scholarly journals Path Planning Method for UAVs Based on Constrained Polygonal Space and an Extremely Sparse Waypoint Graph

2021 ◽  
Vol 11 (12) ◽  
pp. 5340
Author(s):  
Abdul Majeed ◽  
Seong Oun Hwang
Keyword(s):  
Path Planning ◽  
Computing Time ◽  
Optimal Path ◽  
Three Dimensional ◽  
Search Space ◽  
Urban Environments ◽  
Time Performance ◽  
Probability Spaces ◽  
Obstacle Geometry ◽  
Time Overhead

Finding an optimal/quasi-optimal path for Unmanned Aerial Vehicles (UAVs) utilizing full map information yields time performance degradation in large and complex three-dimensional (3D) urban environments populated by various obstacles. A major portion of the computing time is usually wasted on modeling and exploration of spaces that have a very low possibility of providing optimal/sub-optimal paths. However, computing time can be significantly reduced by searching for paths solely in the spaces that have the highest priority of providing an optimal/sub-optimal path. Many Path Planning (PP) techniques have been proposed, but a majority of the existing techniques equally evaluate many spaces of the maps, including unlikely ones, thereby creating time performance issues. Ignoring high-probability spaces and instead exploring too many spaces on maps while searching for a path yields extensive computing-time overhead. This paper presents a new PP method that finds optimal/quasi-optimal and safe (e.g., collision-free) working paths for UAVs in a 3D urban environment encompassing substantial obstacles. By using Constrained Polygonal Space (CPS) and an Extremely Sparse Waypoint Graph (ESWG) while searching for a path, the proposed PP method significantly lowers pathfinding time complexity without degrading the length of the path by much. We suggest an intelligent method exploiting obstacle geometry information to constrain the search space in a 3D polygon form from which a quasi-optimal flyable path can be found quickly. Furthermore, we perform task modeling with an ESWG using as few nodes and edges from the CPS as possible, and we find an abstract path that is subsequently improved. The results achieved from extensive experiments, and comparison with prior methods certify the efficacy of the proposed method and verify the above assertions.

scholarly journals A Fast Global Flight Path Planning Algorithm Based on Space Circumscription and Sparse Visibility Graph for Unmanned Aerial Vehicle

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 375 ◽  
Author(s):  
Abdul Majeed ◽  
Sungchang Lee
Keyword(s):  
Path Planning ◽  
Computing Time ◽  
Three Dimensional ◽  
Search Space ◽  
Flight Path ◽  
Visibility Graph ◽  
Planning Algorithm ◽  
Path Lengths ◽  
Path Planning Algorithm ◽  
Obstacle Geometry

This paper proposes a new flight path planning algorithm that finds collision-free, optimal/near-optimal and flyable paths for unmanned aerial vehicles (UAVs) in three-dimensional (3D) environments with fixed obstacles. The proposed algorithm significantly reduces pathfinding computing time without significantly degrading path lengths by using space circumscription and a sparse visibility graph in the pathfinding process. We devise a novel method by exploiting the information about obstacle geometry to circumscribe the search space in the form of a half cylinder from which a working path for UAV can be computed without sacrificing the guarantees on near-optimality and speed. Furthermore, we generate a sparse visibility graph from the circumscribed space and find the initial path, which is subsequently optimized. The proposed algorithm effectively resolves the efficiency and optimality trade-off by searching the path only from the high priority circumscribed space of a map. The simulation results obtained from various maps, and comparison with the existing methods show the effectiveness of the proposed algorithm and verify the aforementioned claims.

scholarly journals Time-efficient path planning using two virtual robots

2019 ◽  
Vol 16 (6) ◽  
pp. 172988141988674
Author(s):  
Jonghoek Kim
Keyword(s):  
Path Planning ◽  
Finite Time ◽  
Free Space ◽  
Path Length ◽  
Optimal Path ◽  
Three Dimensional ◽  
Computational Time ◽  
Topological Map ◽  
Planning Algorithms ◽  
Safe Path

This article introduces time-efficient path planning algorithms handling both path length and safety within a reasonable computational time. The path is planned considering the robot’s size so that as the robot traverses the constructed path, it doesn’t collide with an obstacle boundary. This article introduces two virtual robots deploying virtual nodes which discretize the obstacle-free space into a topological map. Using the topological map, the planner generates a safe and near-optimal path within a reasonable computational time. It is proved that our planner finds a safe path to the goal in finite time. Using MATLAB simulations, we verify the effectiveness of our path planning algorithms by comparing it with the rapidly-exploring random tree (RRT)-star algorithm in three-dimensional environments.

scholarly journals Stochastic C-GNet Environment Modeling and Path Planning Optimization in a Narrow and Long Space

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Jianjian Yang ◽  
Zhiwei Tang ◽  
Xiaolin Wang ◽  
Zirui Wang ◽  
Biaojun Yin ◽  
...  
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Three Dimensional ◽  
Test Model ◽  
Environment Modeling ◽  
Grid Network ◽  
Planning Optimization ◽  
Elevation Data ◽  
Dynamic Stochastic ◽  
Terrain Elevation

This study proposes a novel method of optimal path planning in stochastic constraint network scenarios. We present a dynamic stochastic grid network model containing semienclosed narrow and long constraint information according to the unstructured environment of an underground or mine tunnel. This novel environment modeling (stochastic constraint grid network) computes the most likely global path in terms of a defined minimum traffic cost for a roadheader in such unstructured environments. Designing high-dimensional constraint vector and traffic cost in nodes and arcs based on two- and three-dimensional terrain elevation data in a grid network, this study considers the walking and space constraints of a roadheader to construct the network topology for the traffic cost value weights. The improved algorithm of variation self-adapting particle swarm optimization is proposed to optimize the regional path. The experimental results both in the simulation and in the actual test model settings illustrate the performance of the described approach, where a hybrid, centralized-distributed modeling method with path planning capabilities is used.

scholarly journals UCAV Path Planning by Fitness-Scaling Adaptive Chaotic Particle Swarm Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Yudong Zhang ◽  
Lenan Wu ◽  
Shuihua Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Optimal Path ◽  
Particle Swarm ◽  
Search Space ◽  
Swarm Optimization ◽  
Adaptive Parameter ◽  
Bee Colony ◽  
Minimum Probability ◽  
Chaotic Particle Swarm Optimization

Path planning plays an extremely important role in the design of UCAVs to accomplish the air combat task fleetly and reliably. The planned path should ensure that UCAVs reach the destination along the optimal path with minimum probability of being found and minimal consumed fuel. Traditional methods tend to find local best solutions due to the large search space. In this paper, a Fitness-scaling Adaptive Chaotic Particle Swarm Optimization (FAC-PSO) approach was proposed as a fast and robust approach for the task of path planning of UCAVs. The FAC-PSO employed the fitness-scaling method, the adaptive parameter mechanism, and the chaotic theory. Experiments show that the FAC-PSO is more robust and costs less time than elite genetic algorithm with migration, simulated annealing, and chaotic artificial bee colony. Moreover, the FAC-PSO performs well on the application of dynamic path planning when the threats cruise randomly and on the application of 3D path planning.

UAV Path Planning Based on Particle Swarm Optimization with Global Best Path Competition

2018 ◽  
Vol 32 (06) ◽  
pp. 1859008 ◽  
Author(s):  
Chen Huang ◽  
Jiyou Fei
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Optimal Path ◽  
Three Dimensional ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Competition Strategy ◽  
Swarm Optimization ◽  
Evaluation Functions ◽  
Planning Methods

Path planning is the essential aspect of autonomous flight system for unmanned aerial vehicles (UAVs). An improved particle swarm optimization (PSO) algorithm, named GBPSO, is proposed to enhance the performance of three-dimensional path planning for fixed-wing UAVs in this paper. In order to improve the convergence speed and the search ability of the particles, the competition strategy is introduced into the standard PSO to optimize the global best solution during the process of particle evolution. More specifically, according to a set of segment evaluation functions, the optimal path found by single waypoint selection way is adopted as one of the candidate global best paths. Meanwhile, based on the particle as an integrated individual, an optimal trajectory from the start point to the flight target is generated as another global best candidate path. Subsequently, the global best path is determined by considering the pre-specified elevation function values of two candidate paths. Finally, to verify the performance of the proposed method, GBPSO is compared with some existing path-planning methods in two simulation scenarios with different obstacles. The results demonstrate that GBPSO is more effective, robust and feasible for UAV path planning.

scholarly journals Path Planning for Indoor UAV Using A* and Late Acceptance Hill Climbing Algorithms Utilizing Probabilistic Roadmap

2020 ◽  
Vol 9 (4) ◽  
pp. 857
Author(s):  
Jacob Hopkins ◽  
Forrest Joy ◽  
Alaa Sheta ◽  
Hamza Turabieh ◽  
Dulal Kar
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Real Life ◽  
Search Space ◽  
Hill Climbing ◽  
Specific Criterion ◽  
Indoor Space ◽  
Probabilistic Roadmap ◽  
Package Delivery ◽  
Late Acceptance Hill Climbing

The main objective of an unmanned aerial vehicle (UAV) path planning is to generate a flight path that links a start point to an endpoint in an indoor space avoiding obstacles.  Path planning is essential for many real-life applications such as an autonomous car, surveillance mission, farming robots, unmanned aerial vehicles package delivery, space exploration, and many others. To create an optimal path, we need to adopt a specific criterion to minimize the distance the UAV must travel such as the Euclidean distance. In this paper, we provide our initial idea of creating an optimal path for indoor UAV using both A* and the Late Acceptance Hill Climbing (LAHC) algorithms. We are adopting an indoor search environment with various complexity and utilize the Probabilistic Roadmap algorithm (PRM) as a search space for both algorithms. The basic idea following PRM is to generate random sample points in the space and search these points for an optimal path. The developed results show that the LAHC algorithm outperforms the A* algorithm.

Research on Vehicle Path Planning Based on the BDD in the Uncertain Environment

2013 ◽  
Vol 373-375 ◽  
pp. 1144-1149
Author(s):  
Ya Li Peng ◽  
Jia Yao Liu ◽  
Hong Yin
Keyword(s):  
Path Planning ◽  
Heuristic Search ◽  
High Efficiency ◽  
Road Traffic ◽  
Optimal Path ◽  
Search Space ◽  
Routing Problem ◽  
Uncertain Environment ◽  
The Road ◽  
Global Planning

Aimed at the high dynamics and uncertainty of road traffic, we propose a method combine BDD (binary decision diagram)-Based heuristic algorithm which used to do the initial path planning with BDD-Based incremental to solve the route replanning problem. In order to get the optimal path set, BDD-Based heuristic Search is firstly used for global planning. BDD is a compact data structure, the BDD-Based heuristic Search use this characteristic to represent state space and compress the search space through heuristic information at the same time; when the road network information changes, incremental replanning was used in difference type of congestion and the optimum path set again. The simulation results show that the BDD-Based heuristic Search and incremental replanning method has high efficiency and practicability in solving vehicle routing problem under dynamic and uncertain environment.

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