Collision-Free Multi-UAV Flight Scheduling for Power Network Damage Assessment

Ahmadian, Navid; Lim, Gino J.; Torabbeigi, Maryam; Kim, Seon Jin

doi:10.1109/icuas.2019.8797851

Cited by 10 publications

(3 citation statements)

References 15 publications

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“…Denis et al [15] proposed the network-centric multiagent system for real-time task scheduling of the UAV group, which achieved coordination and control of several UAVs in the group in order to perform joint tasks. Ahmadian et al [16] proposed a mixed-integer programming model to determine the optimal collision-free schedule for multiple UAVs. The approach reduced the possibility of collision between UAVs by creating a gap between their arrival intervals to each node.…”

Section: Related Work and Contributionsmentioning

confidence: 99%

Multiobjective Scheduling of Logistics UAVs Based on Variable Neighborhood Search

Yuan

Zhu

et al. 2020

Applied Sciences

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This study focuses on the issue of logistics Unmanned Aerial Vehicle (UAV) distribution in urban environment and an automatic delivery system to support the delivery of packages. It can effectively integrate existing facilities and be easily deployed. There is a scheduling problem in this system with multiple UAVs and multiple flights. We manage to optimize the two objectives of customer satisfaction and total completion time. The scheduling problem is formulated to a Mixed Integer Linear Programming (MILP), and we propose a multiple objectives decision-making method. A special encoding method suitable for the small scale problem is presented and Variable Neighborhood Search (VNS) algorithm framework is used to generate the approximate optimal solution for this problem. In experiments, we calibrate the important parameter and analyze the robustness of the algorithm. The experimental results show that the proposed algorithms are efficient for this problem.

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Section: Related Work and Contributionsmentioning

confidence: 99%

Multiobjective Scheduling of Logistics UAVs Based on Variable Neighborhood Search

Yuan

Zhu

et al. 2020

Applied Sciences

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“…These techniques provide effectiveness and immediate applicability, but may encounter difficulties in complex and dynamic situations. On the other hand, optimization-based algorithms approach obstacle avoidance as a problem of finding the best solution, using mathematical methods to calculate the most efficient path, while avoiding the obstacles [16,17].…”

Section: Introductionmentioning

confidence: 99%

QuickNav: An Effective Collision Avoidance and Path-Planning Algorithm for UAS

Debnath,

Hawary,

Ramdan

et al. 2023

Drones

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Obstacle avoidance is a desirable capability for Unmanned Aerial Systems (UASs)/drones which prevents crashes and reduces pilot fatigue, particularly when operating in the Beyond Visual Line of Sight (BVLOS). In this paper, we present QuickNav, a solution for obstacle detection and avoidance designed to function as a pre-planned onboard navigation system for UAS flying in a known obstacle-cluttered environment. Our method uses a geometrical approach and a predefined safe perimeter (square area) based on Euclidean Geometry for the estimation of intercepting points, as a simple and efficient way to detect obstacles. The square region is treated as the restricted zone that the UAS must avoid entering, therefore providing a perimeter for manoeuvring and arriving at the next waypoints. The proposed algorithm is developed in a MATLAB environment and can be easily translated into other programming languages. The proposed algorithm is tested in scenarios with increasing levels of complexity, demonstrating that the QuickNav algorithm is able to successfully and efficiently generate a series of avoiding waypoints. Furthermore, QuickNav produces shorter distances as compared to those of the brute force method and is able to solve difficult obstacle avoidance problems in fractions of the time and distance required by the other methods. QuickNav can be used to improve the safety and efficiency of UAV missions and can be applied to the deployment of UAVs for surveillance, search and rescue, and delivery operations.

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“…The heterogeneous multi‐drone routing optimisation with recharging consideration in disasters is presented in [29]. A multi‐drone flight scheduling was designed in [30] constrained to avoid collision between drones.…”

Section: Introductionmentioning

confidence: 99%

Budget‐constrained drone allocation for distribution system damage assessment

et al. 2021

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Natural disasters threaten the sustainability of electric power supply. This fact highlights the importance of enhancing technological resourcefulness to handle the upcoming events. Once a natural disaster occurs, the most urgent undertaking is to rapidly pinpoint and assess component damages and dispatch the repair crews towards the most critical impaired elements. Practical efforts confirm that utilising a drone, which is an unmanned aerial vehicle, can notably reduce the duration of post-disaster distribution system damage assessment (DA) and increase the resilience of power systems. This study presents an optimisation model to determine the optimal number and type of drones required for DA. The main goal is to minimise the time duration of the DA mission, which includes scanning the grid and identifying the damaged components. As a matter of fact, the financial resources are limited, so the problem is subject to the available budget constraint. Besides, the technical capacity of purchased drones for inspecting the grid is considered. The optimisation problem is formulated as a mixed-integer programme with a guaranteed optimum solution. Case studies confirm the applicability of the model and the optimality of the results. As the major conclusion, the best allocation is a compromise between the speed and price of drones.

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Collision-Free Multi-UAV Flight Scheduling for Power Network Damage Assessment

Cited by 10 publications

References 15 publications

Multiobjective Scheduling of Logistics UAVs Based on Variable Neighborhood Search

Multiobjective Scheduling of Logistics UAVs Based on Variable Neighborhood Search

QuickNav: An Effective Collision Avoidance and Path-Planning Algorithm for UAS

Budget‐constrained drone allocation for distribution system damage assessment

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