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 of Slab Library Crane Based on Improved Ant Colony Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Yuntao Zhao ◽  
Weigang Li ◽  
Xiao Wang ◽  
Chengxin Yi
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Turning Points ◽  
Grid Method ◽  
Ant Colony ◽  
Improved Ant Colony Algorithm

Due to the equipment characteristics (for example, the crane of each span cannot transfer products directly to other spans and path has less turning points and no slash lines) in a slab library, slab transportation is mainly realized by manually operating the crane. Firstly, the grid method is used to model the slab library. Secondly, an improved ant colony algorithm is proposed. The algorithm is used to solve the path planning of the slab library crane, which is improved by integrating the turning points, filtering the candidate solutions, dynamically evaporating pheromone, setting the dynamic region, etc. Finally, the algorithm is applied to plan the crane path of the slab library. The results show that the obstacle-free optimal path with fewer turning points, no slash lines, and short paths is found automatically.

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.

Path Planning for the Chassis of Duct-Cleaning Robot Based on Ant Colony Algorithm

2012 ◽  
Vol 190-191 ◽  
pp. 715-718
Author(s):  
Qian Zhu ◽  
Wei Sun ◽  
Zhi Wei Zhou ◽  
Su Wei Zhang
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Grid Method ◽  
Ant Colony ◽  
Simulation Experiments ◽  
Environment Model ◽  
Cleaning Robot ◽  
Duct Cleaning Robot ◽  
Duct Cleaning

For the path planning for the chassis of duct cleaning robot in an obstacle environment, ant colony algorithm and grid method are adopted to achieve an optimal path between two different arbitrary points and establish the environment model. The results of computer simulation experiments demonstrate the effectiveness of ant colony algorithm applied in path planning for the chassis of duct cleaning robot.

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 Research on path planning of cleaning robot based on an improved ant colony algorithm

2021 ◽  
Vol 336 ◽  
pp. 07005
Author(s):  
Zhidong Wang ◽  
Changhong Wu ◽  
Jing Xu ◽  
Hongjie Ling
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Grid Method ◽  
Ant Colony ◽  
Local Optimum ◽  
Cleaning Robot ◽  
Initial Stage ◽  
Searching Ability ◽  
Improved Ant Colony Algorithm

The conventional ant colony algorithm is easy to fall into the local optimal in some complex environments, and the blindness in the initial stage of search leads to long searching time and slow convergence. In order to solve these problems, this paper proposes an improved ant colony algorithm and applies it to the path planning of cleaning robot. The algorithm model of the environmental map is established according to the grid method. And it built the obstacle matrix for the expansion and treatment of obstacles, so that the robot can avoid collision with obstacles as much as possible in the process of movement. The directional factor is introduced in the new heuristic function, and we can reduce the value of the inflection point of paths, enhance the algorithm precision, and avoid falling into the local optimal. The volatile factor of pheromones with an adaptive adjustment and the improved updating rule of pheromones can not only solve the problem that the algorithm falls into local optimum, but also accelerate the running efficiency of the algorithm in the later stage. Simulation results show that the algorithm has the better global searching ability, the convergence speed is obviously accelerated, and an optimal path can be planned in the complex environment.

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.

Cutting trajectory planning of sections with complex composition for roadheader

2018 ◽  
Vol 233 (4) ◽  
pp. 1441-1452 ◽  
Author(s):  
Suyu Wang ◽  
Miao Wu
Keyword(s):  
Trajectory Planning ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Grid Method ◽  
Ant Colony ◽  
Local Optimum ◽  
Complex Composition ◽  
Cutting Head ◽  
Pheromone Concentration ◽  
Simulation Results

In order to realize the autonomous cutting for tunneling robot, the method of cutting trajectory planning of sections with complex composition was proposed. Firstly, based on the multi-sensor parameters, the existence, the location, and size of the dirt band were determined. The roadway section environment was modeled by grid method. Secondly, according to the cutting process and tunneling cutting characteristics, the cutting trajectory ant colony algorithm was proposed. To ensure the operation safety and avoid the cutting head collision, the expanding operation was adopt for dirt band, and the aborting strategy for the ants trapped in the local optimum was put forward to strengthen the pheromone concentration of the found path. The simulation results showed that the proposed method can be used to plan the optimal cutting trajectory. The ant colony algorithm was used to search for the shortest path to avoid collision with the dirt band, and the S-path cutting was used for the left area to fulfill section forming by following complete cover principle. All the ants have found the optimal path within 50 times iteration of the algorithm, and the simulation results were better than particle swarm optimization and basic ant colony optimization.

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