搜索结果：Drone

We consider a cellular network containing $n$ nodes where nodes within a cell gossip with each other in a fully-connected fashion and a source shares updates with these nodes via a mobile drone. The drone receives source updates and shares them with nodes in the cell where it currently resides. The drone moves between cells according to an underlying continuous-time Markov chain (CTMC). We evaluate the impact of the number of cells $f(n)$, drone speed $λ_m(n)$ and drone dissemination rate $λ_d(n)$ on the information freshness of nodes in the network. We use the version age of information metric to quantify information freshness. We observe that the expected duration between two drone-to-cell service times depends on the stationary distribution of the underlying CTMC and $λ_d(n)$, but not on $λ_m(n)$. However, the version age instability makes high probability analysis for a general underlying CTMC difficult. Therefore, we focus on the fully-connected drone mobility model. Under this model, we uncover a dual-bottleneck, by leveraging stochastic equivalence between drone mobility and drone dissemination speed: the version age is constrained by the slower of these two processes. If $λ

This paper proposes an Emergency Battery Service (EBS) for drones in which an EBS drone flies to a drone in the field with a depleted battery and transfers a fresh battery to the exhausted drone. The authors present a unique battery transfer mechanism and drone localization that uses the Cross Marker Position (CMP) method. The main challenges include a stable and balanced transfer that precisely localizes the receiver drone. The proposed EBS drone mitigates the effects of downwash due to the vertical proximity between the drones by implementing diagonal alignment with the receiver, reducing the distance to 0.5 m between the two drones. CFD analysis shows that diagonal instead of perpendicular alignment minimizes turbulence, and the authors verify the actual system for change in output airflow and thrust measurements. The CMP marker-based localization method enables position lock for the EBS drone with up to 0.9 cm accuracy. The performance of the transfer mechanism is validated experimentally by successful mid-air transfer in 5 seconds, where the EBS drone is within 0.5 m vertical distance from the receiver drone, wherein 4m/s turbulence does not affect the transfer process.

We demonstrate the peer-to-peer impact of drones flying in close proximity. Understanding these impacts is crucial for planning efficient drone delivery services. In this regard, we conducted a set of experiments using drones at varying positions in a 3D space under different wind conditions. We collected data on drone energy consumption traveling in a skyway segment. We developed a Graphical User Interface (GUI) that plots drone trajectories within a segment. The GUI facilitates analyzing the peer-to-peer influence of drones on their energy consumption. The analysis includes drones' positions, distance of separation, and wind impact.

Drones have been widely utilized in various fields, but the number of drones being used illegally and for hazardous purposes has increased recently. To prevent those illegal drones, in this work, we propose a novel framework for reconstructing 3D trajectories of drones using a single camera. By leveraging calibrated cameras, we exploit the relationship between 2D and 3D spaces. We automatically track the drones in 2D images using the drone tracker and estimate their 2D rotations. By combining the estimated 2D drone positions with their actual length information and camera parameters, we geometrically infer the 3D trajectories of the drones. To address the lack of public drone datasets, we also create synthetic 2D and 3D drone datasets. The experimental results show that the proposed methods accurately reconstruct drone trajectories in 3D space, and demonstrate the potential of our framework for single camera-based surveillance systems.

Vision-based drone-to-drone detection has attracted increasing attention due to its importance in numerous tasks such as vision-based swarming, aerial see-and-avoid, and malicious drone detection. However, existing methods often encounter failures when the background is complex or the target is tiny. This paper proposes a novel end-to-end framework that accurately identifies small drones in complex environments using motion guidance. It starts by creating a motion difference map to capture the motion characteristics of tiny drones. Next, this motion difference map is combined with an RGB image using a bimodal fusion module, allowing for adaptive feature learning of the drone. Finally, the fused feature map is processed through an enhanced backbone and detection head based on the YOLOv5 framework to achieve accurate detection results. To validate our method, we propose a new dataset, named ARD100, which comprises 100 videos (202,467 frames) covering various challenging conditions and has the smallest average object size compared with the existing drone detection datasets. Extensive experiments on the ARD100 and NPS-Drones datasets show that our proposed detector performs exceptional

Drones are begin used for various purposes such as border security, surveillance, cargo delivery, visual shows and it is not possible to overcome such intensive tasks with a single drone. In order to expedite performing such tasks, drone swarms are employed. The number of drones in a swarm can be high depending on the assigned duty. The current solution to authenticate a single drone using a 5G new radio (NR) network requires the execution of two steps. The first step covers the authentication between a drone and the 5G core network, and the second step is the authentication between the drone and the drone control station. It is not feasible to authenticate each drone in a swarm with the current solution without causing a significant latency. Authentication keys between a base station (BS) and a user equipment (UE) must be shared with the new BS while performing handover. The drone swarms are heavily mobile and require several handovers from BS to a new BS. Sharing authentication keys for each drone as explained in 5G NR is not scalable for the drone swarms. Also, the drones can be used as a UE or a radio access node on board unmanned aerial vehicle (UxNB). A UxNB may provide servi

This study introduces the Distributed Drone Reputation Management (DDRM) framework, designed to fortify trust and authenticity within the Internet of Drone Things (IoDT) ecosystem. As drones increasingly play a pivotal role across diverse sectors, integrating crowdsourced drone services within the IoDT has emerged as a vital avenue for democratizing access to these services. A critical challenge, however, lies in ensuring the authenticity and reliability of drone service reviews. Leveraging the Ethereum blockchain, DDRM addresses this challenge by instituting a verifiable and transparent review mechanism. The framework innovates with a dual-token system, comprising the Service Review Authorization Token (SRAT) for facilitating review authorization and the Drone Reputation Enhancement Token (DRET) for rewarding and recognizing drones demonstrating consistent reliability. Comprehensive analysis within this paper showcases DDRM's resilience against various reputation frauds and underscores its operational effectiveness, particularly in enhancing the efficiency and reliability of drone services.

Delivering a parcel from the distribution hub to the customer's doorstep is called the \textit{last-mile delivery} step in delivery logistics. In this paper, we study a hybrid {\it truck-drones} model for the last-mile delivery step, in which a truck moves on a predefined path carrying parcels and drones deliver the parcels. We define the \textsc{online drone scheduling} problem, where the truck moves in a predefined path, and the customer's requests appear online during the truck's movement. The objective is to schedule a drone associated with every request to minimize the number of drones used subject to the battery budget of the drones and compatibility of the schedules. We propose a 3-competitive deterministic algorithm using the next-fit strategy and 2.7-competitive algorithms using the first-fit strategy for the problem with $O(\log n)$ worst-case time complexity per request, where $n$ is the maximum number of active requests at any time. We also introduce \textsc{online variable-size drone scheduling} problem (OVDS). Here, we know all the customer's requests in advance; however, the drones with different battery capacities appear online. The objective is to schedule customer

Adaptive teaming-the capability of agents to effectively collaborate with unfamiliar teammates without prior coordination-is widely explored in virtual video games but overlooked in real-world multi-robot contexts. Yet, such adaptive collaboration is crucial for real-world applications, including border surveillance, search-and-rescue, and counter-terrorism operations. To address this gap, we introduce AT-Drone, the first dedicated benchmark explicitly designed to facilitate comprehensive training and evaluation of adaptive teaming strategies in multi-drone pursuit scenarios. AT-Drone makes the following key contributions: (1) An adaptable simulation environment configurator that enables intuitive and rapid setup of adaptive teaming multi-drone pursuit tasks, including four predefined pursuit environments. (2) A streamlined real-world deployment pipeline that seamlessly translates simulation insights into practical drone evaluations using edge devices and Crazyflie drones. (3) A novel algorithm zoo integrated with a distributed training framework, featuring diverse algorithms explicitly tailored, for the first time, to multi-pursuer and multi-evader settings. (4) Standardized evalu

We study last-mile delivery problems where trucks and drones collaborate to deliver goods to final customers. In particular, we focus on problem settings where either a single truck or a fleet with several homogeneous trucks work in parallel to drones, and drones have the capability of collaborating for delivering missions. This cooperative behaviour of the drones, which are able to connect to each other and work together for some delivery tasks, enhance their potential, since connected drone has increased lifting capabilities and can fly at higher speed, overcoming the main limitations of the setting where the drones can only work independently. In this work, we contribute a Constraint Programming model and a valid inequality for the version of the problem with one truck, namely the \emph{Parallel Drone Scheduling Traveling Salesman Problem with Collective Drones} and we introduce for the first time the variant with multiple trucks, called the \emph{Parallel Drone Scheduling Vehicle Routing Problem with Collective Drones}. For the latter variant, we propose two Constraint Programming models and a Mixed Integer Linear Programming model. An extensive experimental campaign leads to s

Drone-to-drone detection using visual feed has crucial applications, such as detecting drone collisions, detecting drone attacks, or coordinating flight with other drones. However, existing methods are computationally costly, follow non-end-to-end optimization, and have complex multi-stage pipelines, making them less suitable for real-time deployment on edge devices. In this work, we propose a simple yet effective framework, \textit{TransVisDrone}, that provides an end-to-end solution with higher computational efficiency. We utilize CSPDarkNet-53 network to learn object-related spatial features and VideoSwin model to improve drone detection in challenging scenarios by learning spatio-temporal dependencies of drone motion. Our method achieves state-of-the-art performance on three challenging real-world datasets (Average Precision@0.5IOU): NPS 0.95, FLDrones 0.75, and AOT 0.80, and a higher throughput than previous methods. We also demonstrate its deployment capability on edge devices and its usefulness in detecting drone-collision (encounter). Project: \url{https://tusharsangam.github.io/TransVisDrone-project-page/}.

The Unmanned Aerial Vehicles (UAVs) market has been significantly growing and Considering the availability of drones at low-cost prices the possibility of misusing them, for illegal purposes such as drug trafficking, spying, and terrorist attacks posing high risks to national security, is rising. Therefore, detecting and tracking unauthorized drones to prevent future attacks that threaten lives, facilities, and security, become a necessity. Drone detection can be performed using different sensors, while image-based detection is one of them due to the development of artificial intelligence techniques. However, knowing unauthorized drone types is one of the challenges due to the lack of drone types datasets. For that, in this paper, we provide a dataset of various drones as well as a comparison of recognized object detection models on the proposed dataset including YOLO algorithms with their different versions, like, v3, v4, and v5 along with the Detectronv2. The experimental results of different models are provided along with a description of each method. The collected dataset can be found in https://drive.google.com/drive/folders/1EPOpqlF4vG7hp4MYnfAecVOsdQ2JwBEd?usp=share_link

Currently, drone research and development has received significant attention worldwide. Particularly, delivery services employ drones as it is a viable method to improve delivery efficiency by using a several unmanned drones. Research has been conducted to realize complete automation of drone control for such services. However, regarding the takeoff and landing port of the drones, conventional methods have focused on the landing operation of a single drone, and the continuous landing of multiple drones has not been realized. To address this issue, we propose a completely novel port system, "EAGLES Port," that allows several drones to continuously land and takeoff in a short time. Experiments verified that the landing time efficiency of the proposed port is ideally 7.5 times higher than that of conventional vertical landing systems. Moreover, the system can tolerate 270 mm of horizontal positional error, +-30 deg of angular error in the drone's approach (+-40 deg with the proposed gate mechanism), and up to 1.9 m/s of drone's approach speed. This technology significantly contributes to the scalability of drone usage. Therefore, it is critical for the development of a future drone po

We introduce and study a new cooperative delivery problem inspired by drone-assisted package delivery. We consider a scenario where a drone, en route to deliver a package to a destination (a point on the plane), unexpectedly loses communication with its central command station. The command station cannot know whether the drone's system has wholly malfunctioned or merely experienced a communications failure. Consequently, a second, helper drone must be deployed to retrieve the package to ensure successful delivery. The central question of this study is to find the optimal trajectory for this second drone. We demonstrate that the optimal solution relies heavily on the relative spatial positioning of the command station, the destination point, and the last known location of the disconnected drone.

Drones are widely used in the energy, construction, agriculture, transportation, warehousing, real estate and movie industries. Key applications include surveys, inspections, deliveries and cinematography. With approximately 70-80% of the global market share of commercial off-the-shelf drones, Da-Jiang Innovations (DJI), headquartered in Shenzhen, China, essentially monopolizes the drone market. As commercial-off-the-shelf drone sales steadily rise, the Federal Aviation Administration has instituted regulations to protect the federal airspace. DJI has become a pioneer in developing remote identification technology in the form of drone ID (also known as AeroScope signals). Despite claims from the company touting its implementation of drone ID technology as "encrypted" yet later being proved incorrect for the claim, it remains a mystery on how one can grab and decode drone IDs over the air with low-cost radio frequency hardware in real-time. This research paper discusses a methodology using radio software and hardware to detect both Enhanced Wi-Fi and OcuSync drone IDs, the three types of drone ID packet structures and a functioning prototype of a DJI OcuSync detection system equippe

Semantic segmentation of drone images is critical for various aerial vision tasks as it provides essential semantic details to understand scenes on the ground. Ensuring high accuracy of semantic segmentation models for drones requires access to diverse, large-scale, and high-resolution datasets, which are often scarce in the field of aerial image processing. While existing datasets typically focus on urban scenes and are relatively small, our Varied Drone Dataset (VDD) addresses these limitations by offering a large-scale, densely labeled collection of 400 high-resolution images spanning 7 classes. This dataset features various scenes in urban, industrial, rural, and natural areas, captured from different camera angles and under diverse lighting conditions. We also make new annotations to UDD and UAVid, integrating them under VDD annotation standards, to create the Integrated Drone Dataset (IDD). We train seven state-of-the-art models on drone datasets as baselines. It's expected that our dataset will generate considerable interest in drone image segmentation and serve as a foundation for other drone vision tasks. Datasets are publicly available at \href{our website}{https://github

This paper reports a visible and thermal drone monitoring system that integrates deep-learning-based detection and tracking modules. The biggest challenge in adopting deep learning methods for drone detection is the paucity of training drone images especially thermal drone images. To address this issue, we develop two data augmentation techniques. One is a model-based drone augmentation technique that automatically generates visible drone images with a bounding box label on the drone's location. The other is exploiting an adversarial data augmentation methodology to create thermal drone images. To track a small flying drone, we utilize the residual information between consecutive image frames. Finally, we present an integrated detection and tracking system that outperforms the performance of each individual module containing detection or tracking only. The experiments show that even being trained on synthetic data, the proposed system performs well on real-world drone images with complex background. The USC drone detection and tracking dataset with user labeled bounding boxes is available to the public.

Wildlife monitoring with drones must balance competing demands: approaching close enough to capture behaviorally-relevant video while avoiding stress responses that compromise animal welfare and data validity. Human operators face a fundamental attentional bottleneck: they cannot simultaneously control drone operations and monitor vigilance states across entire animal groups. By the time elevated vigilance becomes obvious, an adverse flee response by the animals may be unavoidable. To solve this challenge, we present an edge-native, behavior-adaptive drone system for wildlife monitoring. This configurable decision-support system augments operator expertise with automated group-level vigilance monitoring. Our system continuously tracks individual behaviors using YOLOv11m detection and YOLO-Behavior classification, aggregates vigilance states into a real-time group stress metric, and provides graduated alerts (alert vigilance to flee response) with operator-tunable thresholds for context-specific calibration. We derive service-level objectives (SLOs) from video frame rates and behavioral dynamics: to monitor 30fps video streams in real-time, our system must complete detection and cla

The mainstream of educational robotics platforms orbits the various versions of versatile robotics sets and kits, while interesting outliers add new opportunities and extend the possible learning situations. Examples of such are reconfigurable robots, rolling sphere robots, humanoids, swimming, or underwater robots. Another kind within this category are flying drones. While remotely controlled drones were a very attractive target for hobby model makers for quite a long time already, they were seldom used in educational scenarios as robots that are programmed by children to perform various simple tasks. A milestone was reached with the introduction of the educational drone Tello, which can be programmed even in Scratch, or some general-purpose languages such as Node.js or Python. The programs can even have access to the robot sensors that are used by the underlying layers of the controller. In addition, they have the option to acquire images from the drone camera and perform actions based on processing the frames applying computer vision algorithms. We have been using this drone in an educational robotics competition for three years without camera, and after our students have develo