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.

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 Efficient Path Planning and Truthful Incentive Mechanism Design for Mobile Crowdsensing

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4408 ◽  
Author(s):  
Xi Tao ◽  
Wei Song
Keyword(s):  
Path Planning ◽  
Mechanism Design ◽  
Heuristic Algorithm ◽  
Incentive Mechanism ◽  
Optimal Solution ◽  
Planning Problem ◽  
Vcg Mechanism ◽  
The Incentive Mechanism ◽  
Incentive Mechanism Design ◽  
Path Planning Problem

Mobile crowdsensing (MCS) is a promising paradigm for large-scale sensing. A group of users are recruited as workers to accomplish various sensing tasks and provide data to the platform and requesters. A key problem in MCS is to design the incentive mechanism, which can attract enough workers to participate in sensing activities and maintain the truthfulness. As the main advantage of MCS, user mobility is a factor that must be considered. We make an attempt to build a technical framework for MCS, which is associated with a truthful incentive mechanism taking the movements of numerous workers into account. Our proposed framework contains two challenging problems: path planning and incentive mechanism design. In the path planning problem, every worker independently plans a tour to carry out the posted tasks according to its own strategy. A heuristic algorithm is proposed for the path planning problem, which is compared with two baseline algorithms and the optimal solution. In the incentive mechanism design, the platform develops a truthful mechanism to select the winners and determine their payments. The proposed mechanism is proved to be computationally efficient, individually rational, and truthful. In order to evaluate the performance of our proposed mechanism, the well-known Vickrey–Clarke–Groves (VCG) mechanism is considered as a baseline. Simulations are conducted to evaluate the performance of our proposed framework. The results show that the proposed heuristic algorithm for the path planning problem outperforms the baseline algorithms and approaches the optimal solution. Meanwhile, the proposed mechanism holds a smaller total payment compared with the VCG mechanism when both mechanisms achieve the same performance. Finally, the utility of a selected winner shows the truthfulness of proposed mechanism by changing its bid.

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.

Solving the multi-objective path planning problem in mobile robotics with a firefly-based approach

2015 ◽  
Vol 21 (4) ◽  
pp. 949-964 ◽  
Author(s):  
Alejandro Hidalgo-Paniagua ◽  
Miguel A. Vega-Rodríguez ◽  
Joaquín Ferruz ◽  
Nieves Pavón
Keyword(s):  
Path Planning ◽  
Mobile Robotics ◽  
Planning Problem ◽  
Multi Objective ◽  
Path Planning Problem

Collision-Free Motion Planning for an Aligned Multiple-turret System Operating in Extreme Environment

Robotica ◽  
2021 ◽  
pp. 1-30
Author(s):  
Ümit Yerlikaya ◽  
R.Tuna Balkan
Keyword(s):  
Path Planning ◽  
Extreme Environment ◽  
Configuration Spaces ◽  
Planning Problem ◽  
Different Types ◽  
Planning Algorithm ◽  
Line Path ◽  
Path Planning Algorithm ◽  
Path Planning Problem ◽  
System Operating

Abstract Instead of using the tedious process of manual positioning, an off-line path planning algorithm has been developed for military turrets to improve their accuracy and efficiency. In the scope of this research, an algorithm is proposed to search a path in three different types of configuration spaces which are rectangular-, circular-, and torus-shaped by providing three converging options named as fast, medium, and optimum depending on the application. With the help of the proposed algorithm, 4-dimensional (D) path planning problem was realized as 2-D + 2-D by using six sequences and their options. The results obtained were simulated and no collision was observed between any bodies in these three options.

Path Planning for Spheres in Three Dimensional Environments With Low Interference Index

1994 ◽  
Author(s):  
Duane W. Storti ◽  
Debasish Dutta
Keyword(s):  
Path Planning ◽  
Free Path ◽  
Configuration Space ◽  
Local Knowledge ◽  
Three Dimensional ◽  
Target Point ◽  
Planning Problem ◽  
Spherical Object ◽  
Starting Point ◽  
Path Planning Problem

Abstract We consider the path planning problem for a spherical object moving through a three-dimensional environment composed of spherical obstacles. Given a starting point and a terminal or target point, we wish to determine a collision free path from start to target for the moving sphere. We define an interference index to count the number of configuration space obstacles whose surfaces interfere simultaneously. In this paper, we present algorithms for navigating the sphere when the interference index is ≤ 2. While a global calculation is necessary to characterize the environment as a whole, only local knowledge is needed for path construction.

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