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.

scholarly journals Constrained Path Planning for Unmanned Aerial Vehicle in 3D Terrain Using Modified Multi-Objective Particle Swarm Optimization

Actuators ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 255
Author(s):  
Shuang Xia ◽  
Xiangyin Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Unmanned Aerial Vehicle ◽  
Gaussian Distribution ◽  
Planning Problem ◽  
Swarm Optimization ◽  
Q Learning ◽  
Multi Objective ◽  
Aerial Vehicle ◽  
Path Planning Problem

This paper considered the constrained unmanned aerial vehicle (UAV) path planning problem as the multi-objective optimization problem, in which both costs and constraints are treated as the objective functions. A novel multi-objective particle swarm optimization algorithm based on the Gaussian distribution and the Q-Learning technique (GMOPSO-QL) is proposed and applied to determine the feasible and optimal path for UAV. In GMOPSO-QL, the Gaussian distribution based updating operator is adopted to generate new particles, and the exploration and exploitation modes are introduced to enhance population diversity and convergence speed, respectively. Moreover, the Q-Learning based mode selection logic is introduced to balance the global search with the local search in the evolution process. Simulation results indicate that our proposed GMOPSO-QL can deal with the constrained UAV path planning problem and is superior to existing optimization algorithms in terms of efficiency and robustness.

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 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 Solving the Path Planning Problem in Mobile Robotics with the Multi-Objective Evolutionary Algorithm

2018 ◽  
Vol 8 (9) ◽  
pp. 1425 ◽  
Author(s):  
Yang Xue ◽  
Jian-Qiao Sun
Keyword(s):  
Path Planning ◽  
Evolutionary Algorithm ◽  
Mobile Robotics ◽  
Good Choice ◽  
Target Point ◽  
Planning Problem ◽  
Multi Objective ◽  
Starting Point ◽  
Feasible Path ◽  
Path Planning Problem

Path planning problems involve finding a feasible path from the starting point to the target point. In mobile robotics, path planning (PP) is one of the most researched subjects at present. Since the path planning problem is an NP-hard problem, it can be solved by multi-objective evolutionary algorithms (MOEAs). In this article, we propose a multi-objective method for solving the path planning problem. It is a population evolutionary algorithm and solves three different objectives (path length, safety, and smoothness) to acquire precise and effective solutions. In addition, five scenarios and another existing method are used to test the proposed algorithm. The results show the advantages of the algorithm. In particular, different quality metrics are used to assess the obtained results. In the end, the research indicates that the proposed multi-objective evolutionary algorithm is a good choice for solving the path planning problem.

Online cooperative path planning for multi-quadrotors in an unknown dynamic environment

2021 ◽  
pp. 095441002110166
Author(s):  
Zhenyue Jia ◽  
Ping Lin ◽  
Jiaolong Liu ◽  
Luyang Liang
Keyword(s):  
Path Planning ◽  
Partial Information ◽  
Three Dimensional ◽  
Dynamic Environment ◽  
Planning Problem ◽  
Planning Strategy ◽  
Coordination Strategy ◽  
Planning Strategies ◽  
Feasible Path ◽  
Path Planning Problem

The online cooperative path planning problem is discussed for multi-quadrotor maneuvering in an unknown dynamic environment. Based on the related basic concepts, typical three-dimensional obstacle models, such as spherical and cubic, and their collision checking criteria are presented in this article. An improved rapidly exploring random tree (RRT) algorithm with goal bias and greed property is proposed based on the heuristic search strategy to overcome the shortcomings of the classical RRT algorithm. Not only are the kinematic constraints of the quadrotor established but the time and space coordination strategy matching with the improved RRT algorithm is also presented in this article. Furthermore, a novel online collision avoidance strategy according to the partial information of the surrounding environment is proposed. On the basis of the above work, a distributed online path planning strategy is proposed to obtain the feasible path for each quadrotor. Numerical simulation results show that the improved RRT algorithm has better search efficiency than the classical RRT algorithm. And the satisfactory path planning and path tracking results prove that the above model and related planning strategies are reasonable and effective.

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.

scholarly journals An Improved Heuristic Algorithm for UCAV Path Planning

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Kun Zhang ◽  
Peipei Liu ◽  
Weiren Kong ◽  
Jie Zou ◽  
Min Liu
Keyword(s):  
Mathematical Model ◽  
Path Planning ◽  
Heuristic Algorithm ◽  
Task Demands ◽  
Planning Problem ◽  
Aerial Vehicle ◽  
Constrained Conditions ◽  
Path Planning Problem ◽  
And Task

The study of unmanned combat aerial vehicle (UCAV) path planning is increasingly important in military and civil field. This paper presents a new mathematical model and an improved heuristic algorithm based on Sparse A⁎ Search (SAS) for UCAV path planning problem. In this paper, flight constrained conditions will be considered to meet the flight restrictions and task demands. With three simulations, the impacts of the model on the algorithms will be investigated, and the effectiveness and the advantages of the model and algorithm will be validated.

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