Coverage path planning for multiple unmanned aerial vehicles in maritime search and rescue operations

Cho, Sung Won; Park, Hyun Ji; Lee, Hanseob; Shim, David Hyunchul; Kim, Sun‐Young

doi:10.1016/j.cie.2021.107612

Cited by 95 publications

(40 citation statements)

References 22 publications

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“…Some approaches such as [ 11 , 21 ] apply exact cellular decomposition methods; in the first case, the authors use auction-based algorithms to assign tasks and in the other, with a leader–follower approach. Proposals as in [ 22 , 23 ] address the problem using grid pattern map decomposition and linear programming methods to solve the graph generated by the possible trajectories and the agents. Others such as [ 24 ] use a column generation model to perform the coverage mission with multiple UAVs using energy constraints; only numerical simulations were collected in the results.…”

Section: Introductionmentioning

confidence: 99%

Fast Multi-UAV Path Planning for Optimal Area Coverage in Aerial Sensing Applications

Luna

Isaac

Ragab

et al. 2022

Sensors

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This paper deals with the problems and the solutions of fast coverage path planning (CPP) for multiple UAVs. Through this research, the problem is solved and analyzed with both a software framework and algorithm. The implemented algorithm generates a back-and-forth path based on the onboard sensor footprint. In addition, three methods are proposed for the individual path assignment: simple bin packing trajectory planner (SIMPLE-BINPAT); bin packing trajectory planner (BINPAT); and Powell optimized bin packing trajectory planner (POWELL-BINPAT). The three methods use heuristic algorithms, linear sum assignment, and minimization techniques to optimize the planning task. Furthermore, this approach is implemented with applicable software to be easily used by first responders such as police and firefighters. In addition, simulation and real-world experiments were performed using UAVs with RGB and thermal cameras. The results show that POWELL-BINPAT generates optimal UAV paths to complete the entire mission in minimum time. Furthermore, the computation time for the trajectory generation task decreases compared to other techniques in the literature. This research is part of a real project funded by the H2020 FASTER Project, with grant ID: 833507.

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Section: Introductionmentioning

confidence: 99%

Fast Multi-UAV Path Planning for Optimal Area Coverage in Aerial Sensing Applications

Luna

Isaac

Ragab

et al. 2022

Sensors

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show abstract

“…However, this work can be seen as a task assignment method, and could be used as a previous step to our proposed target area CPP method. Cho et al [ 40 ] highlight a two-phase method for solving the CPP problem of multiple-UAV areas in maritime search and rescue. The first phase decomposes the search area into a graph, and the second phase uses the mixed-integer linear programming (MILP) to calculate an optimal path that minimizes the completion time.…”

Section: Introductionmentioning

confidence: 99%

Multi UAV Coverage Path Planning in Urban Environments

Muñoz

López

Quevedo

et al. 2021

Sensors

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Coverage path planning (CPP) is a field of study which objective is to find a path that covers every point of a certain area of interest. Recently, the use of Unmanned Aerial Vehicles (UAVs) has become more proficient in various applications such as surveillance, terrain coverage, mapping, natural disaster tracking, transport, and others. The aim of this paper is to design efficient coverage path planning collision-avoidance capable algorithms for single or multi UAV systems in cluttered urban environments. Two algorithms are developed and explored: one of them plans paths to cover a target zone delimited by a given perimeter with predefined coverage height and bandwidth, using a boustrophedon flight pattern, while the other proposed algorithm follows a set of predefined viewpoints, calculating a smooth path that ensures that the UAVs pass over the objectives. Both algorithms have been developed for a scalable number of UAVs, which fly in a triangular deformable leader-follower formation with the leader at its front. In the case of an even number of UAVs, there is no leader at the front of the formation and a virtual leader is used to plan the paths of the followers. The presented algorithms also have collision avoidance capabilities, powered by the Fast Marching Square algorithm. These algorithms are tested in various simulated urban and cluttered environments, and they prove capable of providing safe and smooth paths for the UAV formation in urban environments.

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“…Thus, it takes a relatively long time to generate a path. However, the grid-based decomposition method does generate an optimal path [43][44][45][46][47]. This study focuses on generating the optimal search path for multiple UAVs by segmenting the target area using the grid-based decomposition method.…”

Section: Introductionmentioning

confidence: 99%

“…In most grid-based decomposition studies [43][44][45][46][47][48][49][50], the shape of the grid cell is set to a square, which is the motivation for this study. The search path may derive differently as the location and number of adjacent nodes that the UAV can move to from each node vary depending on the shape of the grid cell.…”

Section: Introductionmentioning

confidence: 99%

“…First, to the best of our knowledge, this is the first study to propose a MILP model for CPP based on hexagonal grid decomposition. The model proposed by [44] is a comprehensive model that does not consider the angle between adjacent nodes as a constraint, but it is time-consuming to derive the solution. Therefore, we develop a model that lowers the computational volume by utilizing the angles between adjacent grid cells.…”

Section: Introductionmentioning

confidence: 99%

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Multi-UAV Coverage Path Planning Based on Hexagonal Grid Decomposition in Maritime Search and Rescue

Cho¹,

Park²,

Park³

et al. 2021

Mathematics

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In the event of a maritime accident, surveying the maximum area efficiently in the least amount of time is crucial for rescuing survivors. Increasingly, unmanned aerial vehicles (UAVs) are being used in search and rescue operations. This study proposes a method to generate a search path that covers all generated nodes in the shortest amount of time with multiple heterogeneous UAVs. The proposed model, which is a mixed-integer linear programming (MILP) model based on a hexagonal grid-based decomposition method, was verified through a simulation analysis based on the performance of an actual UAV. This study presents both the optimization technique’s calculation time as a function of the search area size and the various UAV routes derived as the search area grows. The results of this study can have wide-ranging applications for emergency search and rescue operations.

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Coverage path planning for multiple unmanned aerial vehicles in maritime search and rescue operations

Cited by 95 publications

References 22 publications

Fast Multi-UAV Path Planning for Optimal Area Coverage in Aerial Sensing Applications

Fast Multi-UAV Path Planning for Optimal Area Coverage in Aerial Sensing Applications

Multi UAV Coverage Path Planning in Urban Environments

Multi-UAV Coverage Path Planning Based on Hexagonal Grid Decomposition in Maritime Search and Rescue

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