A Path Planning Method of Robot Arm Obstacle Avoidance Based on Dynamic Recursive Ant Colony Algorithm

2019 ◽  
Author(s):  
Zhao Huadong ◽  
Lei Chaofan ◽  
Jiang Nan
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Ant Colony Algorithm ◽  
Planning Method ◽  
Ant Colony ◽  
Robot Arm

A path planning method using adaptive polymorphic ant colony algorithm for smart wheelchairs

2018 ◽  
Vol 25 ◽  
pp. 50-57 ◽  
Author(s):  
Zhuqing Jiao ◽  
Kai Ma ◽  
Yiling Rong ◽  
Peng Wang ◽  
Hongkai Zhang ◽  
...  
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Planning Method ◽  
Ant Colony ◽  
Smart Wheelchairs

scholarly journals Path planning of lunar robot based on dynamic adaptive ant colony algorithm and obstacle avoidance

2020 ◽  
Vol 17 (3) ◽  
pp. 172988141989897 ◽  
Author(s):  
Shinan Zhu ◽  
Weiyi Zhu ◽  
Xueqin Zhang ◽  
Tao Cao
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Ant Colony Algorithm ◽  
Global Search ◽  
Convergence Speed ◽  
Ant Colony ◽  
Local Optimum ◽  
Adaptive Potential ◽  
Avoidance Strategy

Path planning of lunar robots is the guarantee that lunar robots can complete tasks safely and accurately. Aiming at the shortest path and the least energy consumption, an adaptive potential field ant colony algorithm suitable for path planning of lunar robot is proposed to solve the problems of slow convergence speed and easy to fall into local optimum of ant colony algorithm. This algorithm combines the artificial potential field method with ant colony algorithm, introduces the inducement heuristic factor, and adjusts the state transition rule of the ant colony algorithm dynamically, so that the algorithm has higher global search ability and faster convergence speed. After getting the planned path, a dynamic obstacle avoidance strategy is designed according to the predictable and unpredictable obstacles. Especially a geometric method based on moving route is used to detect the unpredictable obstacles and realize the avoidance of dynamic obstacles. The experimental results show that the improved adaptive potential field ant colony algorithm has higher global search ability and faster convergence speed. The designed obstacle avoidance strategy can effectively judge whether there will be collision and take obstacle avoidance measures.

Obstacle Avoidance Path Planning for Harvesting Robot Manipulator Based on MAKLINK Graph and Improved Ant Colony Algorithm

2014 ◽  
Vol 530-531 ◽  
pp. 1063-1067 ◽  
Author(s):  
Wei Ji ◽  
Jun Le Li ◽  
De An Zhao ◽  
Yang Jun
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Ant Colony Algorithm ◽  
Robot Manipulator ◽  
Ant Colony ◽  
Dijkstra Algorithm ◽  
Short Time ◽  
Vector Description ◽  
Harvesting Robot ◽  
Improved Ant Colony Algorithm

To the problems of real-time obstacle avoidance path planning for apple harvesting robot manipulator in dynamic and unstructured environment, a method based on improved ant colony algorithm is presented. Firstly, Vector description is utilized to describe the area where obstacles such as branches is located as irregular polygon in free space, and MAKLINK graph is used to build up the environment space model. Then, the improved Dijkstra algorithm is used to find the initial walk path for apple harvesting robot manipulator. Finally, the improved ant colony algorithm is applied to optimize the initial path. The experiment result shows that the proposed method is simple and the robot manipulator can avoid the branches to pick the apple successfully in a relatively short time.

Global Path Planning Method for USV System Based on Improved Ant Colony Algorithm

2014 ◽  
Vol 568-570 ◽  
pp. 785-788 ◽  
Author(s):  
Chang Hui Song
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Main Idea ◽  
Planning Method ◽  
Ant Colony ◽  
Grid Environment ◽  
Environment Model ◽  
Global Path Planning ◽  
The Right ◽  
Improved Ant Colony Algorithm

An improved ant colony algorithm based grid environment model for global path planning method for USV was introduced. The main idea of the improved ant colony algorithm was distributing each ant route dynamically. When the active ant was selecting the next route, this algorithm program determined the nearest direction to the end point. There were many possible route points which were distributed artificially. Thereby, the probability for each ant to choose the right direction was increased. The simulating results demonstrate that the improved ant colony algorithm in this paper is very suitable for solving the question of global path planning for USV system in the complex oceanic environment where there are a lot of obstacles. At the same time, this method costs less time, and the path is very smooth.

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