scholarly journals A Multi-Objective Coverage Path Planning Algorithm for UAVs to Cover Spatially Distributed Regions in Urban Environments

Aerospace ◽  
2021 ◽  
Vol 8 (11) ◽  
pp. 343
Author(s):  
Abdul Majeed ◽  
Seong Oun Hwang
Keyword(s):  
Path Planning ◽  
Low Cost ◽  
Computing Time ◽  
Urban Environments ◽  
Minimum Length ◽  
Multi Objective ◽  
Area Of Interest ◽  
Spatially Distributed ◽  
Planning Algorithm ◽  
Path Planning Algorithm

This paper presents a multi-objective coverage flight path planning algorithm that finds minimum length, collision-free, and flyable paths for unmanned aerial vehicles (UAV) in three-dimensional (3D) urban environments inhabiting multiple obstacles for covering spatially distributed regions. In many practical applications, UAVs are often required to fully cover multiple spatially distributed regions located in the 3D urban environments while avoiding obstacles. This problem is relatively complex since it requires the optimization of both inter (e.g., traveling from one region/city to another) and intra-regional (e.g., within a region/city) paths. To solve this complex problem, we find the traversal order of each area of interest (AOI) in the form of a coarse tour (i.e., graph) with the help of an ant colony optimization (ACO) algorithm by formulating it as a traveling salesman problem (TSP) from the center of each AOI, which is subsequently optimized. The intra-regional path finding problem is solved with the integration of fitting sensors’ footprints sweeps (SFS) and sparse waypoint graphs (SWG) in the AOI. To find a path that covers all accessible points of an AOI, we fit fewer, longest, and smooth SFSs in such a way that most parts of an AOI can be covered with fewer sweeps. Furthermore, the low-cost traversal order of each SFS is computed, and SWG is constructed by connecting the SFSs while respecting the global and local constraints. It finds a global solution (i.e., inter + intra-regional path) without sacrificing the guarantees on computing time, number of turning maneuvers, perfect coverage, path overlapping, and path length. The results obtained from various representative scenarios show that proposed algorithm is able to compute low-cost coverage paths for UAV navigation in urban environments.

scholarly journals A New Coverage Flight Path Planning Algorithm Based on Footprint Sweep Fitting for Unmanned Aerial Vehicle Navigation in Urban Environments

2019 ◽  
Vol 9 (7) ◽  
pp. 1470 ◽  
Author(s):  
Abdul Majeed ◽  
Sungchang Lee
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicle ◽  
Travelling Salesman Problem ◽  
Urban Environments ◽  
Flight Path ◽  
Computational Time ◽  
Minimum Length ◽  
Planning Algorithm ◽  
Aerial Vehicle ◽  
Path Planning Algorithm

This paper presents a new coverage flight path planning algorithm that finds collision-free, minimum length and flyable paths for unmanned aerial vehicle (UAV) navigation in three-dimensional (3D) urban environments with fixed obstacles for coverage missions. The proposed algorithm significantly reduces computational time, number of turns, and path overlapping while finding a path that passes over all reachable points of an area or volume of interest by using sensor footprints’ sweeps fitting and a sparse waypoint graph in the pathfinding process. We devise a novel footprints’ sweep fitting method considering UAV sensor footprint as coverage unit in the free spaces to achieve maximal coverage with fewer and longer footprints’ sweeps. After footprints’ sweeps fitting, the proposed algorithm determines the visiting sequence of footprints’ sweeps by formulating it as travelling salesman problem (TSP), and ant colony optimization (ACO) algorithm is employed to solve the TSP. Furthermore, we generate a sparse waypoint graph by connecting footprints’ sweeps’ endpoints to obtain a complete coverage flight path. The simulation results obtained from various scenarios fortify the effectiveness of the proposed algorithm and verify the aforementioned claims.

Multi-objective Evolutive Multi-thread Path-Planning Algorithm Considering Real-Time Extreme Weather Events

2019 ◽  
pp. 137-146
Author(s):  
Alfredo Cristóbal-Salas ◽  
Andrei Tchernykh ◽  
Sergio Nesmachnow ◽  
Bardo Santiago-Vicente ◽  
Raúl Alejandro Luna-Sánchez ◽  
...  
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Multi Objective ◽  
Weather Events ◽  
Planning Algorithm ◽  
Path Planning Algorithm

scholarly journals HCTNav: A Path Planning Algorithm for Low-Cost Autonomous Robot Navigation in Indoor Environments

2013 ◽  
Vol 2 (3) ◽  
pp. 729-748 ◽  
Author(s):  
Marco Pala ◽  
Nafiseh Eraghi ◽  
Fernando López-Colino ◽  
Alberto Sanchez ◽  
Angel de Castro ◽  
...  
Keyword(s):  
Path Planning ◽  
Robot Navigation ◽  
Low Cost ◽  
Autonomous Robot ◽  
Indoor Environments ◽  
Planning Algorithm ◽  
Autonomous Robot Navigation ◽  
Path Planning Algorithm

scholarly journals Multi-constrained cooperative path planning of multiple drones for persistent surveillance in urban environments

2021 ◽  
Author(s):  
Yu Wu ◽  
Shaobo Wu ◽  
Xinting Hu
Keyword(s):  
Path Planning ◽  
Urban Environments ◽  
Time Interval ◽  
Planning Problem ◽  
Complete Coverage ◽  
Coverage Area ◽  
Covered Area ◽  
Long Time ◽  
Planning Algorithm ◽  
Path Planning Algorithm

AbstractDifferent from the usual surveillance task in which the goal is to achieve complete coverage of the specified area, the cooperative path planning problem of drones for persistent surveillance task is studied in this paper considering multiple constraints of the covered area. The goal is to maximize the combinational coverage area of drones while giving preference to the area that hasn’t been visited beyond a certain time interval. The influence of shooting resolution and blocking of buildings are considered, and the state information of each grid is defined to record the visit information of the ground area. Considering the characteristic of the established model, the multi-constrained cooperative path planning (MCCPP) algorithm is developed. The grids which have not been visited for a long time are received special attentions, and the drone is led to reducing the flight height to cover the gird which has a special requirement on the shooting resolution. The cooperation mechanism among drones is also set to ensure that all the drones can determine the next path point synchronously. An emergency path planning algorithm with the continuous checking strategy is designed for a drone to fly to the specified area and finish a complete coverage of it.

scholarly journals Recursive Rewarding Modified Adaptive Cell Decomposition (RR-MACD): A Dynamic Path Planning Algorithm for UAVs

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 306 ◽  
Author(s):  
Franklin Samaniego ◽  
Javier Sanchis ◽  
Sergio García-Nieto ◽  
Raúl Simarro
Keyword(s):  
Path Planning ◽  
Computing Time ◽  
Cell Decomposition ◽  
Computational Time ◽  
Dynamic Path Planning ◽  
Relevant Task ◽  
Planning Algorithm ◽  
Free Spaces ◽  
Memory Resources ◽  
Path Planning Algorithm

A relevant task in unmanned aerial vehicles (UAV) flight is path planning in 3 D environments. This task must be completed using the least possible computing time. The aim of this article is to combine methodologies to optimise the task in time and offer a complete 3 D trajectory. The flight environment will be considered as a 3 D adaptive discrete mesh, where grids are created with minimal refinement in the search for collision-free spaces. The proposed path planning algorithm for UAV saves computational time and memory resources compared with classical techniques. With the construction of the discrete meshing, a cost response methodology is applied as a discrete deterministic finite automaton (DDFA). A set of optimal partial responses, calculated recursively, indicates the collision-free spaces in the final path for the UAV flight.

Graph-based path planning for intelligent UAVs in area coverage applications

2020 ◽  
Vol 39 (6) ◽  
pp. 8191-8203
Author(s):  
J. Akshya ◽  
P.L.K. Priyadarsini
Keyword(s):  
Path Planning ◽  
Minimum Spanning Tree ◽  
Optimal Path ◽  
Approximate Solutions ◽  
Geographic Area ◽  
Optimal Path Planning ◽  
Area Of Interest ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
The Given

In recent times, Unmanned Air Vehicles (UAVs) are being deployed for several tasks of terrain coverage such as surveillance, photogrammetry, smart irrigation, civil security, and disaster management. In these applications, one of the most vital issues to be addressed is, covering the area under observation with minimum traversal for the UAV. So, the problem addressed in this paper is as follows: For a given geographic area and the given parameters describing the UAV’s coverage capacity, the problem is to find an optimal route that covers the given geographic area. In this paper, an optimal path planning algorithm for the area under observation, given as a closed curve, is proposed. The algorithm partitions the given area of interest into multiple non-uniform rectangles while considering other parameters such as the flying altitude of the UAV and obstacles that could be encountered during its flight. The problem is transformed into Traveling Salesman Problem by constructing a graph from the rectangular partitioning. Effective approximate solutions are provided to this problem, using the Minimum Spanning Tree (MST) approximation algorithm and Ant Colony Optimization (ACO). The experimental results show that ACO outperforms the MST based algorithm as it does not get stuck in local minima.

scholarly journals A Global Path Planning Algorithm Based on Bidirectional SVGA

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Taizhi Lv ◽  
Chunxia Zhao ◽  
Jiancheng Bao
Keyword(s):  
Path Planning ◽  
Multicore Processors ◽  
Computing Time ◽  
Optimal Path ◽  
Visibility Graph ◽  
Path Optimization ◽  
Time And Space ◽  
Global Path Planning ◽  
Planning Algorithm ◽  
Path Planning Algorithm

For path planning algorithms based on visibility graph, constructing a visibility graph is very time-consuming. To reduce the computing time of visibility graph construction, this paper proposes a novel global path planning algorithm, bidirectional SVGA (simultaneous visibility graph construction and path optimization by A⁎). This algorithm does not construct a visibility graph before the path optimization. However it constructs a visibility graph and searches for an optimal path at the same time. At each step, a node with the lowest estimation cost is selected to be expanded. According to the status of this node, different through lines are drawn. If this line is free-collision, it is added to the visibility graph. If not, some vertices of obstacles which are passed through by this line are added to the OPEN list for expansion. In the SVGA process, only a few visible edges which are in relation to the optimal path are drawn and the most visible edges are ignored. For taking advantage of multicore processors, this algorithm performs SVGA in parallel from both directions. By SVGA and parallel performance, this algorithm reduces the computing time and space. Simulation experiment results in different environments show that the proposed algorithm improves the time and space efficiency of path planning.

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.

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