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.

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 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.

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

2021 ◽  
Vol 2083 (4) ◽  
pp. 042033
Author(s):  
Yanyun Li ◽  
Fenggang Liu
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Path Length ◽  
Ant Colony Algorithm ◽  
Planning Process ◽  
Optimal Path ◽  
Ant Colony ◽  
Position Information ◽  
Environment Model ◽  
Planning Algorithm

Abstract Due to the influence of full traversal environment, the path length obtained by existing methods is too long. In order to improve the performance of path planning and obtain the optimal path, a full traversal path planning method for omnidirectional mobile robots based on ant colony algorithm is proposed. On the basis of the topology modeling schematic diagram, according to the position information of the mobile robot in the original coordinate system, a new environment model is established by using the Angle transformation. Considering the existing problems of ant colony algorithm, the decline coefficient is introduced into the heuristic function to update the local pheromone, and the volatility coefficient of the pheromone is adjusted by setting the iteration threshold. Finally, through the design of path planning process, the planning of omnidirectional mobile robot’s full traversal path is realized. Experimental results show that the proposed method can not only shorten the full traversal path length, but also shorten the time of path planning to obtain the optimal path, thus improving the performance of full traversal path planning of omnidirectional mobile robot.

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.

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.

scholarly journals Modular Robotic Design and Reconfiguring Path Planning

2022 ◽  
Vol 12 (2) ◽  
pp. 723
Author(s):  
Ye Dai ◽  
Chao-Fang Xiang ◽  
Zhao-Xu Liu ◽  
Zhao-Long Li ◽  
Wen-Yin Qu ◽  
...  
Keyword(s):  
Path Planning ◽  
Industrial Application ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Configuration Design ◽  
Functional Modules ◽  
Simulation Experiments ◽  
Innovative Design ◽  
And Performance ◽  
Robot Configurations

The modular robot is becoming a prevalent research object in robots because of its unique configuration advantages and performance characteristics. It is possible to form robot configurations with different functions by reconfiguring functional modules. This paper focuses on studying the modular robot’s configuration design and self-reconfiguration process and hopes to realize the industrial application of the modular self-reconfiguration robot to a certain extent. We design robotic configurations with different DOF based on the cellular module of the hexahedron and perform the kinematic analysis of the structure. An innovative design of a modular reconfiguration platform for conformational reorganization is presented, and the collaborative path planning between different modules in the reconfiguration platform is investigated. We propose an optimized ant colony algorithm for reconfiguration path planning and verify the superiority and rationality of this algorithm compared with the traditional ant colony algorithm for platform path planning through simulation experiments.

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