Paper Info
Title
Mobile Robot Path Planning Based on Ant Colony Algorithm With A * Heuristic Method.
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.
Year
DOI
Venue
2019
10.3389/fnbot.2019.00015
FRONTIERS IN NEUROROBOTICS
Keywords
Field
DocType
path planning,ant colony algorithm,A* algorithm,bending suppression,retraction mechanism
Motion planning,Ant colony optimization algorithms,Mathematical optimization,Heuristic,Premature convergence,Computer science,Deadlock,Evaluation function,Artificial intelligence,Mobile robot,Machine learning,A* search algorithm
Journal
Volume
ISSN
Citations 
13
1662-5218
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Xiaolin Dai100.34
Shuai Long200.34
Zhiwen Zhang301.01
Dawei Gong411.03