Coverage Path Planning Techniques for Inspection of Disjoint Regions With Precedence Provision

Khanam, Zeba; Saha, Sangeet; Ehsan, Shoaib; McDonald-Maier, Klaus

doi:10.1109/access.2020.3044987

Cited by 8 publications

(9 citation statements)

References 47 publications

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“…The approach takes into account the priority of monitoring sites but focuses on optimizing drone swarm scheduling, distribution, and communication coverage to minimize flight length and energy consumption. In [17], the access order of multiple regions is manually prescribed, and a heuristic algorithm is used to generate the sequence of regions without considering the different priority levels of different regions.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Regional Path-Planning Method for Rescue UAVs with Priority Constraints

Du,

Fan,

Gui

et al. 2023

Drones

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This study focuses on the path-planning problem of rescue UAVs with regional detection priority. Initially, we propose a mixed-integer programming model that integrates coverage path planning (CPP) and the hierarchical traveling salesman problem (HTSP) to address multi-regional path planning under priority constraints. For intra-regional path planning, we present an enhanced method for acquiring reciprocating flight paths to ensure complete coverage of convex polygonal regions with shorter flight paths when a UAV is equipped with sensors featuring circular sampling ranges. An additional comparison was made for spiral flight paths, and second-order Bezier curves were employed to optimize both sets of paths. This optimization not only reduced the path length but also enhanced the ability to counteract inherent drone jitter. Additionally, we propose a variable neighborhood descent algorithm based on K-nearest neighbors to solve the inter-regional access order path-planning problem with priority. We establish parameters for measuring distance and evaluating the priority order of UAV flight paths. Simulation and experiment results demonstrate that the proposed algorithm can effectively assist UAVs in performing path-planning tasks with priority constraints, enabling faster information collection in important areas and facilitating quick exploration of three-dimensional characteristics in unknown disaster areas by rescue workers. This algorithm significantly enhances the safety of rescue workers and optimizes crucial rescue times in key areas.

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Section: Introductionmentioning

confidence: 99%

A Multi-Regional Path-Planning Method for Rescue UAVs with Priority Constraints

Du,

Fan,

Gui

et al. 2023

Drones

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“…The robot has to follow arbitrary routes to move between components. This is a less studied area and the works that have been found are the following: [9][10][11][12][13][14]. Methods developed for single regions are not adequate for disjoint scenarios since, by definition, they will try to cover all intermediate areas, while methods for disjoint areas only follow the shortest available path.…”

Section: Introductionmentioning

confidence: 99%

“…The advantage of this strategy is that, even though the problem remains NP-Hard, the instance size is drastically reduced. In the previous grid-based works [10,12], the size of the instance depends on the number of cells. For large floors, the division of the map by the robot footprint might result in hundreds or thousands of positions, so optimising a path for such an instance size could be prohibitive; such fact can be observed in previous works where the maps have a small number of cells [10].…”

Section: Introductionmentioning

confidence: 99%

“…In the previous grid-based works [10,12], the size of the instance depends on the number of cells. For large floors, the division of the map by the robot footprint might result in hundreds or thousands of positions, so optimising a path for such an instance size could be prohibitive; such fact can be observed in previous works where the maps have a small number of cells [10]. On the other hand, the presented proposal divides the problem into rooms and then optimises for an instance equal to the number of rooms on the map, which should be noticeably smaller.…”

Section: Introductionmentioning

confidence: 99%

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A Divide and Conquer Strategy for Sweeping Coverage Path Planning

Vasquez-Gomez

Merchán-Cruz

2022

Energies

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One of the main challenges faced by floor treatment service robots is to compute optimal paths that completely cover a set of target areas. Short paths are of noticeable importance because their length is directly proportional to the energy used by the robot. Such a problem is known to be NP-hard; therefore, efficient algorithms are needed. In particular, computation efficiency is important for mobile robots with limited onboard computation capability. The general problem is known as coverage path planning (CPP). The CPP has several variants for single regions and for disjoint regions. In this research, we are investigating the solutions for disjoint target regions (rooms) that have fixed connection points (doors). In particular, we have found effective simplifications for the cases of rooms with one and two doors, while the challenging case of an unbounded number of rooms can be solved by approximation. As a result, this work presents a divide and conquer strategy (DnCS) to address the problem of finding a path for a sweeping robot that needs to sweep a set of disjoint rooms that are connected by fixed doors and corridors. The strategy divides the problem into computing the sweeping paths for the target rooms and then merges those paths into one solution by optimising the room visiting order. In this strategy, a geometrical approach for room coverage and an undirected rural postman problem optimisation are strategically combined to solve the coverage of the entire area of interest. The strategy has been tested in several synthetic maps and a real scenario showing short computation times and complete coverage.

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“…The elemental approach most used to solve offline CPP problem sis the area decomposition into non-overlapping sub-regions [56], to determine the appropriate visiting sequence of each sub-region and to cover each decomposed region in a back-and-forth movement to secure a complete coverage path. As a result, the methods for obtaining complete coverage of an RoI are the exact and approximate cellular decomposition methods [57][58][59].…”

Section: Introductionmentioning

confidence: 99%

Coverage Path Planning with Semantic Segmentation for UAV in PV Plants

et al. 2021

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Solar energy is one of the most strategic energy sources for the world’s economic development. This has caused the number of solar photovoltaic plants to increase around the world; consequently, they are installed in places where their access and manual inspection are arduous and risky tasks. Recently, the inspection of photovoltaic plants has been conducted with the use of unmanned aerial vehicles (UAV). Although the inspection with UAVs can be completed with a drone operator, where the UAV flight path is purely manual or utilizes a previously generated flight path through a ground control station (GCS). However, the path generated in the GCS has many restrictions that the operator must supply. Due to these restrictions, we present a novel way to develop a flight path automatically with coverage path planning (CPP) methods. Using a DL server to segment the region of interest (RoI) within each of the predefined PV plant images, three CPP methods were also considered and their performances were assessed with metrics. The UAV energy consumption performance in each of the CPP methods was assessed using two different UAVs and standard metrics. Six experiments were performed by varying the CPP width, and the consumption metrics were recorded in each experiment. According to the results, the most effective and efficient methods are the exact cellular decomposition boustrophedon and grid-based wavefront coverage, depending on the CPP width and the area of the PV plant. Finally, a relationship was established between the size of the photovoltaic plant area and the best UAV to perform the inspection with the appropriate CPP width. This could be an important result for low-cost inspection with UAVs, without high-resolution cameras on the UAV board, and in small plants.

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Coverage Path Planning Techniques for Inspection of Disjoint Regions With Precedence Provision

Cited by 8 publications

References 47 publications

A Multi-Regional Path-Planning Method for Rescue UAVs with Priority Constraints

A Multi-Regional Path-Planning Method for Rescue UAVs with Priority Constraints

A Divide and Conquer Strategy for Sweeping Coverage Path Planning

Coverage Path Planning with Semantic Segmentation for UAV in PV Plants

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