Path Planning of Mobile Robot Based on Voronoi Diagram by Approximation Structuring and Zonal Ant Colony Algorithm

2009 ◽  
Author(s):  
Rui Wen ◽  
Hang-yu Wang ◽  
Jun Xie
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Voronoi Diagram ◽  
Ant Colony Algorithm ◽  
Ant Colony

scholarly journals Mobile robot path planning using an improved ant colony optimization

2018 ◽  
Vol 15 (3) ◽  
pp. 172988141877467 ◽  
Author(s):  
Khaled Akka ◽  
Farid Khaber
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Ant Colony Algorithm ◽  
Search Space ◽  
Step Length ◽  
Ant Colony ◽  
Dynamic Adjustment ◽  
Feedback Information ◽  
Intelligent Optimization Algorithm ◽  
Selection Of

Ant colony algorithm is an intelligent optimization algorithm that is widely used in path planning for mobile robot due to its advantages, such as good feedback information, strong robustness and better distributed computing. However, it has some problems such as the slow convergence and the prematurity. This article introduces an improved ant colony algorithm that uses a stimulating probability to help the ant in its selection of the next grid and employs new heuristic information based on the principle of unlimited step length to expand the vision field and to increase the visibility accuracy; and also the improved algorithm adopts new pheromone updating rule and dynamic adjustment of the evaporation rate to accelerate the convergence speed and to enlarge the search space. Simulation results prove that the proposed algorithm overcomes the shortcomings of the conventional algorithms.

scholarly journals Mobile Robot Path Planning Using Ant Colony Algorithm and Improved Potential Field Method

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Guoliang Chen ◽  
Jie Liu
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Potential Field ◽  
Ant Colony Algorithm ◽  
Field Method ◽  
Resultant Force ◽  
Ant Colony ◽  
Hybrid Strategy ◽  
Two Phases ◽  
Robot Path

For the problem of mobile robot’s path planning under the known environment, a path planning method of mixed artificial potential field (APF) and ant colony optimization (ACO) based on grid map is proposed. First, based on the grid model, APF is improved in three ways: the attraction field, the direction of resultant force, and jumping out the infinite loop. Then, the hybrid strategy combined global updating with local updating is developed to design updating method of the ACO pheromone. The process of optimization of ACO is divided into two phases. In the prophase, the direction of the resultant force obtained by the improved APF is used as the inspired factors, which leads ant colony to move in a directional manner. In the anaphase, the inspired factors are canceled, and ant colony transition is completely based on pheromone updating, which can overcome the inertia of the ant colony and force them to explore a new and better path. Finally, some simulation experiments and mobile robot environment experiments are done. The experiment results verify that the method has stronger stability and environmental adaptability.

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