Coverage path planning for spraying drones

Vazquez-Carmona, Esther Viridiana; Vasquez-Gomez, J. Irving; Lozada, Juan Carlos Herrera; Antonio-Cruz, Mayra

doi:10.1016/j.cie.2022.108125

Cited by 37 publications

(23 citation statements)

References 46 publications

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“…Authors of the study [18] propose a coverage path planning method for a spraying drone, which allows restricting the flight to the region of interest avoiding potential collisions; this method also provides the shorted computational time. Nevertheless, this method can be used for the homogenous group of UAVs only.…”

Section: B Coverage Path Planning Problem For Pamentioning

confidence: 99%

“…2. Flight control methods, providing the solutions to the problems of covering a given area with one device or a group of devices [18]. In this paper, we focus on the second problem.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we focus on the second problem. At the present time, this problem is considered in the studies which propose the planning of the optimal path for one drone's or group of drones' mission based on the coverage problem [18]. Although the coverage algorithm is usually quite simple, however, its implementation in specific cases may require non-trivial optimization.…”

Section: Introductionmentioning

confidence: 99%

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Coverage Path Planning Optimization of Heterogeneous UAVs Group for Precision Agriculture

et al. 2023

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This research was funded by the: Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan, grant number AP14869972 «Development and adaptation of computer vision and machine learning methods for solving precision agriculture problems using unmanned aerial systems», grant number AP08856412 « Development of Intelligent Data Processing and Flight Planning Models for Precision Farming Tasks Using UAVs», grant number BR18574144 "Development of a data mining system for monitoring dams and other engineering structures under the conditions of man-made and natural impacts", grant number BR10965172 «Space monitoring and GIS for quantitative assessment of soil salinity and degradation of agricultural lands in South Kazakhstan»; the Slovak Research and Development Agency ''New methods development for reliability analysis of complex system'' under Grant APVV-18-0027; the Ministry of Education, Science, Research, and Sport of the Slovak Republic ''New approaches of reliability analysis of non-coherent systems'' under Grant VEGA 1/0165/21.

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Section: B Coverage Path Planning Problem For Pamentioning

confidence: 99%

“…2. Flight control methods, providing the solutions to the problems of covering a given area with one device or a group of devices [18]. In this paper, we focus on the second problem.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Coverage Path Planning Optimization of Heterogeneous UAVs Group for Precision Agriculture

et al. 2023

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“…The literature review did reveal interesting algorithms for CPP, but full comparison is prohibited since our work does not share similarities during the simulation analysis. For example, in [ 49 ] the authors examine the coverage of public spaces for disinfection purposes using areal systems. Although they do analyze the area coverage using the parallel coverage path they only use a single drone to complete the coverage and there is no comparison with other coverage paths in terms of simulation time.…”

Section: Past Related Workmentioning

confidence: 99%

Coverage Path Planning and Point-of-Interest Detection Using Autonomous Drone Swarms

Bezas

Tsoumanis

Angelis

et al. 2022

Sensors

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Unmanned Aerial Vehicles (UAVs) or drones presently are enhanced with miniature sensors that can provide information relative to their environment. As such, they can detect changes in temperature, orientation, altitude, geographical location, electromagnetic fluctuations, lighting conditions, and more. Combining this information properly can help produce advanced environmental awareness; thus, the drone can navigate its environment autonomously. Wireless communications can also aid in the creation of drone swarms that, combined with the proper algorithm, can be coordinated towards area coverage for various missions, such as search and rescue. Coverage Path Planning (CPP) is the field that studies how drones, independently or in swarms, can cover an area of interest efficiently. In the current work, a CPP algorithm is proposed for a swarm of drones to detect points of interest and collect information from them. The algorithm’s effectiveness is evaluated under simulation results. A set of characteristics is defined to describe the coverage radius of each drone, the speed of the swarm, and the coverage path followed by it. The results show that, for larger swarm sizes, the missions require less time while more points of interest can be detected within the area. Two coverage paths are examined here—parallel lines and spiral coverage. The results depict that the parallel lines coverage is more time-efficient since the spiral increases the required time by an average of 5% in all cases for the same number of detected points of interest.

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“…Another problem studied in automated planning is the complete Coverage Path-Planning (CPP) problem, where the objective is to find an optimal or quasi-optimal path that covers every area in the region (we call such a path a complete coverage path of the region). This problem has many practical applications, such as: a) robotic vacuum-cleaning ( Viet et al., 2013 ; Yakoubi and Laskri, 2016 ; Edwards and Sörme, 2018 ; Liu et al., 2018 ); b) underwater autonomous vehicles (AUVs) ( Zhu et al., 2019 ; Han et al., 2020 ; Yordanova and Gips, 2020 ); c) 3d printing using fused deposition modeling ( Lechowicz et al., 2016 ; Afzal et al., 2019 ; Gupta, 2021 ); d) window washer robots ( Farsi et al., 1994 ; Dr.; John Dhanaseely and Srinivasan, 2021 ); e) disinfection of regions ( Conroy et al., 2021 ; Nasirian et al., 2021 ; Vazquez-Carmona et al., 2022 ); f) minesweeping ( Healey, 2001 ; Williams, 2010 ; Ðakulovic and Petrovic, 2012 ); g) agriculture and farming ( Oksanen and Visala, 2009 ; Jin, 2010 ; Santos et al., 2020 ); h) surveillance drones ( Ahmadzadeh et al., 2008 ; Modares et al., 2017 ; Vasquez-Gomez et al., 2018 ); i) search and rescue aerial drones ( Hayat et al., 2020 ; Ai et al., 2021 ; Cho et al., 2021 ). …”

Section: Introductionmentioning

confidence: 99%

Fast and optimal branch-and-bound planner for the grid-based coverage path planning problem based on an admissible heuristic function

Gareau

Beaudry²

2023

Front. Robot. AI

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This paper introduces an optimal algorithm for solving the discrete grid-based coverage path planning (CPP) problem. This problem consists in finding a path that covers a given region completely. First, we propose a CPP-solving baseline algorithm based on the iterative deepening depth-first search (ID-DFS) approach. Then, we introduce two branch-and-bound strategies (Loop detection and an Admissible heuristic function) to improve the results of our baseline algorithm. We evaluate the performance of our planner using six types of benchmark grids considered in this study: Coast-like, Random links, Random walk, Simple-shapes, Labyrinth and Wide-Labyrinth grids. We are first to consider these types of grids in the context of CPP. All of them find their practical applications in real-world CPP problems from a variety of fields. The obtained results suggest that the proposed branch-and-bound algorithm solves the problem optimally (i.e., the exact solution is found in each case) orders of magnitude faster than an exhaustive search CPP planner. To the best of our knowledge, no general CPP-solving exact algorithms, apart from an exhaustive search planner, have been proposed in the literature.

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Coverage path planning for spraying drones

Cited by 37 publications

References 46 publications

Coverage Path Planning Optimization of Heterogeneous UAVs Group for Precision Agriculture

Coverage Path Planning Optimization of Heterogeneous UAVs Group for Precision Agriculture

Coverage Path Planning and Point-of-Interest Detection Using Autonomous Drone Swarms

Fast and optimal branch-and-bound planner for the grid-based coverage path planning problem based on an admissible heuristic function

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