scholarly journals UAV Path Planning Based on Multi-Stage Constraint Optimization

Drones ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 144
Author(s):  
Yong Shen ◽  
Yunlou Zhu ◽  
Hongwei Kang ◽  
Xingping Sun ◽  
Qingyi Chen ◽  
...  
Keyword(s):  
Path Planning ◽  
Three Dimensional ◽  
Evolutionary Process ◽  
Constraint Processing ◽  
Multi Stage ◽  
Planning Ability ◽  
Path Constraints ◽  
Aerial Vehicle ◽  
Multi Level ◽  
Processing Mechanism

Evolutionary Algorithms (EAs) based Unmanned Aerial Vehicle (UAV) path planners have been extensively studied for their effectiveness and high concurrency. However, when there are many obstacles, the path can easily violate constraints during the evolutionary process. Even if a single waypoint causes a few constraint violations, the algorithm will discard these solutions. In this paper, path planning is constructed as a multi-objective optimization problem with constraints in a three-dimensional terrain scenario. To solve this problem in an effective way, this paper proposes an evolutionary algorithm based on multi-level constraint processing (ANSGA-III-PPS) to plan the shortest collision-free flight path of a gliding UAV. The proposed algorithm uses an adaptive constraint processing mechanism to improve different path constraints in a three-dimensional environment and uses an improved adaptive non-dominated sorting genetic algorithm (third edition—ANSGA-III) to enhance the algorithm’s path planning ability in a complex environment. The experimental results show that compared with the other four algorithms, ANSGA-III-PPS achieves the best solution performance. This not only validates the effect of the proposed algorithm, but also enriches and improves the research results of UAV path planning.

Three-dimensional path planning for unmanned aerial vehicle based on linear programming

Robotica ◽  
2011 ◽  
Vol 30 (5) ◽  
pp. 773-781 ◽  
Author(s):  
Yang Chen ◽  
Jianda Han ◽  
Xingang Zhao
Keyword(s):  
Linear Programming ◽  
Path Planning ◽  
Real Time ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Linear Constraints ◽  
Flying Height ◽  
Ant Colony Optimization Algorithm ◽  
Optimal Velocity ◽  
Aerial Vehicle

SUMMARYIn this paper, an approach based on linear programming (LP) is proposed for path planning in three-dimensional space, in which an aerial vehicle is requested to pursue a target while avoiding static or dynamic obstacles. This problem is very meaningful for many aerial robots, such as unmanned aerial vehicles. First, the tasks of target-pursuit and obstacle-avoidance are modelled with linear constraints in relative coordination according to LP formulation. Then, two weighted cost functions, representing the optimal velocity resolution, are integrated into the final objective function. This resolution, defined to achieve the optimal velocity, deals with the optimization of a pair of orthogonal vectors. Some constraints, such as boundaries of the vehicle velocity, acceleration, sensor range, and flying height, are considered in this method. A number of simulations, under static and dynamic environments, are carried out to validate the performance of generating optimal trajectory in real time. Compared with ant colony optimization algorithm and genetic algorithm, our method has less parameters to tune and can achieve better performance in real-time application.

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.

A rapid method for planning paths in three dimensions for a small aerial robot

Robotica ◽  
2001 ◽  
Vol 19 (2) ◽  
pp. 125-135 ◽  
Author(s):  
M. Williams ◽  
D.I. Jones
Keyword(s):  
Path Planning ◽  
Computational Algorithm ◽  
Three Dimensional ◽  
Three Dimensions ◽  
Computer Memory ◽  
Test Results ◽  
Effective Operation ◽  
Execution Speed ◽  
Aerial Vehicle ◽  
Electricity Power

This paper describes a path planning method for a small autonomous aerial vehicle to be used for inspecting overhead electricity power lines. A computational algorithm is described which converts a standard three dimensional array representation of one or more obstacles in the vehicle's environment into an octree and a connectivity graph. This achieves a significant reduction in computer memory usage and an increase in execution speed when the graph is searched. Path planning is based on a three-dimensional extension of the distance transform. Test results demonstrate rapid and effective operation of the planner within different workspaces.

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.

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 A minimum-time obstacle-avoidance path planning algorithm for unmanned aerial vehicles

2021 ◽  
Author(s):  
Arturo De Marinis ◽  
Felice Iavernaro ◽  
Francesca Mazzia
Keyword(s):  
Path Planning ◽  
Minimum Principle ◽  
Three Dimensional ◽  
Planning Problem ◽  
Pontryagin Minimum Principle ◽  
New Strategy ◽  
Planning Algorithm ◽  
Aerial Vehicle ◽  
Vehicle Trajectory ◽  
Path Planning Algorithm

AbstractIn this article, we present a new strategy to determine an unmanned aerial vehicle trajectory that minimizes its flight time in presence of avoidance areas and obstacles. The method combines classical results from optimal control theory, i.e. the Euler-Lagrange Theorem and the Pontryagin Minimum Principle, with a continuation technique that dynamically adapts the solution curve to the presence of obstacles. We initially consider the two-dimensional path planning problem and then move to the three-dimensional one, and include numerical illustrations for both cases to show the efficiency of our approach.

scholarly journals A Hybrid Differential Symbiotic Organisms Search Algorithm for UAV Path Planning

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3037
Author(s):  
Lisu Huo ◽  
Jianghan Zhu ◽  
Zhimeng Li ◽  
Manhao Ma
Keyword(s):  
Path Planning ◽  
Search Algorithm ◽  
Three Dimensional ◽  
Simulation Experiments ◽  
Symbiotic Organisms Search ◽  
Mutation Strategy ◽  
Aerial Vehicle ◽  
Symbiotic Organism Search ◽  
Symbiotic Organisms ◽  
Search Capability

Unmanned aerial vehicle (UAV) path planning is crucial in UAV mission fulfillment, with the aim of finding a satisfactory path within affordable time and moderate computation resources. The problem is challenging due to the complexity of the flight environment, especially in three-dimensional scenarios with obstacles. To solve the problem, a hybrid differential symbiotic organisms search (HDSOS) algorithm is proposed by combining the mutation strategy of differential evolution (DE) with the modified strategies of symbiotic organism search (SOS). The proposed algorithm preserves the local search capability of SOS, and at the same time has impressive global search ability. The concept of traction function is put forward and used to improve the efficiency. Moreover, a perturbation strategy is adopted to further enhance the robustness of the algorithm. Extensive simulation experiments and comparative study in two-dimensional and three-dimensional scenarios show the superiority of the proposed algorithm compared with particle swarm optimization (PSO), DE, and SOS algorithm.

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