An Effective Method for Complete Visual Coverage Path Planning

Li, Jie; Zheng, Changwen; Hu, Xiaohui

doi:10.1109/cso.2010.167

Cited by 4 publications

(5 citation statements)

References 13 publications

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“…To connect the waypoints of each flight path, we solved the TSP using the Network Analyst extension from ArcGIS [38]. On the one hand, the design of this path has not considered restricted visibility as appears, e.g., in [31]. Current camera technology allows high-definition image processing with minimal loss of definition.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…The proposal in [31] addressed the path planning problem using a new method based on siting multiple observers and an evolutionary algorithm. First, an iterative process removes redundant points according to how each particular viewshed value affects the area coverage.…”

Section: Area Coveragementioning

confidence: 99%

“…3) Stage-3: identifying the isolated areas: Some path planning strategies, such as in [31] and [32], ensure that the UAV reaches all isolated areas of the target region, but following a greedy approach: they force the UAV to fly over almost any point on the ground. Conversely, VPP provides a better solution: identify visually isolated areas for the UAV to fly over their centroids, avoiding the need to sweep the whole region.…”

Section: Workflow Of the Vpp Heuristicmentioning

confidence: 99%

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VPP: Visibility-Based Path Planning Heuristic for Monitoring Large Regions of Complex Terrain Using a UAV Onboard Camera

Sanchez-Fernandez

Romero

Burgueno

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The use of Unmanned Aerial Vehicles (UAVs) with multiple onboard sensors has grown significantly in tasks involving terrain coverage such as environmental and civil monitoring, disaster management, and forest fire fighting. Many of these tasks require a quick and early response, which makes maximising the land covered by the flight path a challenging objective, especially when the area to be monitored is irregular, large and includes many blind spots. Accordingly, state-of-the-art total viewshed algorithms can be of great help to analyse large areas and find new paths providing maximum visibility. This paper shows how the total viewshed computation is a valuable tool for generating paths that provide maximum visibility during a flight. We introduce a new heuristic called Visibility-based Path Planning (VPP) that offers a different solution to the path planning problem. VPP identifies the hidden areas from the target territory to generate a path that provides the highest visual coverage. Simulation results show that VPP can cover up to 98.7% of the Montes de Malaga Natural Park and 94.5% of the Sierra de las Nieves National Park, both located within the province of Malaga (Spain) and chosen as regions of interest. In addition, a real flight test confirmed the high visibility achieved using VPP. Our methodology and analysis can be easily applied to enhance monitoring in other large outdoor areas.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Area Coveragementioning

confidence: 99%

Section: Workflow Of the Vpp Heuristicmentioning

confidence: 99%

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VPP: Visibility-Based Path Planning Heuristic for Monitoring Large Regions of Complex Terrain Using a UAV Onboard Camera

Sanchez-Fernandez

Romero

Burgueno

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The viewshed in a terrain refers to a distinct region of the terrain that can be seen at one or more specific positions within a certain range on the terrain, and viewshed analysis is the process to compute such viewshed, with the elevation value of a given location based on the digital elevation model (DEM) data [1]. Terrain viewshed analysis based on the digital elevation model is widely used in archaeological research [2], path planning [3], siting optimization [4], landscape analysis [5], military analysis [6], security monitoring [7], or combination with other grids for terrain description [8,9].…”

Section: Introductionmentioning

confidence: 99%

HiPDERL: An Improved Implementation of the PDERL Viewshed Algorithm and Accuracy Analysis

Cheng¹,

Dou²

2022

IJGI

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Terrain viewshed analysis based on the digital elevation model (DEM) is of significant application value. A lot of viewshed analysis algorithms have been proposed, including R3 as the accurate one and others as efficient ones. The R3 algorithm is accurate because of its comprehensive but time-consuming computation, while the others are efficient due to proper approximation. However, no algorithm is capable of taking advantage of both until one algorithm is proposed, which is based on a ‘proximity-direction-elevation’ (PDE) coordinate system and named the PDE spatial reference line (PDERL) algorithm. The original research proves the PDERL algorithm is perfectly accurate by theory and experimental results, in comparison with R3 as standard, and even more efficient than R3. However, the original research does not mention the cases where the observer is placed on grid points, and the original implementation does not produce very accurate results in practice. It is important to find out and correct the errors. In this paper, a checking algorithm for PDERL is proposed to allow further investigation of errors. With the fundamental ideas of PDERL unchallenged, an improved implementation of the PDERL algorithm is proposed, named HiPDERL. By experimental results, this paper proves HiPDERL utilizes the potential of PDERL on accuracy at the cost of a little efficiency when the observer is placed on grid points.

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“…Given the terrain data and a viewpoint, what viewshed analysis does is to identify which part of the analysis area is visible from the viewpoint. Viewshed analysis is widely adopted as an underlying technology in various applications, such as siting optimization [1], path planning [2], security monitoring [3] and so on.…”

Section: Introductionmentioning

confidence: 99%

HiXDraw: An Improved XDraw Algorithm Free of Chunk Distortion

Zhu

et al. 2019

IJGI

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Viewshed analysis is of great interest to location optimization, environmental planning, ecology and tourism. There have been plenty of viewshed analysis methods which are generally time-consuming and among these methods, the XDraw algorithm is one of the fastest algorithms and has been widely adopted in various applications. Unfortunately, XDraw suffers from chunk distortion which greatly lowers the accuracy, which limits the application of XDraw to a certain extent. Previous works failed to remove chunk distortion because they are unaware of the underlying contribution relationship. In this paper, we propose HiXDraw—an improved XDraw algorithm free of chunk distortion. We first uncover the causation of chunk distortion from an innovative contributing perspective. Instead of recording LOS (line-of-sight) height, we use a new auxiliary grid to preserve contributing points. By preventing improper terrain data from contributing to determining the visibility, we significantly improve the accuracy of the outcome viewshed. The experimental results reveal that the error rate largely decreases by 65%. Given the same computing time, HiXDraw is more accurate than previous improvements in XDraw. To validate the removal of chunk distortion, we also present a pillar experiment.

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An Effective Method for Complete Visual Coverage Path Planning

Cited by 4 publications

References 13 publications

VPP: Visibility-Based Path Planning Heuristic for Monitoring Large Regions of Complex Terrain Using a UAV Onboard Camera

VPP: Visibility-Based Path Planning Heuristic for Monitoring Large Regions of Complex Terrain Using a UAV Onboard Camera

HiPDERL: An Improved Implementation of the PDERL Viewshed Algorithm and Accuracy Analysis

HiXDraw: An Improved XDraw Algorithm Free of Chunk Distortion

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