Using Cellular Ant Colony Algorithm for Path-Planning of Robots

2012 ◽  
Vol 182-183 ◽  
pp. 1776-1780 ◽  
Author(s):  
Yong Fen Wu ◽  
Xin Xing Zhang ◽  
Jun Qing Wu
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Ant Colony ◽  
Ant Colonies ◽  
Ant Colony Algorithms ◽  
Current Region ◽  
Novel Method ◽  
Short Time ◽  
Robot Path

To overcome some shortcoming existed in the conventional ant colony algorithms, e.g. slow converging and trend for falling into local convergences, a novel method for robot path planning is introduced based on cellular ant colony. Firstly, two ant colonies were set to run with different strategies. Secondly, the existing ant colony paths were evolved by following the cellular rules, so that the ants could jump from the current region into the region with a solution. Experiment results showed that the proposed algorithm proved to be stable, and that the global optimal path was found in a short time in a number of iterations.

scholarly journals An Improved Ant Colony Algorithm of Robot Path Planning for Obstacle Avoidance

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Hong-Jun Wang ◽  
Yong Fu ◽  
Zhuo-Qun Zhao ◽  
You-Jun Yue
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Robot Control ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Ant Colony Algorithms ◽  
Mobile Robot Control ◽  
Technical Issues ◽  
Improved Ant Colony Algorithm ◽  
Robot Path

The obstacle avoidance in path planning, a hot topic in mobile robot control, has been extensively investigated. The existing ant colony algorithms, however, remain as drawbacks including failing to cope with narrow aisles in working areas, large amount of calculation, etc. To address above technical issues, an improved ant colony algorithm is proposed for path planning. In this paper, a new weighted adjacency matrix is presented to determine the walking direction and the narrow aisles therefore are avoided by redesigning the walking rules. Also, the best ant and the worst ant are introduced for the adjustment of pheromone to facilitate the searching process. The proposed algorithm guarantees that robots are able to find a satisfying path in the presence of narrow aisles. The simulation results show the effectiveness of the proposed algorithm.

Mobile Robot Path Planning Based on Ant Colony Optimization

2014 ◽  
Vol 687-691 ◽  
pp. 706-709
Author(s):  
Bao Feng Zhang ◽  
Ya Chun Wang ◽  
Xiao Ling Zhang
Keyword(s):  
Path Planning ◽  
High Performance ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Grid Method ◽  
Ant Colony ◽  
Infrared Sensors ◽  
Object Based ◽  
Starting Point ◽  
Robot Path

Global path planning is quoted in this paper. The stoical and global environment has been given to us, which is abstracted with grid method before we build the workspace model of the robot. With the adoption of the ant colony algorithm, the robot tries to find a path which is optimal or optimal-approximate path from the starting point to the destination. The robot with the built-in infrared sensors navigates autonomously to avoid collision the optimal path which has been built, and moves to the object. Based on the MATLAB platform, the simulation results indicate that the algorithm is rapid, simple, efficient and high-performance. Majority of traditional algorithms of the path planning have disadvantages, for instance, the method of artificial potential field is falling into the problem of local minimum value easily. ACO avoids these drawbacks, therefore the convergence period can be extended, and optimal path can be planned rapidly.

scholarly journals Path Planning Based on an Improved Ant Colony Algorithm

2018 ◽  
Vol 228 ◽  
pp. 01010
Author(s):  
Miaomiao Wang ◽  
Zhenglin Li ◽  
Qing Zhao ◽  
Fuyuan Si ◽  
Dianfang Huang
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Search Algorithm ◽  
Convergence Speed ◽  
Ant Colony ◽  
Local Optimum ◽  
Pheromone Concentration ◽  
Suppression Strategy ◽  
Improved Ant Colony Algorithm ◽  
Robot Path

The classical ant colony algorithm has the disadvantages of initial search blindness, slow convergence speed and easy to fall into local optimum when applied to mobile robot path planning. This paper presents an improved ant colony algorithm in order to solve these disadvantages. First, the algorithm use A* search algorithm for initial search to generate uneven initial pheromone distribution to solve the initial search blindness problem. At the same time, the algorithm also limits the pheromone concentration to avoid local optimum. Then, the algorithm optimizes the transfer probability and adopts the pheromone update rule of "incentive and suppression strategy" to accelerate the convergence speed. Finally, the algorithm builds an adaptive model of pheromone coefficient to make the pheromone coefficient adjustment self-adaptive to avoid falling into a local minimum. The results proved that the proposed algorithm is practical and effective.

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