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 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.

Three-Dimensional Shortest Path Planning in the Presence of Polyhedral Obstacles

1996 ◽  
Vol 210 (4) ◽  
pp. 373-381 ◽  
Author(s):  
K Jiang ◽  
L D Seneviratne ◽  
S W E Earles
Keyword(s):  
Computational Complexity ◽  
Path Planning ◽  
Shortest Path ◽  
Turning Points ◽  
Three Dimensional ◽  
Two Dimensions ◽  
Visibility Graph ◽  
Three Dimensions ◽  
View Point ◽  
Polyhedral Obstacles

A new algorithm is presented for solving the three-dimensional shortest path planning (3DSP) problem for a point object moving among convex polyhedral obstacles. It is the first non-approximate three-dimensional path planing algorithm that can deal with more than two polyhedral obstacles. The algorithm extends the visibility graph concept from two dimensions to three dimensions. The two main problems with 3DSP are identifying the edge sequence the shortest path passes through and the turning points of the shortest path. A technique based on projective relationships is presented for identifying the set of visible boundary edges (VBE) corresponding to a given view point over which the shortest path, from the view point to the goal, will pass. VBE are used to construct an initial reduced visibility graph (RVG). Optimization is used to revise the position of the turning points and hence the three-dimensional RVG (3DRVG) and the global shortest path is then selected from the 3DRVG. The algorithm is of computational complexity O(n3vk), where n is the number of verticles, v is the maximum number of vertices on any one obstacle and k is the number of obstacles. The algorithm is applicable only with polyhedral obstacles, as the theorems developed for searching for the turning points of the three-dimensional shortest path are based on straight edges of the obstacles. It needs to be further developed for dealing with arbitrary-shaped obstacles and this would increase the computational complexity. The algorithm is tested using computer simulations and some results are presented.

Algorithmic path planning of static robots in three dimensions using configuration space metrics

Robotica ◽  
2010 ◽  
Vol 29 (2) ◽  
pp. 295-315 ◽  
Author(s):  
Debanik Roy
Keyword(s):  
Path Planning ◽  
Configuration Space ◽  
Heuristic Algorithm ◽  
Degrees Of Freedom ◽  
Three Dimensional ◽  
Industrial Applications ◽  
Three Dimensions ◽  
Task Space ◽  
Articulated Robot

SUMMARYCollision-free path planning for static robots is a demanding manifold of contemporary robotics research, vastly due to the growing industrial applications. In this paper, a novel ‘visibility map’-based heuristic algorithm is used to generate near-optimal safe path for a three-dimensional congested robot workspace. The final path is obtainable in terms of joint configurations, by considering the Configuration Space of the task space. The developed algorithm has been verified initially by considering representative 2D workspaces, cluttered with different obstacles with regular geometries and then after with the spatial endeavour. A case study reveals the effectiveness of the developed modules of the configuration space mapping, pertaining to a five degrees-of-freedom low payload articulated robot.

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
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