Unmanned Aerial Vehicle Three-dimensional Trajectory Planning Based on Ant Colony Algorithm

2018 ◽  
Author(s):  
Zheng Li ◽  
Ruihua Han
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Trajectory Planning ◽  
Ant Colony Algorithm ◽  
Three Dimensional ◽  
Ant Colony ◽  
Aerial Vehicle

Optimized artificial potential field algorithm to multi-unmanned aerial vehicle coordinated trajectory planning and collision avoidance in three-dimensional environment

2019 ◽  
Vol 233 (16) ◽  
pp. 6032-6043 ◽  
Author(s):  
Jun Tang ◽  
Jiayi Sun ◽  
Cong Lu ◽  
Songyang Lao
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Collision Avoidance ◽  
Unmanned Aerial Vehicle ◽  
Trajectory Planning ◽  
Potential Field ◽  
Three Dimensional ◽  
Artificial Potential Field ◽  
Aerial Vehicles ◽  
Aerial Vehicle ◽  
Vehicle Trajectory

Multi-unmanned aerial vehicle trajectory planning is one of the most complex global optimum problems in multi-unmanned aerial vehicle coordinated control. Results of recent research works on trajectory planning reveal persisting theoretical and practical problems. To mitigate them, this paper proposes a novel optimized artificial potential field algorithm for multi-unmanned aerial vehicle operations in a three-dimensional dynamic space. For all purposes, this study considers the unmanned aerial vehicles and obstacles as spheres and cylinders with negative electricity, respectively, while the targets are considered spheres with positive electricity. However, the conventional artificial potential field algorithm is restricted to a single unmanned aerial vehicle trajectory planning in two-dimensional space and usually fails to ensure collision avoidance. To deal with this challenge, we propose a method with a distance factor and jump strategy to resolve common problems such as unreachable targets and ensure that the unmanned aerial vehicle does not collide into the obstacles. The method takes companion unmanned aerial vehicles as the dynamic obstacles to realize collaborative trajectory planning. Besides, the method solves jitter problems using the dynamic step adjustment method and climb strategy. It is validated in quantitative test simulation models and reasonable results are generated for a three-dimensional simulated urban environment.

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 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 Distributed Path Planning of Unmanned Aerial Vehicle Communication Chain Based on Dual Decomposition

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xiaohua Wei ◽  
Jianliang Xu
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicle ◽  
Voronoi Diagram ◽  
Ant Colony Algorithm ◽  
Original Method ◽  
Path Selection ◽  
Ant Colony ◽  
Dijkstra Algorithm ◽  
Dual Decomposition ◽  
Aerial Vehicle

Limited by the insufficiency of single UAV’s load and flight time capabilities, the multi-UAV (unmanned aerial vehicle) collaboration to improve mission efficiency and expand mission functions has become the focus of current UAV theory and application research. In this paper, the research on UAV global path planning is carried out using the ant colony algorithm, and an indoor UAV path planning model based on the ant colony algorithm is constructed. In order to improve the efficiency of the algorithm, enhance the adaptability and robustness of the algorithm, a distributed path planning algorithm based on the dual decomposition UAV communication chain is proposed. This algorithm improves the basic ant colony algorithm from the aspects of path selection, pheromone update, and rollback strategy in view of the inherent shortcomings of the ant colony algorithm. In order to achieve the best performance of the algorithm, this paper analyzes each parameter in the ant colony algorithm in depth and obtains the optimal combination of parameters. The construction method of the Voronoi diagram was improved, and the method was simulated to verify that the method can obtain a Voronoi diagram path that is safer than the original method under certain time conditions. Through the principle analysis and simulation verification of the Dijkstra algorithm and the dual decomposition ant colony algorithm, it is concluded that the dual decomposition ant colony algorithm is more efficient in pathfinding. Finally, through simulation, it was verified that the dual decomposition ant colony algorithm can plan a safe and reasonable flight path for multiple UAV formation flights in an offline state and achieve offline global obstacle avoidance for multiple UAVs.

scholarly journals Trajectory planning of unmanned aerial vehicle based on the improved biogeography-based optimization algorithm

2021 ◽  
Vol 13 (3) ◽  
pp. 168781402110042
Author(s):  
Zhishuang Xue ◽  
Xiaofang Liu
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Trajectory Planning ◽  
Optimization Algorithm ◽  
Three Dimensional ◽  
Electricity Consumption ◽  
Lyapunov Theory ◽  
Planning Problem ◽  
Reference Trajectory ◽  
Control Scheme ◽  
Aerial Vehicle

This paper is concerned with trajectory planning for unmanned aerial vehicle in a three-dimensional complex workspace. Biogeography-based optimization algorithm is widely used in solving practical problems because of its fewer parameters, fast convergence rate, and good global optimization ability. In this paper, some improvements, modifying migration and mutation operations are made on the biogeography-based optimization algorithm to make it suitable for solving the trajectory planning problem. The optimal trajectory obtained by the improved algorithm can be used to generate the reference trajectory. Then, a control scheme of unmanned aerial vehicle based on the Lyapunov theory and radial basis function neural network is formed to track the reference trajectory. The improved trajectory algorithm generates the shortest trajectory and the time consumption is the lowest. Finally, the designed control scheme makes the unmanned aerial vehicle track the different trajectories quite well, the effectiveness of it can be illustrated by algorithm accuracy and electricity consumption.

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