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

Bezas, Konstantinos; Tsoumanis, Georgios; Angelis, Constantinos T.; Οικονόμου, Κωνσταντίνος

doi:10.3390/s22197551

Cited by 12 publications

(5 citation statements)

References 45 publications

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“…The findings indicate that as swarm numbers increase, tasks take less time to complete, and additional areas of interest may be found nearby. The findings demonstrated that covering parallel lines is more time- efficient since, for every scenario, the spiral increases the amount of time needed by an average of 5% to identify the same number of points of interest [11].…”

Section: Drones In Trackingmentioning

confidence: 99%

Sensory System for Swarm Drone: A Systematic Review

Almutairi,

Baroom,

Alsubey

et al. 2024

IJCI

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Drone swarms, or unmanned aerial vehicles (UAVs), are becoming a promising domain in many areas of our lives. They are intricate, multidisciplinary systems, and most research projects concentrate on individual system components for particular use cases. Its involvement in missions and services has shown an imperatively positive influence. This review's objectives are to give a broad overview of the primary applications that spur most research efforts in this area. In our review, we have selected sixty articles between 2019 and 2024 about drone swarms. The review results outline the covered usage fields of drones in the chosen articles. It highlighted communication and control mostly in twenty-seven articles, services in nineteen articles, and tracking in fourteen articles, and we categorized the service domains more specifically as inventory, health care, defense, rescuing, and delivery. Besides that, the simulation aspect has been used for categorization as follows: twelve articles have specified their simulations, thirty-one articles haven’t specified their simulations, and seventeen articles haven’t used simulations. In addition, we have concluded the result of the SWOT analysis for the drone applications.

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Section: Drones In Trackingmentioning

confidence: 99%

Sensory System for Swarm Drone: A Systematic Review

Almutairi,

Baroom,

Alsubey

et al. 2024

IJCI

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“…Moreover, compared with a single UAV, multi-UAV communication systems consume more energy, thereby reducing the service life and performance of the UAVs; therefore, it is important to reduce energy consumption. To provide hotspot coverage, the UAV must choose an efficient flight path that allows it to access all areas within the plan [ 48 , 49 ]. To ensure safety, conflicts and collisions between multiple UAVs should be avoided.…”

Section: Preliminariesmentioning

confidence: 99%

Bio-Inspired Optimization-Based Path Planning Algorithms in Unmanned Aerial Vehicles: A Survey

Poudel

Arafat

Moh

2023

Sensors

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Advancements in electronics and software have enabled the rapid development of unmanned aerial vehicles (UAVs) and UAV-assisted applications. Although the mobility of UAVs allows for flexible deployment of networks, it introduces challenges regarding throughput, delay, cost, and energy. Therefore, path planning is an important aspect of UAV communications. Bio-inspired algorithms rely on the inspiration and principles of the biological evolution of nature to achieve robust survival techniques. However, the issues have many nonlinear constraints, which pose a number of problems such as time restrictions and high dimensionality. Recent trends tend to employ bio-inspired optimization algorithms, which are a potential method for handling difficult optimization problems, to address the issues associated with standard optimization algorithms. Focusing on these points, we investigate various bio-inspired algorithms for UAV path planning over the past decade. To the best of our knowledge, no survey on existing bio-inspired algorithms for UAV path planning has been reported in the literature. In this study, we investigate the prevailing bio-inspired algorithms extensively from the perspective of key features, working principles, advantages, and limitations. Subsequently, path planning algorithms are compared with each other in terms of their major features, characteristics, and performance factors. Furthermore, the challenges and future research trends in UAV path planning are summarized and discussed.

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“…However, our study considers both recharging states in the route and the time spent during the route including flight time and recharging time. Bezas et al [33] studied the CPP for swarms of UAVs and solved the model considering paths of parallel lines and spiral coverage. Vazquez-Carmona et al [34] developed an efficient algorithm for the CPP, especially for disinfecting the areas, and simulated the routes that they developed.…”

Section: Literature Reviewmentioning

confidence: 99%

Single-drone energy efficient coverage path planning with multiple charging stations for surveillance

Celik,

Ustaomer,

Satoglu

2023

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

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Drones have started to be used for surveillance within the cities, visually scanning the predefined zones, quickly detecting abnormal states such as fires, accidents, and pollution, or assessing the disaster zones. Coverage Path Planning (CPP) is a problem that aims to determine the most suitable path or motion plan for a vehicle to cover the entire desired area in the task. So, this paper proposes a novel two-dimensional coverage path planning (CPP) mathematical model with the fact that a single drone may need to be recharged within its route based on its energy consumption, and the obstacles must be avoided while constructing the route. Our study aims to create realistic routes for drones by considering multiple charging stations and obstacles for surveillance. We tested the model for a grid example based on the scenarios obtained by changing the layout, the number of obstacles and recharging stations, and area size using the Python Gurobi Optimization library. As a contribution, we analyzed the impact of the number of existing obstacles and recharging stations, the size and layout of the area to be covered on total energy consumption, and the total solution time of CPP in our study for the first time in the literature, through a detailed Scenario Analysis. Results show that the map size and the number of covered cells affect the total energy consumption, but different layouts with shuffled cells are not effective. The area size to be covered affects the total computation time, significantly. As the number of obstacles and recharging stations increases, the computation time decreases up to a certain limit, then stabilizes.

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

Cited by 12 publications

References 45 publications

Sensory System for Swarm Drone: A Systematic Review

Sensory System for Swarm Drone: A Systematic Review

Bio-Inspired Optimization-Based Path Planning Algorithms in Unmanned Aerial Vehicles: A Survey

Single-drone energy efficient coverage path planning with multiple charging stations for surveillance

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