PPS: Energy-Aware Grid-Based Coverage Path Planning for UAVs Using Area Partitioning in the Presence of NFZs

Ghaddar, Alia; Merei, Ahmad; Natalizio, Enrico

doi:10.3390/s20133742

Cited by 31 publications

(20 citation statements)

References 34 publications

(130 reference statements)

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“…An analysis of the metrics used in this work, such as redundancy, R % , which does not represent a significant factor when comparing these three CPP methods, showed that, although the BECD was the method with the highest redundancy, it was also the method with the lowest consumption of energy. On the other hand, the other metrics, such as the L ct metric, flight time metric, and the number of maneuvers, are directly related to energy consumption, as can be seen in Appendix B and C. It also helps to conclude that the BECD method is more suitable for widths in a range between 0 and 7 m, due to the lower number of maneuvers in these ranges, as other authors have also mentioned [102,103]. The percentage of coverage of the total area, C % , always showed that coverage was total…”

Section: Discussionmentioning

confidence: 69%

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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Section: Discussionmentioning

confidence: 69%

Coverage Path Planning with Semantic Segmentation for UAV in PV Plants

et al. 2021

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“…In reality, the height at a constant depth varies when UAV or AUV covers the region of interest (ROI) on a non-planar surface (large degree of the surface slope). When the environment is prior known, cellular decomposition is the simplest method to segment the region into smaller sub-areas, either regular grid cells or polygon shapes [27,28,29]. CPP in the 3D space mainly focuses on the target coverage in such a way as to cover the critical ROI for evaluating the quality of the structure (3D model).…”

Section: Subareamentioning

confidence: 99%

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

Mohd‐Mokhtar²,

2021

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The small battery capacities of the mobile robot and the un-optimized planning efficiency of the industrial robot bottlenecked the time efficiency and productivity rate of coverage tasks in terms of speed and accuracy, putting a great constraint on the usability of the robot applications in various planning strategies in specific environmental conditions. Thus, it became highly desirable to address the optimization problems related to exploration and coverage path planning (CPP). In general, the goal of the CPP is to find an optimal coverage path with generates a collision-free trajectory by reducing the travel time, processing speed, cost energy, and the number of turns along the path length, as well as low overlapped rate, which reflect the robustness of CPP. This paper reviews the principle of CPP and discusses the development trend, including design variations and the characteristic of optimization algorithms, such as classical, heuristic, and most recent deep learning methods. Then, we compare the advantages and disadvantages of the existing CPP-based modeling in the area and target coverage. Finally, we conclude numerous open research problems of the CPP and make suggestions for future research directions to gain insights. INDEX TERMSCoverage path planning, exploration, heuristic algorithm, deep reinforcement learning.

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“…The current paper uses the grid-based decomposition as presented by the authors in [30,31]. Numerous algorithms are available that can enable us to divide an environment using different strategies like spanning trees [32], energy-aware algorithms [33,34], and neural networks [35], among others. The grid-based map decomposition algorithm is light-weight in terms of computational complexity for generating a coverage path.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning-Based Complete Area Coverage Path Planning for a Modified hTrihex Robot

Apuroop

Elara

et al. 2021

Sensors

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One of the essential attributes of a cleaning robot is to achieve complete area coverage. Current commercial indoor cleaning robots have fixed morphology and are restricted to clean only specific areas in a house. The results of maximum area coverage are sub-optimal in this case. Tiling robots are innovative solutions for such a coverage problem. These new kinds of robots can be deployed in the cases of cleaning, painting, maintenance, and inspection, which require complete area coverage. Tiling robots’ objective is to cover the entire area by reconfiguring to different shapes as per the area requirements. In this context, it is vital to have a framework that enables the robot to maximize the area coverage while minimizing energy consumption. That means it is necessary for the robot to cover the maximum area with the least number of shape reconfigurations possible. The current paper proposes a complete area coverage planning module for the modified hTrihex, a honeycomb-shaped tiling robot, based on the deep reinforcement learning technique. This framework simultaneously generates the tiling shapes and the trajectory with minimum overall cost. In this regard, a convolutional neural network (CNN) with long short term memory (LSTM) layer was trained using the actor-critic experience replay (ACER) reinforcement learning algorithm. The simulation results obtained from the current implementation were compared against the results that were generated through traditional tiling theory models that included zigzag, spiral, and greedy search schemes. The model presented in the current paper was also compared against other methods where this problem was considered as a traveling salesman problem (TSP) solved through genetic algorithm (GA) and ant colony optimization (ACO) approaches. Our proposed scheme generates a path with a minimized cost at a lesser time.

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PPS: Energy-Aware Grid-Based Coverage Path Planning for UAVs Using Area Partitioning in the Presence of NFZs

Cited by 31 publications

References 34 publications

Coverage Path Planning with Semantic Segmentation for UAV in PV Plants

Coverage Path Planning with Semantic Segmentation for UAV in PV Plants

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

Reinforcement Learning-Based Complete Area Coverage Path Planning for a Modified hTrihex Robot

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