones, heuristic function, and updating of pheromones, the improved ACO can lead to a shorter path with fewer turning points in fewer iterations. Based on the globally optimal path, a modified DWA is presented for the path planning of mobile robots in dynamic environments. By deleting the redundant nodes, optimizing the initial orientation, and improving the evaluation function, the modified DWA can result in a more efficient path for mobile robots to avoid moving obstacles. Some simulations are conducted in different environments, which confirm the effectiveness and superiority of the proposed path planning algorithms.

References

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