Path Planning for Underwater Glider Based on Ant Colony Algorithm Guided by Artificial Potential Field

2021 ◽  
Author(s):  
Xiantao Jin ◽  
Chuangxia Huang ◽  
Changchun Bao
Keyword(s):  
Path Planning ◽  
Potential Field ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Artificial Potential Field ◽  
Underwater Glider

An improved ant colony algorithm based on artificial potential field and quantum evolution theory

2021 ◽  
pp. 1-16
Author(s):  
Longzhen Zhai ◽  
Shaohong Feng
Keyword(s):  
Path Planning ◽  
Potential Field ◽  
Ant Colony Algorithm ◽  
Emergency Evacuation ◽  
Ant Colony ◽  
Quantum Evolution ◽  
Artificial Potential Field ◽  
Evolution Theory ◽  
Field Force ◽  
Improved Ant Colony Algorithm

The optimal evacuation route in emergency evacuation can further reduce casualties. Therefore, path planning is of great significance to emergency evacuation. Aiming at the blindness and relatively slow convergence speed of ant colony algorithm path planning search, an improved ant colony algorithm is proposed by combining artificial potential field and quantum evolution theory. On the one hand, the evacuation environment of pedestrians is modeled by the grid method. Use the potential field force in the artificial potential field, the influence coefficient of the potential field force heuristic information, and the distance between the person and the target position in the ant colony algorithm to construct comprehensive heuristic information. On the other hand, the introduction of quantum evolutionary theory. The pheromone is represented by quantum bits, and the pheromone is updated by quantum revolving door feedback control. In this way, it can not only reflect the high efficiency of quantum parallel computing, but also have the better optimization ability of ant colony algorithm. A large number of simulation experiments show that the improved ant colony algorithm has a faster convergence rate and is more effective in evacuation path planning.

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.

scholarly journals Path Planning for Construction Machinery Based on Improved Potential Field Ant Colony Algorithm

2021 ◽  
Vol 2095 (1) ◽  
pp. 012062
Author(s):  
Peigang Li ◽  
Pengcheng Li ◽  
Yining Xie ◽  
Xianying Feng ◽  
Bin Hu ◽  
...  
Keyword(s):  
Path Planning ◽  
Potential Field ◽  
Ant Colony Algorithm ◽  
Weight Coefficient ◽  
Field Method ◽  
Ant Colony ◽  
Construction Machinery ◽  
Field Force ◽  
Heuristic Information ◽  
Planning Algorithm

Abstract The path planning algorithm of unmanned construction machinery is studied, and the potential field ant colony algorithm is improved to be applied in the field of unmanned construction machinery. Firstly, the raster map modeling was optimized to eliminate the trap grid in the map. At the beginning of algorithm iteration, the heuristic information of artificial potential field method was added and the global pheromone updating model was improve the convergence speed of the algorithm. In addition, the weight coefficient of potential field force and local pheromone updating model were introduced to enhance the development of raster map in the later iteration of ant colony algorithm and reduce the influence of heuristic information of potential field force. Finally, the selection range of parameters such as optimal pheromone heuristic factor and ant colony number is determined by simulation, and it is verified that the algorithm is better than the basic ant colony algorithm.

scholarly journals Mobile Robot Path Planning Using Ant Colony Algorithm and Improved Potential Field Method

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Guoliang Chen ◽  
Jie Liu
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Potential Field ◽  
Ant Colony Algorithm ◽  
Field Method ◽  
Resultant Force ◽  
Ant Colony ◽  
Hybrid Strategy ◽  
Two Phases ◽  
Robot Path

For the problem of mobile robot’s path planning under the known environment, a path planning method of mixed artificial potential field (APF) and ant colony optimization (ACO) based on grid map is proposed. First, based on the grid model, APF is improved in three ways: the attraction field, the direction of resultant force, and jumping out the infinite loop. Then, the hybrid strategy combined global updating with local updating is developed to design updating method of the ACO pheromone. The process of optimization of ACO is divided into two phases. In the prophase, the direction of the resultant force obtained by the improved APF is used as the inspired factors, which leads ant colony to move in a directional manner. In the anaphase, the inspired factors are canceled, and ant colony transition is completely based on pheromone updating, which can overcome the inertia of the ant colony and force them to explore a new and better path. Finally, some simulation experiments and mobile robot environment experiments are done. The experiment results verify that the method has stronger stability and environmental adaptability.

scholarly journals A Study on Path Planning Algorithms of UAV Collision Avoidance

2019 ◽  
Vol 37 (1) ◽  
pp. 100-106
Author(s):  
Zhao Xu ◽  
Jinwen Hu ◽  
Yunhong Ma ◽  
Man Wang ◽  
Chunhui Zhao
Keyword(s):  
Path Planning ◽  
Collision Avoidance ◽  
Potential Field ◽  
Ant Colony Algorithm ◽  
Artificial Potential Field ◽  
Fuzzy Logic Algorithm ◽  
Aerial Vehicle ◽  
Simulation Results ◽  
Planning Algorithms ◽  
Dynamic Obstacle

The unmanned aerial vehicle (UAV) has been a research hotspot worldwide. The UAV system is developing to be more and more intelligent and autonomous. UAV path planning is an important part of UAV autonomous control and the important guarantee of UAV's safety. For the purpose of improving the collision avoidance and path planning algorithms, the artificial potential field, fuzzy logic algorithm and ant colony algorithm are simulated respectively in the static obstacle and dynamic obstacle environments, and compared based on the minimum avoidance distance and range ratio. Meanwhile, an improved algorithm of artificial potential field is proposed, and the improvement helps the UAV escape the local minimum by introducing the vertical guidance repulsion. The simulation results are rigorous and reliable, which lay a foundation for the further fusion of multiple algorithms and improving the path planning algorithms.

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