Flight Testing Boustrophedon Coverage Path Planning for Fixed Wing UAVs in Wind

Sung

2019

Sensors

To cover an area of interest by an autonomous vehicle, such as an Unmanned Aerial Vehicle (UAV), planning a coverage path which guides the unit to cover the area is an essential process. However, coverage path planning is often problematic, especially when the boundary of the area is complicated and the area contains several obstacles. A common solution for this situation is to decompose the area into disjoint convex sub-polygons and to obtain coverage paths for each sub-polygon using a simple back-and-forth pattern. Aligned with the solution approach, we propose a new convex decomposition method which is simple and applicable to any shape of target area. The proposed method is designed based on the idea that, given an area of interest represented as a polygon, a convex decomposition of the polygon mainly occurs at the points where an interior angle between two edges of the polygon is greater than 180 degrees. The performance of the proposed method is demonstrated by comparison with existing convex decomposition methods using illustrative examples.

Section: Discussionmentioning

confidence: 99%

Convex Decomposition for a Coverage Path Planning for Autonomous Vehicles: Interior Extension of Edges

Sung

2019

Sensors

“…The wing panels of a solar-powered unmanned aerial vehicle (UAV) receive two parts of scattered radiation, namely the refracted and reflected sky scattered radiation energy. 6 This part of the Energy II is simplified as follows:…”

Section: Establishment Of Solar Radiation-related Cost Modelmentioning

confidence: 99%

“…When the optimal objective function is determined, the weight of each edge in the network graph is also determined. That is, the cost weight of the first d edge in the network graph is calculated by the following cost function in the process of searching nodes [5][6][7][8][9][10] :…”

Section: Establishment Of Alternative Cost Modelsmentioning

confidence: 99%

Research on route planning for solar UAV based on the intelligent optimization algorithm

Hu,

Liu

2023

Science Progress

The solar unmanned aerial vehicle (UAV) route planning needs to comprehensively consider the conversion efficiency of solar cells under the influence of solar ground reflection radiation and sky scattering radiation. On the one hand, it is necessary to consider the cost of radar threat, mileage energy consumption, mountain impact and other costs. On the other hand, it is also necessary to consider the influence of high threat, mountain shadow occlusion cost, and cloud shading cost on solar photovoltaic conversion efficiency. The above problem was solved through using the ant colony intelligent optimization algorithm. By constructing ant colony paths rationally, models of mountain impact cost, high threat, mountain shadow shelter cost, and cloud shading cost were established. The constraints such as the maximum action distance, solar irradiation angle and effective action distance of various costs were introduced into the cost model and exploration factor calculation, and the comprehensive optimization problem of solar UAV route was solved. Finally, the simulation results show that the algorithm path structure is reasonable; the target node can be found independently; the convergence speed can meet the requirements of route planning; the generated route cost is small; the algorithm is reasonable and effective.

“…For instance, Simon et al [ 26 ] utilized a tail-sitter configuration UAV to conduct 3D mapping of the terrain of a specific region, in which they tried to directly acquire photogrammetric information via a lightweight VTOL aerial vehicle. Work conducted by Coombes et al [ 27 , 28 , 29 , 30 ], on the other hand, developed coverage path planning methods through geometry decomposition, Boustrophedon paths, etc., and meanwhile, different scenarios were addressed, including utilization in agriculture activities and windy situations. Paull et al [ 31 ] also proposed a path planner that attempted to achieve a coverage path without a priori understanding of the workspace, in which a camera sensor was equipped, and an in situ path planning module was included for the aircraft to make control decisions.…”

Section: Introductionmentioning

confidence: 99%

Development of Fixed-Wing UAV 3D Coverage Paths for Urban Air Quality Profiling

Zhou

Jiang

et al. 2022

Sensors

Due to the ever-increasing industrial activity, humans and the environment suffer from deteriorating air quality, making the long-term monitoring of air particle indicators essential. The advances in unmanned aerial vehicles (UAVs) offer the potential to utilize UAVs for various forms of monitoring, of which air quality data acquisition is one. Nevertheless, most current UAV-based air monitoring suffers from a low payload, short endurance, and limited range, as they are primarily dependent on rotary aerial vehicles. In contrast, a fixed-wing UAV may be a better alternative. Additionally, one of the most critical modules for 3D profiling of a UAV system is path planning, as it directly impacts the final results of the spatial coverage and temporal efficiency. Therefore, this work focused on developing 3D coverage path planning based upon current commercial ground control software, where the method mainly depends on the Boustrophedon and Dubins paths. Furthermore, a user interface was also designed for easy accessibility, which provides a generalized tool module that links up the proposed algorithm, the ground control software, and the flight controller. Simulations were conducted to assess the proposed methods. The result showed that the proposed methods outperformed the existing coverage paths generated by ground control software, as it showed a better coverage rate with a sampling density of 50 m.