scholarly journals Improved ant colony algorithm for path planning of fixed wing unmanned aerial vehicle

2022 ◽  
Vol 355 ◽  
pp. 03002
Author(s):  
Hongchao Zhao ◽  
Jianzhong Zhao
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicle ◽  
Ant Colony Algorithm ◽  
Search Time ◽  
Optimal Solution ◽  
Ant Colony ◽  
Local Optimal Solution ◽  
Search Speed ◽  
Aerial Vehicle ◽  
Improved Ant Colony Algorithm

Aiming at the problems of long search time and local optimal solution of ant colony algorithm (ACA) in the path planning of unmanned aerial vehicle (UAV), an improved ant colony algorithm (IACA) was proposed from the aspects of simplicity and effectiveness. The flight performance constraints of fixed wing UAVs were treated as conditions of judging whether the candidate expanded nodes are feasible, thus the feasible nodes’ number was reduced and the search efficiency was effectively raised. In order to overcome the problem of local optimal solution, the pheromone update rule is improved by combining local pheromone update and global pheromone update. The heuristic function was improved by integrating the distance heuristic factor with the safety heuristic factor, and it enhanced the UAV flight safety performance. The transfer probability was improved to increase the IACA search speed. Simulation results show that the proposed IACA possesses stronger global search ability and higher practicability than the former IACA.

scholarly journals AGV optimal path planning based on improved ant colony algorithm

2018 ◽  
Vol 232 ◽  
pp. 03052 ◽  
Author(s):  
Chengwei He ◽  
Jian Mao
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Optimal Solution ◽  
Ant Colony ◽  
Undirected Network ◽  
Optimal Path Planning ◽  
Local Optimal Solution ◽  
Global Optimal ◽  
Improved Ant Colony Algorithm

Using the traditional Ant Colony Algorithm for AGV path planning is easy to fall into the local optimal solution and lacking the capability of obstacle avoidance in the complex storage environment. In this paper, by constructing the MAKLINK undirected network routes and the heuristic function is optimized in the Ant Colony Algorithm, then the AGV path reaches the global optimal path and has the ability to avoid obstacles. According to research, the improved Ant Colony Algorithm proposed in this paper is superior to the traditional Ant Colony Algorithm in terms of convergence speed and the distance of optimal path planning.

scholarly journals Penetration Planning and Design Method of Unmanned Aerial Vehicle Inspired by Biological Swarm Intelligence Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Fengtao Xiang ◽  
Keqin Chen ◽  
Jiongming Su ◽  
Hongfu Liu ◽  
Wanpeng Zhang
Keyword(s):  
Path Planning ◽  
Swarm Intelligence ◽  
Unmanned Aerial Vehicle ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Planning And Design ◽  
Intelligence Algorithm ◽  
Aerial Vehicle ◽  
Swarm Intelligence Algorithm ◽  
Improved Ant Colony Algorithm

Unmanned aerial vehicles (UAVs) are gradually used in logistics transportation. They are forbidden to fly in some airspace. To ensure the safety of UAVs, reasonable path planning and design is one of the key factors. Aiming at the problem of how to improve the success rate of unmanned aerial vehicle (UAV) maneuver penetration, a method of UAV penetration path planning and design is proposed. Ant colony algorithm has strong path planning ability in biological swarm intelligence algorithm. Based on the modeling of UAV planning and threat factors, improved ant colony algorithm is used for UAV penetration path planning and design. It is proposed that the path with the best pheromone content is used as the planning path. Some principles are given for using ant colony algorithm in UAV penetration path planning. By introducing heuristic information into the improved ant colony algorithm, the convergence is completed faster under the same number of iteratives. Compared with classical methods, the total steps reduced by 56% with 50 ant numbers and 200 iterations. 62% fewer steps to complete the first iteration. It is found that the optimal trajectory planned by the improved ant colony algorithm is smoother and the shortest path satisfying the constraints.

scholarly journals Application of an Improved Ant Colony Algorithm to Optimal Path Planning for Automatic Guided Vehicles

2020 ◽  
Author(s):  
weikang zhu ◽  
jicheng liu
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Transition Probability ◽  
Optimal Solution ◽  
Random Selection ◽  
Ant Colony ◽  
Smoothing Factor ◽  
Local Optimal Solution ◽  
Selection Operator ◽  
Improved Ant Colony Algorithm

Abstract The path planning is the key technology of AGV path finding. This paper uses an improved ant colony algorithm to plan the path of an AGV. For avoiding the defects of traditional ant colony algorithm such as low smoothness of route and local optimal solution, the transition probability and pheromone update method are improved. Various actual turning situations are analyzed in the transition probability, the basis for defining the smoothing factor is provided by the Bezier curve, and a random selection operator is formed for updating local pheromone by extracting characteristic information of iterative process. The simulation results in different environments prove that the smoothing factor plays an important role in optimizing the smoothness of the path and the diversity of the constructed solutions, and the random selection operator is effective in solving the contradiction of the local optimal solution and in finding the optimal solution.

A Image Segmentation Method of Improved Ant Colony Algorithm for the Manipulator Self-Recognition Target

2011 ◽  
Vol 135-136 ◽  
pp. 50-55
Author(s):  
Yuan Bin Hou ◽  
Yang Meng ◽  
Jin Bo Mao
Keyword(s):  
Image Segmentation ◽  
Ant Colony Algorithm ◽  
Optimal Solution ◽  
Immune Algorithm ◽  
Ant Colony ◽  
Segmentation Result ◽  
Segmentation Method ◽  
Self Recognition ◽  
Local Optimal Solution ◽  
Improved Ant Colony Algorithm

According to the requirements of efficient image segmentation for the manipulator self-recognition target, a method of image segmentation based on improved ant colony algorithm is proposed in the paper. In order to avoid segmentation errors by local optimal solution and the stagnation of convergence, ant colony algorithm combined with immune algorithm are taken to traversing the whole image, which uses pheromone as standard. Further, immunization selection through vaccination optimizes the heuristic information, then it improves the efficiency of ergodic process, and shortens the time of segmentation effectively. Simulation and experimental of image segmentation result shows that this algorithm can get better effect than generic ant colony algorithm, at the same condition, segmentation time is shortened by 6.8%.

An Improved Ant-Colony Algorithm Based on Cloud Model Theory

2010 ◽  
Vol 143-144 ◽  
pp. 1204-1206
Author(s):  
Xian Min Wei
Keyword(s):  
Model Theory ◽  
Ant Colony Algorithm ◽  
Cloud Model ◽  
Optimal Solution ◽  
Optimal Performance ◽  
Experimental Results ◽  
Ant Colony ◽  
Local Optimal Solution ◽  
Cloud Model Theory ◽  
Improved Ant Colony Algorithm

This paper studies one method of cloud model to effectively limit the using Ant-colony Algorithm into local optimal solution, and experimental results show that this Ant-colony Algorithm can improve the speed of global search and optimal performance significantly.

Optimal Path Selection for Mine Emergency Rescue Based on Improved Ant Colony Algorithm

2010 ◽  
Vol 159 ◽  
pp. 168-171
Author(s):  
Feng Tian ◽  
Zhong Zhao Chen
Keyword(s):  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Optimal Solution ◽  
Lower Solution ◽  
Path Selection ◽  
Ant Colony ◽  
Emergency Rescue ◽  
Local Optimal Solution ◽  
Solution Accuracy ◽  
Improved Ant Colony Algorithm

It is the primary task to ensure the safety of lives and property of underground workers with the increasing amount of coal mining. Under the actual complex environment of coal, all kinds of uncertian factors should be considered except for the distance for the selection of the optimal path to reduce casualties. Aiming at the defect of the lower solution accuracy and tending to fall into local optimal solution of the basic ant colony algorithm(ACA), an improved ant colony algorithm is presented based on the model of ACA. Experiment results show that the new algorithm can get better results.

Research of In-Car Navigation Based on Improved Ant Colony Algorithm

2014 ◽  
Vol 536-537 ◽  
pp. 461-465
Author(s):  
Fang Ding ◽  
Su Zhuo Wu
Keyword(s):  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Optimal Solution ◽  
Convergence Speed ◽  
Ant Colony ◽  
Ant Colony System ◽  
Navigation Systems ◽  
Local Optimal Solution ◽  
Improved Ant Colony Algorithm ◽  
Car Navigation

Determining how to select path efficiently in complex transportation networks was one of the main problems in-car navigation systems. For the drawbacks of slow convergence and easy to fall into local optimal solution of basic ant colony algorithm in solving the optimal path problem, a method of improving the expect-heuristic function is proposed in this paper, which enhances search direction and improves the convergence rate. Meanwhile, with the introduction of a new strategy to update the pheromone on ant colony system, the contradiction that convergence speed brings stagnation is balanced. The results show that the improved ant colony algorithm is easier to get the optimal solution compared with basic ant colony algorithm, and the convergence speed is faster, having a good navigation effect.

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