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.

scholarly journals An Improved Path Planning Method Based on Artificial Potential Field for a Mobile Robot

2015 ◽  
Vol 15 (2) ◽  
pp. 181-191 ◽  
Author(s):  
Wenbai Chen ◽  
Xibao Wu ◽  
Yang Lu
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Potential Field ◽  
Field Method ◽  
Artificial Potential Field ◽  
Improved Method ◽  
Chaos Optimization ◽  
Potential Field Method ◽  
Artificial Potential Field Method ◽  
Robot Path

Abstract To solve the problem of local minima and unreachable destination of the traditional artificial potential field method in mobile robot path planning, chaos optimization is introduced to improve the artificial potential field method. The potential field function was adopted as a target function of chaos optimization, and a kind of “two-stage” chaos optimization was used. The corresponding movement step and direction of the robot were achieved by chaos search. Comparison of the improved method proposed in this paper and the traditional artificial potential field method is performed by simulation. The simulation results show that the improved method gets rid of the drawbacks, such as local minima and unreachable goal. Furthermore, the improved method is also verified by building up a physical platform based on “Future Star” robot. The success of the physical experiment indicates that the improved algorithm is feasible and efficient for mobile robot path planning.

The free step length ant colony algorithm in mobile robot path planning

2016 ◽  
Vol 30 (23) ◽  
pp. 1509-1514 ◽  
Author(s):  
Ming-Ru Zeng ◽  
Lu Xi ◽  
Ai-Min Xiao
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Ant Colony Algorithm ◽  
Step Length ◽  
Ant Colony ◽  
Robot Path Planning ◽  
Robot Path

scholarly journals Path Planning for Construction Machinery Based on Improved Potential Field Ant Colony Algorithm

2021 ◽  
Vol 2095 (1) ◽  
pp. 012062
Author(s):  
Peigang Li ◽  
Pengcheng Li ◽  
Yining Xie ◽  
Xianying Feng ◽  
Bin Hu ◽  
...  
Keyword(s):  
Path Planning ◽  
Potential Field ◽  
Ant Colony Algorithm ◽  
Weight Coefficient ◽  
Field Method ◽  
Ant Colony ◽  
Construction Machinery ◽  
Field Force ◽  
Heuristic Information ◽  
Planning Algorithm

Abstract The path planning algorithm of unmanned construction machinery is studied, and the potential field ant colony algorithm is improved to be applied in the field of unmanned construction machinery. Firstly, the raster map modeling was optimized to eliminate the trap grid in the map. At the beginning of algorithm iteration, the heuristic information of artificial potential field method was added and the global pheromone updating model was improve the convergence speed of the algorithm. In addition, the weight coefficient of potential field force and local pheromone updating model were introduced to enhance the development of raster map in the later iteration of ant colony algorithm and reduce the influence of heuristic information of potential field force. Finally, the selection range of parameters such as optimal pheromone heuristic factor and ant colony number is determined by simulation, and it is verified that the algorithm is better than the basic ant colony algorithm.

The Application of an Improved Ant Colony Algorithm in Mobile Robot Path Planning

2011 ◽  
Vol 467-469 ◽  
pp. 222-225 ◽  
Author(s):  
Xiao Guang Zhu ◽  
Qing Yao Han ◽  
Zhang Qi Wang
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Ant Colony Algorithm ◽  
Special Functions ◽  
Grid Method ◽  
Ant Colony ◽  
Robot Path Planning ◽  
Improve Performance ◽  
Improved Ant Colony Algorithm ◽  
Robot Path

This paper presents an improved ant colony algorithm to plan an optimal collision-free path for mobile robot in complicated static environment. Based on the work space model with grid method, simulated foraging behavior of ants and to serve the mobile robot path planning, update the conventional ant colony algorithm with some special functions. To avoid mobile robot path deadlock, a dead-corner table is established and the penalty function is used to update the trail intensity when an ant explores a dead—corner in the path searching. The simulation results show that the algorithm can improve performance of path planning obviously, and the algorithm is simple and effective.

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