scholarly journals Three-Dimensional Path Planning of Constant Thrust Unmanned Aerial Vehicle Based on Artificial Fluid Method

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yongqiang Qi ◽  
Shuai Li ◽  
Yi Ke
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicle ◽  
Interpolation Method ◽  
Three Dimensional ◽  
Planning Problem ◽  
Perturbation Matrix ◽  
Fluid Field ◽  
Control Scheme ◽  
Aerial Vehicle ◽  
Tangential Vector

In this paper, a three-dimensional path planning problem of an unmanned aerial vehicle under constant thrust is studied based on the artificial fluid method. The effect of obstacles on the original fluid field is quantified by the perturbation matrix, the streamlines can be regarded as the planned path for the unmanned aerial vehicle, and the tangential vector and the disturbance matrix of the artificial fluid method are improved. In particular, this paper addresses a novel algorithm of constant thrust fitting which is proposed through the impulse compensation, and then the constant thrust switching control scheme based on the isochronous interpolation method is given. It is proved that the planned path can avoid all obstacles smoothly and swiftly and reach the destination eventually. Simulation results demonstrate the effectiveness of this method.

scholarly journals Fast Path Planning for On-Water Automatic Rescue Intelligent Robot Based on Constant Thrust Artificial Fluid Method

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yongqiang Qi ◽  
Yi Ke
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Interpolation Method ◽  
Three Dimensional ◽  
Intelligent Robot ◽  
Perturbation Matrix ◽  
Fluid Field ◽  
Environment Model ◽  
Simulation Results ◽  
Novel Algorithm

In this paper, fast path planning of on-water automatic rescue intelligent robot is studied based on the constant thrust artificial fluid method. First, a three-dimensional environment model is established, and then the kinematics equation of the robot is given. The constant thrust artificial fluid method based on the isochronous interpolation method is proposed, and a novel algorithm of constant thrust fitting is researched through the impulse compensation. The effect of obstacles on original fluid field is quantified by the perturbation matrix, and the streamlines can be regarded as the planned path. Simulation results demonstrate the effectiveness of this method by comparing with A-star algorithm and ant colony algorithm. It is proved that the planned path can avoid all obstacles smoothly and swiftly and reach the destination eventually.

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.

scholarly journals Improved Particle Swarm Optimization of Three-Dimensional Path Planning for Fixed Wing Unmanned Aerial Vehicle

2019 ◽  
Vol 35 (2) ◽  
Author(s):  
Giang Thi - Huong Dang ◽  
Quang - Huy Vuong ◽  
Minh Hoang Ha ◽  
Minh - Trien Pham
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Unmanned Aerial Vehicle ◽  
Particle Swarm ◽  
Adaptive Mutation ◽  
Planning Problem ◽  
Swarm Optimization ◽  
3D Environment ◽  
Aerial Vehicle ◽  
Improved Pso

Path planning for Unmanned Aerial Vehicle (UAV) targets at generating an optimal global path to the target, avoiding collisions and optimizing the given cost function under constraints. In this paper, the path planning problem for UAV in pre-known 3D environment is presented. Particle Swarm Optimization (PSO) was proved the efficiency for various problems. PSO has high convergence speed yet with its major drawback of premature convergence when solving large-scale optimization problems. In this paper, the improved PSO with adaptive mutation to overcome its drawback in order to applied PSO the UAV path planning in real 3D environment which composed of mountains and constraints. The effectiveness of the proposed PSO algorithm is compared to Genetic Algorithm, standard PSO and other improved PSO using 3D map of Daklak, Dakrong and Langco Beach. The results have shown the potential for applying proposed algorithm in optimizing the 3D UAV path planning. Keywords: UAV, Path planning, PSO, Optimization.

scholarly journals Three-dimensional path planning for unmanned aerial vehicle based on interfered fluid dynamical system

2015 ◽  
Vol 28 (1) ◽  
pp. 229-239 ◽  
Author(s):  
Honglun Wang ◽  
Wentao Lyu ◽  
Peng Yao ◽  
Xiao Liang ◽  
Chang Liu
Keyword(s):  
Dynamical System ◽  
Path Planning ◽  
Unmanned Aerial Vehicle ◽  
Three Dimensional ◽  
Aerial Vehicle

scholarly journals Heuristic Q-learning based on experience replay for three-dimensional path planning of the unmanned aerial vehicle

2019 ◽  
Vol 103 (1) ◽  
pp. 003685041987902 ◽  
Author(s):  
Ronglei Xie ◽  
Zhijun Meng ◽  
Yaoming Zhou ◽  
Yunpeng Ma ◽  
Zhe Wu
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Unmanned Aerial Vehicle ◽  
Learning Algorithm ◽  
Three Dimensional ◽  
Convergence Speed ◽  
Average Reward ◽  
Heuristic Function ◽  
Experience Replay ◽  
Aerial Vehicle

In order to solve the problem that the existing reinforcement learning algorithm is difficult to converge due to the excessive state space of the three-dimensional path planning of the unmanned aerial vehicle, this article proposes a reinforcement learning algorithm based on the heuristic function and the maximum average reward value of the experience replay mechanism. The knowledge of track performance is introduced to construct heuristic function to guide the unmanned aerial vehicles’ action selection and reduce the useless exploration. Experience replay mechanism based on maximum average reward increases the utilization rate of excellent samples and the convergence speed of the algorithm. The simulation results show that the proposed three-dimensional path planning algorithm has good learning efficiency, and the convergence speed and training performance are significantly improved.

scholarly journals An Improved Equilibrium Optimizer with Application in Unmanned Aerial Vehicle Path Planning

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1814
Author(s):  
An-Di Tang ◽  
Tong Han ◽  
Huan Zhou ◽  
Lei Xie
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicle ◽  
Optimization Problem ◽  
Fitness Function ◽  
Population Diversity ◽  
Planning Problem ◽  
Turn Angle ◽  
Set Constraints ◽  
Planning Algorithm ◽  
Aerial Vehicle

The unmanned aerial vehicle (UAV) path planning problem is a type of complex multi-constraint optimization problem that requires a reasonable mathematical model and an efficient path planning algorithm. In this paper, the fitness function including fuel consumption cost, altitude cost, and threat cost is established. There are also four set constraints including maximum flight distance, minimum flight altitude, maximum turn angle, and maximum climb angle. The constrained optimization problem is transformed into an unconstrained optimization problem by using the penalty function introduced. To solve the model, a multiple population hybrid equilibrium optimizer (MHEO) is proposed. Firstly, the population is divided into three subpopulations based on fitness and different strategies are executed separately. Secondly, a Gaussian distribution estimation strategy is introduced to enhance the performance of MHEO by using the dominant information of the populations to guide the population evolution. The equilibrium pool is adjusted to enhance population diversity. Furthermore, the Lévy flight strategy and the inferior solution shift strategy are used to help the algorithm get rid of stagnation. The CEC2017 test suite was used to evaluate the performance of MHEO, and the results show that MHEO has a faster convergence speed and better convergence accuracy compared to the comparison algorithms. The path planning simulation experiments show that MHEO can steadily and efficiently plan flight paths that satisfy the constraints, proving the superiority of the MHEO algorithm while verifying the feasibility of the path planning model.

Heterogeneous multi-unmanned aerial vehicle task planning: Simultaneous attacks on targets using the Pythagorean hodograph curve

2019 ◽  
Vol 233 (13) ◽  
pp. 4735-4749 ◽  
Author(s):  
Fei Yan ◽  
Xiaoping Zhu ◽  
Zhou Zhou ◽  
Yang Tang
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Unmanned Aerial Vehicle ◽  
Task Allocation ◽  
Planning Problem ◽  
Aerial Vehicles ◽  
Pythagorean Hodograph ◽  
Aerial Vehicle ◽  
Path Lengths ◽  
Target Path

The coupled task allocation and path planning problem for heterogeneous multiple unmanned aerial vehicles performing a search and attack mission involving obstacles and no-fly zones are addressed. The importance of the target is measured using a time-dependent value. A task allocation algorithm is proposed to obtain the maximum system utility. In the system utility function, the reward of the target, path lengths of unmanned aerial vehicles, and number of unmanned aerial vehicles to perform a simultaneous attack are considered. The path length of the unmanned aerial vehicles based on the Pythagorean hodograph curve is calculated, and it serves as the input for the task allocation problem. A resource management method for unmanned aerial vehicles is used, so that the resource consumption of the unmanned aerial vehicles can be balanced. To satisfy the requirement of simultaneous attacks and the unmanned aerial vehicle kinematic constraints in an environment involving obstacles and no-fly zones, a distributed cooperative particle swarm optimization algorithm is developed to generate flyable and safe Pythagorean hodograph curve trajectories for unmanned aerial vehicles to achieve simultaneous arrival. Monte Carlo simulations are conducted to demonstrate the performance of the proposed task allocation and path planning method.

Three-dimensional unmanned aerial vehicle path planning using modified wolf pack search algorithm

2017 ◽  
Vol 266 ◽  
pp. 445-457 ◽  
Author(s):  
Chen YongBo ◽  
Mei YueSong ◽  
Yu JianQiao ◽  
Su XiaoLong ◽  
Xu Nuo
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicle ◽  
Search Algorithm ◽  
Three Dimensional ◽  
Aerial Vehicle ◽  
Vehicle Path

scholarly journals Path Planning for a Solar-Powered UAV Inspecting Mountain Sites for Safety and Rescue

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1968
Author(s):  
Hailong Huang ◽  
Andrey V. Savkin
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicle ◽  
Computer Simulations ◽  
Energy Requirement ◽  
Residual Energy ◽  
Planning Problem ◽  
Aerial Vehicle ◽  
Solar Powered ◽  
Feasible Path ◽  
Path Planning Problem

This paper focuses on the application using a solar-powered unmanned aerial vehicle (UAV) to inspect mountain sites for the purpose of safety and rescue. An inspection path planning problem is formulated, which looks for the path for an UAV to visit a set of sites where people may appear while avoiding collisions with mountains and maintaining positive residual energy. A rapidly exploring random tree (RRT)-based planning method is proposed. This method firstly finds a feasible path that satisfies the residual energy requirement and then shortens the path if there is some abundant residual energy at the end. Computer simulations are conducted to demonstrate the performance of the proposed method.

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