A Case for a Battery-Aware Model of Drone Energy Consumption

Baek, Donkyu; Bocca, Alberto; Macii, Alberto; Macii, Enrico; Poncino, Massimo

doi:10.1109/intlec.2018.8612333

Cited by 41 publications

(26 citation statements)

References 14 publications

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“…The value of

should be equal to the sum of quantity delivered to customers by the k th UAV. In this paper, we simply consider the life span of the battery by limiting the range, but non-ideal discharge properties can affect for playing UAVs proposed in References [ 27 , 28 , 29 ], so it is important to take account of battery’s properties in future work. Lastly, additional constraints for ranges of decision variables are:

…”

Section: Unmanned Aerial Vehicles Routingmentioning

confidence: 99%

An Optimal Routing Algorithm for Unmanned Aerial Vehicles

Kim

Kwak

et al. 2021

Sensors

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A delivery service using unmanned aerial vehicles (UAVs) has potential as a future business opportunity, due to its speed, safety and low-environmental impact. To operate a UAV delivery network, a management system is required to optimize UAV delivery routes. Therefore, we create a routing algorithm to find optimal round-trip routes for UAVs, which deliver goods from depots to customers. Optimal routes per UAV are determined by minimizing delivery distances considering the maximum range and loading capacity of the UAV. In order to accomplish this, we propose an algorithm with four steps. First, we build a virtual network to describe the realistic environment that UAVs would encounter during operation. Second, we determine the optimal number of in-service UAVs per depot. Third, we eliminate subtours, which are infeasible routes, using flow variables part of the constraints. Fourth, we allocate UAVs to customers minimizing delivery distances from depots to customers. In this process, we allow multiple UAVs to deliver goods to one customer at the same time. Finally, we verify that our algorithm can determine the number of UAVs in service per depot, round-trip routes for UAVs, and allocate UAVs to customers to deliver at the minimum cost.

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“…The value of

…”

Section: Unmanned Aerial Vehicles Routingmentioning

confidence: 99%

An Optimal Routing Algorithm for Unmanned Aerial Vehicles

Kim

Kwak

et al. 2021

Sensors

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“…In the results presented so far the overall weight of the additional payload like measurement devices and radio relays was not taken into account. Additional payload decreases the drone’s flight speed and, as a result, the flight range is also decreased [ 27 ]. Although each of the considered drones has different characteristics like thrust, propellers diameter, weight, flight speed or battery capacity, a general dependence between flight range reduction and the drone’s payload can be derived.…”

Section: Flight Range Reduction In a Function Of Payload Massmentioning

confidence: 99%

Selection of Solar Powered Unmanned Aerial Vehicles for a Long Range Data Acquisition Chain

Woźniak

Jessa

2021

Sensors

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The paper presents a long range data acquisition chain operating in areas without access to the electricity grid or communication infrastructure built with unmanned aerial vehicles (UAVs). It is assumed that the length of the network chain significantly exceeds the flight range of a single drone. To build such a network three basic problems have to be solved. The first is energy harvesting for battery charging. The second concerns the choice of drone models that can cover a given distance in the shortest time. The third problem is the reduction of the flight range of drones as a function of payload mass. The evaluation of the proposed method is based on the results of simulations and cost analysis of 54 drones and 25 solar cells. The analysis ends with a proposition of seven steps that can help to choose the most suitable drone model for a given task.

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“…As the world becomes increasingly interconnected and technology-dependent, a new wave of smart applications is changing how we handle and confront everyday activities. The development of smart cities is going to play a vital role in sustainable development of the world and unmanned aerial vehicles (UAVs), also known as drones, are going to play a major role in it [2]. Nowadays, the efficiency of UAVs is undeniable.…”

Section: Introductionmentioning

confidence: 99%

A Generic Spatiotemporal Scheduling for Autonomous UAVs: A Reinforcement Learning-Based Approach

Bouhamed

Ghazzai

Besbes

et al. 2020

IEEE Open J. Veh. Technol.

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Considerable attention has been given to leverage a variety of smart city applications using unmanned aerial vehicles (UAVs). The rapid advances in artificial intelligence can empower UAVs with autonomous capabilities allowing them to learn from their surrounding environment and act accordingly without human intervention. In this paper, we propose a spatiotemporal scheduling framework for autonomous UAVs using reinforcement learning. The framework enables UAVs to autonomously determine their schedules to cover the maximum of pre-scheduled events spatially and temporally distributed in a given geographical area and over a predetermined time horizon. The designed framework has the ability to update the planned schedules in case of unexpected emergency events. The UAVs are trained using the Q-learning (QL) algorithm to find effective scheduling plan. A customized reward function is developed to consider several constraints especially the limited battery capacity of the flying units, the time windows of events, and the delays caused by the UAV navigation between events. Numerical simulations show the behavior of the autonomous UAVs for various scenarios and corroborate the ability of QL to handle complex vehicle routing problems with several constraints. A comparison with an optimal deterministic solution is also provided to validate the performance of the learning-based solution.

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A Case for a Battery-Aware Model of Drone Energy Consumption

Cited by 41 publications

References 14 publications

An Optimal Routing Algorithm for Unmanned Aerial Vehicles

An Optimal Routing Algorithm for Unmanned Aerial Vehicles

Selection of Solar Powered Unmanned Aerial Vehicles for a Long Range Data Acquisition Chain

A Generic Spatiotemporal Scheduling for Autonomous UAVs: A Reinforcement Learning-Based Approach

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