Improved Adaptive Ant Colony Algorithm for Obstacle Avoidance Path Planning of Mobile Robots

2021 ◽  
Vol 10 (06) ◽  
pp. 2073-2082
Author(s):  
静 王
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Obstacle Avoidance ◽  
Ant Colony Algorithm ◽  
Ant Colony

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.

scholarly journals An Improved Ant Colony Algorithm of Robot Path Planning for Obstacle Avoidance

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Hong-Jun Wang ◽  
Yong Fu ◽  
Zhuo-Qun Zhao ◽  
You-Jun Yue
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Robot Control ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Ant Colony Algorithms ◽  
Mobile Robot Control ◽  
Technical Issues ◽  
Improved Ant Colony Algorithm ◽  
Robot Path

The obstacle avoidance in path planning, a hot topic in mobile robot control, has been extensively investigated. The existing ant colony algorithms, however, remain as drawbacks including failing to cope with narrow aisles in working areas, large amount of calculation, etc. To address above technical issues, an improved ant colony algorithm is proposed for path planning. In this paper, a new weighted adjacency matrix is presented to determine the walking direction and the narrow aisles therefore are avoided by redesigning the walking rules. Also, the best ant and the worst ant are introduced for the adjustment of pheromone to facilitate the searching process. The proposed algorithm guarantees that robots are able to find a satisfying path in the presence of narrow aisles. The simulation results show the effectiveness of the proposed algorithm.

Research on path planning based on new fusion algorithm for autonomous vehicle

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142091123
Author(s):  
ChaoChun Yuan ◽  
Yue Wei ◽  
Jie Shen ◽  
Long Chen ◽  
Youguo He ◽  
...  
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Ant Colony Algorithm ◽  
Autonomous Vehicle ◽  
Ant Colony ◽  
Fusion Algorithm ◽  
Simulink Simulation ◽  
Initial Stage ◽  
Pheromone Concentration

Ant colony algorithm or artificial potential field is commonly used for path planning of autonomous vehicle. However, vehicle dynamics and road adhesion coefficient are not taken into consideration. In addition, ant colony algorithm has blindness/randomness due to low pheromone concentration at initial stage of obstacle avoidance path searching progress. In this article, a new fusion algorithm combining ant colony algorithm and improved potential field is introduced making autonomous vehicle avoid obstacle and drive more safely. Controller of path planning is modeled and analyzed based on simulation of CarSim and Simulink. Simulation results show that fusion algorithm reduces blindness at initial stage of obstacle avoidance path searching progress and verifies validity and efficiency of path planning. Moreover, all parameters of vehicle are changed within a reasonable range to meet requirements of steering stability and driving safely during path planning progress.

Sign in / Sign up

Export Citation Format

Share Document