Optimal travel path planning and real time forecast system based on ant colony algorithm

2017 ◽  
Author(s):  
Shan Xiao
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Forecast System ◽  
Travel Path

Mobile Robot Design Based on Ant Colony Algorithm

2014 ◽  
Vol 484-485 ◽  
pp. 1134-1137
Author(s):  
Li Cai ◽  
Hong Xia Wu ◽  
Rong Zhang
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Real Time ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Robot Design ◽  
Simulation Results

This paper proposes a mobile robot design based on ant colony algorithm, aiming at how to achieve the optimization of path planning. Obstacle detecting and avoidance method for mobile robot are implemented with the photoelectric sensors. Then the ant colony algorithm for path planning is introduced and the simulation results in the software show that the method of introducing ant colony algorithm into mobile robot is convenient, feasible. By this means, the optimum problem is well resolved in real-time way during the running of mobile robot.

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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