Battery-aware energy model of drone delivery tasks

Drones are becoming increasingly popular in the commercial market for various package delivery services. In this scenario, the mostly adopted drones are quad-rotors (i.e., quadcopters). The energy consumed by a drone may become an issue, since it may affect (i) the delivery deadline (quality of service), (ii) the number of packages that can be delivered (throughput) and (iii) the battery lifetime (number of recharging cycles). It is thus fundamental try to find the proper compromise between the energy used to complete the delivery and the speed at which the quadcopter flies to reach the destination. In order to achieve this, we have to consider that the energy required by the drone for completing a given delivery task does not exactly correspond to the energy requested to the battery, since the latter is a non-ideal power supply that is able to deliver power with different efficiencies depending on its state of charge. In this paper, we demonstrate that the proposed battery-aware delivery scheduling algorithm carries more packages than the traditional delivery model with the same battery capacity. Moreover, the battery-aware delivery model is 17% more accurate than the traditional delivery model for the same delivery scheme, which prevents the unexpected drone landing

A case for a battery-aware model of drone energy consumption

The market of small drones has been recently increasing due to their use in many fields of application. The most popular drones are multirotors, in particular quadcopters. They are usually supplied with batteries of limited capacity, and for this reason their total flight time is also limited.As a consequence of the non linear characteristics of batteries, estimation of the real flight time may become an issue, since most battery models do not include all the non idealities. Consequently, applications such as delivery service, search and rescue, surveillance might not be accomplished correctly because of inaccurate energy estimations.This paper describes a battery-aware model for an accurate analysis of the drone energy consumption; this model is then applied to a scenario of drone delivery. Results show an accuracy greater of about 16% with respect to the traditional estimation model

Energy-efficient coordinated electric truck-drone hybrid delivery service planning

Recent works have shown that a coordinated delivery strategy in which a drone collaborates with a truck using it as a moving depot is quite effective in improving the performance and energy efficiency of the delivery process. As most of these works come from the research community of logistics and transportation, they are instead focused on the optimality of the algorithms, and neglect two critical issues: (1) they consider only a planar version of the problem ignoring the geographic information along the delivery route, and (2) they use a simplified battery model, truck, and drone power consumption model as they are mostly focused on optimizing delivery time alone rather than energy efficiency.In this work, we propose a greedy heuristic algorithm to deter-mine the most energy-efficient sequence of deliveries in which a drone and an EV truck collaborate in the delivery process, while accounting for the two above aspects. In our scenario, a drone delivers packages starting from the truck and returns to the truck after the delivery, while the truck continues on its route and possibly delivers other packages. Results show that, by carefully using the drone’s energy along the truck delivery route, we can achieve 43-69% saving of the truck battery energy on average over a set of different delivery sets and different drone battery sizes. We also compared two "common-sense" heuristics, concerning which we saved up to 42%

Resilient Drone Mission Management and Route Optimization in Drone Delivery Context

The last two decades were characterized by a rapidly increasing of innovative solutions in the microelectronic field, having therefore a significant impact on a huge set of applicative scenarios. This aspect allows the development and improvement of new solutions, giving the possibility of growth and development of new markets, such as the drones ones. Actually, in the unmanned field we have seen an exponential growth of the market, given not only from the increased computing capabilities, but also by a more efficient developed hardware, thus leading to the definition of innovative uses, service paradigms and applications. The latter span in several different areas, from agriculture monitoring to society's services including the Package Delivery which immediately plays a strategic role in the modern society. These types of applications took place mainly in an urban environment, highlighting therefore new rules, needs and management system in order to accommodate the mission's achievement guaranteeing at the same time a high degree of resilience, citizen safety and risks minimization. Furthermore, to assist these types of operations, T-DROMES, a RPAS (Remotely Piloted Aerial Systems) fleet and mission management solution, was developed allowing to scale-up the use of drones in complex operations from a geographical and mission point of view, in different applicative scenarios. The paper aims therefore to presents the tools capabilities and how the developed architecture is able to manage the entire mission for any context scenario and how the developed platforms and tools can be a valid framework for developing new operative working models

An Overview of Drone Energy Consumption Factors and Models

At present, there is a growing demand for drones with diverse capabilities that can be used in both civilian and military applications, and this topic is receiving increasing attention. When it comes to drone operations, the amount of energy they consume is a determining factor in their ability to achieve their full potential. According to this, it appears that it is necessary to identify the factors affecting the energy consumption of the unmanned air vehicle (UAV) during the mission process, as well as examine the general factors that influence the consumption of energy. This chapter aims to provide an overview of the current state of research in the area of UAV energy consumption and provide general categorizations of factors affecting UAV's energy consumption as well as an investigation of different energy models

Auction-based Task Allocation for Safe and Energy Efficient UAS Parcel Transportation

In this paper, two greedy auction-based algorithms are proposed for the allocation of heterogeneous tasks to a heterogeneous fleet of UAVs. The tasks set is composed of parcel delivery tasks and charge tasks, the latter to guarantee service persistency. An optimization problem is solved by each agent to determine its bid for each task. When considering delivery tasks, the bidder aims at minimizing the energy consumption, while the minimization of the flight time is adopted for charge tasks bids. The algorithms include a path planner that computes the minimum risk path for each task-UAV bid exploiting a 2D risk map of the operational area, defined in an urban environment. Each solution approach is implemented by means of two auction strategies: single-item and multiple-item. Considerations about complexity and efficiency of the algorithms are drawn from Monte Carlo simulations

Intelligent and secure fog-aided internet of drones

Internet of drones (IoD), which utilize drones as Internet of Things (IoT) devices, deploys several drones in the air to collect ground information and send them to the IoD gateway for further processing. Computing tasks are usually offloaded to the cloud data center for intensive processing. However, many IoD applications require real-time processing and event response (e.g., disaster response and virtual reality applications). Hence, data processing by the remote cloud may not satisfy the strict latency requirement. Fog computing attaches fog nodes, which are equipped with computing, storage and networking resources, to IoD gateways to assume a substantial amount of computing tasks instead of performing all tasks in the remote cloud, thus enabling immediate service response. Fog-aided IoD provisions future events prediction and image classification by machine learning technologies, where massive training data are collected by drones and analyzed in the fog node. However, the performance of IoD is greatly affected by drones\u27 battery capacities. Also, aggregating all data in the fog node may incur huge network traffic and drone data privacy leakage. To address the challenge of limited drone battery, the power control problem is first investigated in IoD for the data collection service to minimize the energy consumption of a drone while meeting the quality of service (QoS) requirements. A PowEr conTROL (PETROL) algorithm is then proposed to solve this problem and its convergence rate is derived. The task allocation (which distributes tasks to different fog nodes) and the flying control (which adjusts the drone\u27s flying speed) are then jointly optimized to minimize the drone\u27s journey completion time constrained by the drone\u27s battery capacity and task completion deadlines. In consideration of the practical scenario that the future task information is difficult to obtain, an online algorithm is designed to provide strategies for task allocation and flying control when the drone visits each location without knowing the future. The joint optimization of power control and energy harvesting control is also studied to determine each drone\u27s transmission power and the transmitted energy from the charging station in the time-varying IoD network. The objective is to minimize the long-term average system energy cost constrained by the drones\u27 battery capacities and QoS requirements. A Markov Decision Process (MDP) is formulated to characterize the power and energy harvesting control process in time-varying IoD networks. A modified actor-critic reinforcement learning algorithm is then proposed to tackle the problem. To address the challenge of drone data privacy leakage, federated learning (FL) is proposed to preserve drone data privacy by performing local training in drones and sharing training model parameters with a fog node without uploading drone raw data. However, drone privacy can still be divulged to ground eavesdroppers by wiretapping and analyzing uploaded parameters during the FL training process. The power control problem of all drones is hence investigated to maximize the FL system security rate constrained by drone battery capacities and the QoS requirements (e.g., FL training time). This problem is formulated as a non-linear programming problem and an algorithm is designed to obtain the optimum solutions with low computational complexity. All proposed algorithms are demonstrated to perform better than existing algorithms by extensive simulations and can be implemented in the intelligent and secure fog-aided IoD network to improve system performances on energy efficiency, QoS, and security