Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2019 Apr 16:13:15.
doi: 10.3389/fnbot.2019.00015. eCollection 2019.

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

Xiaolin Dai  1   2 , Shuai Long  1 , Zhiwen Zhang  1 , Dawei Gong  1   2
Affiliations

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

Xiaolin Dai et al. Front Neurorobot. .

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.

Keywords: A* algorithm; ant colony algorithm; bending suppression; path planning; retraction mechanism.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Environment model.
Figure 2
Figure 2
Deadlock state diagram.
Figure 3
Figure 3
The test results of three algorithms run on common map. (A) Simulation results in 20*20 grid. (B) Convergence curve.
Figure 4
Figure 4
The test results of three algorithms run on tunnel map. (A) Simulation results in 30*30 grid. (B) Convergence curve.
Figure 5
Figure 5
The test results of three algorithms run on trough map. (A) Simulation results in 40*40 grid. (B) Convergence curve. (Other two algorithms is failed in trough map).
Figure 6
Figure 6
The test results of three algorithms run on baffle map. (A) Simulation results in 20*20 grid. (B) Convergence curve. (Other two algorithms is failed in trough map).
Figure 7
Figure 7
The test results of two algorithms run on trough map. (A) Path planning comparison. (B) Ant retraction number curve.
Figure 8
Figure 8
The test results of two algorithms run on baffle map. (A) Path planning comparison. (B) Ant retraction number curve.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Arantes J. D., Arantes M. D., Toledo C. F., Júnior O. T., Williams B. C. (2017). Heuristic and genetic algorithm approaches for UAV path planning under critical situation. Int. J. Artificial Intelligence Tools 26, 1–30. 10.1142/S0218213017600089 - DOI
    1. Bakdi A., Hentout A., Boutami H., Maoudj A., Hachour O., Bouzouia B. (2016). Optimal path planning and execution for mobile robots using genetic algorithm and adaptive fuzzy-logic control. Robot. Autonomous Syst. 89, 95–109. 10.1016/j.robot.2016.12.008 - DOI
    1. Cetin O., Yilmaz G. (2014). Sigmoid limiting functions and potential field based autonomous air refueling path planning for UAVs. J. Intelligent Robot. Syst. 73, 797–810. 10.1007/s10846-013-9902-y - DOI
    1. Cheng C. T., Fallahi K., Leung H., Tse C. K. (2010). An AUVs path planner using genetic algorithms with a deterministic crossover operator. IEEE Int. Conference Robot. Automat. 2010, 2995–3000. 10.1109/ROBOT.2010.5509335 - DOI
    1. Das P. K., Behera H. S., Panigrahi B. K. (2016). A hybridization of an improved particle swarm optimization and gravitational search algorithm for multi-robot path planning. Swarm Evol. Comput. 28, 14–28. 10.1016/j.swevo.2015.10.011 - DOI

LinkOut - more resources