scholarly journals Smooth Path Planning of Mobile Robot Based on Improved Ant Colony Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wenming Wang ◽  
Jiangdong Zhao ◽  
Zebin Li ◽  
Ji Huang
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Local Optimum ◽  
Planning Problem ◽  
Turning Angle ◽  
Heuristic Information ◽  
Starting Point ◽  
Improved Ant Colony Algorithm

Aiming at the problems of slow convergence, easy to fall into local optimum, and poor smoothness of traditional ant colony algorithm in mobile robot path planning, an improved ant colony algorithm based on path smoothing factor was proposed. Firstly, the environment map was constructed based on the grid method, and each grid was marked to make the ant colony move from the initial grid to the target grid for path search. Then, the heuristic information is improved by referring to the direction information of the starting point and the end point and combining with the turning angle. By improving the heuristic information, the direction of the search is increased and the turning angle of the robot is reduced. Finally, the pheromone updating rules were improved, the smoothness of the two-dimensional path was considered, the turning times of the robot were reduced, and a new path evaluation function was introduced to enhance the pheromone differentiation of the effective path. At the same time, the Max-Min Ant System (MMAS) algorithm was used to limit the pheromone concentration to avoid being trapped in the local optimum path. The simulation results show that the improved ant colony algorithm can search the optimal path length and plan a smoother and safer path with fast convergence speed, which effectively solves the global path planning problem of mobile robot.

scholarly journals An Improved Ant Colony Algorithm for Solving the Path Planning Problem of the Omnidirectional Mobile Vehicle

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Jiang Zhao ◽  
Dingding Cheng ◽  
Chongqing Hao
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Selection Strategy ◽  
Planning Problem ◽  
Evaporation Coefficient ◽  
Mobile Vehicle ◽  
Starting Point ◽  
Path Planning Problem ◽  
Improved Ant Colony Algorithm

This paper presents an improved ant colony algorithm for the path planning of the omnidirectional mobile vehicle. The purpose of the improved ant colony algorithm is to design an appropriate route to connect the starting point and ending point of the environment with obstacles. Ant colony algorithm, which is used to solve the path planning problem, is improved according to the characteristics of the omnidirectional mobile vehicle. And in the improved algorithm, the nonuniform distribution of the initial pheromone and the selection strategy with direction play a very positive role in the path search. The coverage and updating strategy of pheromone is introduced to avoid repeated search reducing the effect of the number of ants on the performance of the algorithm. In addition, the pheromone evaporation coefficient is segmented and adjusted, which can effectively balance the convergence speed and search ability. Finally, this paper provides a theoretical basis for the improved ant colony algorithm by strict mathematical derivation, and some numerical simulations are also given to illustrate the effectiveness of the theoretical results.

Path Planning for Mobile Robot Based on Improved Ant Colony Algorithm

2013 ◽  
Vol 385-386 ◽  
pp. 717-720 ◽  
Author(s):  
Rui Wang ◽  
Zai Tang Wang
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Ant Colony Algorithm ◽  
Probability Function ◽  
Ant Colony ◽  
Local Optimum ◽  
Dynamic Path Planning ◽  
Dynamic Path ◽  
Improved Ant Colony Algorithm ◽  
Improved Algorithm

This paper presents a dynamic path planning method based on improved ant colony algorithm. In order to increasing the algorithm’s convergence speed and avoiding to fall into local optimum, we propose adaptive migratory probability function and updating the pheromone. We apply the improved algorithm to path planning for mobile robot and the simulation experiment proved that improved algorithm is viable and efficient.

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.

scholarly journals 3D Path Planning for the Ground Robot with Improved Ant Colony Optimization

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 815 ◽  
Author(s):  
Lanfei Wang ◽  
Jiangming Kan ◽  
Jun Guo ◽  
Chao Wang
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Visual Fields ◽  
Environmental Modeling ◽  
Ant Colony ◽  
Local Optimum ◽  
Search Range ◽  
Planning Stage ◽  
Average Running Time ◽  
Improved Ant Colony Algorithm

Path planning is a fundamental issue in the aspect of robot navigation. As robots work in 3D environments, it is meaningful to study 3D path planning. To solve general problems of easily falling into local optimum and long search times in 3D path planning based on the ant colony algorithm, we proposed an improved the pheromone update and a heuristic function by introducing a safety value. We also designed two methods to calculate safety values. Concerning the path search, we designed a search mode combining the plane and visual fields and limited the search range of the robot. With regard to the deadlock problem, we adopted a 3D deadlock-free mechanism to enable ants to get out of the predicaments. With respect to simulations, we used a number of 3D terrains to carry out simulations and set different starting and end points in each terrain under the same external settings. According to the results of the improved ant colony algorithm and the basic ant colony algorithm, paths planned by the improved ant colony algorithm can effectively avoid obstacles, and their trajectories are smoother than that of the basic ant colony algorithm. The shortest path length is reduced by 8.164%, on average, compared with the results of the basic ant colony algorithm. We also compared the results of two methods for calculating safety values under the same terrain and external settings. Results show that by calculating the safety value in the environmental modeling stage in advance, and invoking the safety value directly in the path planning stage, the average running time is reduced by 91.56%, compared with calculating the safety value while path planning.

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