Drone Flight Research Articles

An integrative production-delivery system with mobile additive manufacturing and truck-drone delivery considering optimal printing sequence and preferred delivery time window

ABSTRACT Mobile additive manufacturing (MAM) involves deploying 3D printing in moving vehicles and has recently garnered significant attention from academia and industry. However, no prior work integrates MAM with truck-drone delivery systems to support daily operations, despite the benefits to supply chain management. This paper fills this gap by integrating these technologies, considering customer delivery time windows and optimal printing sequences. A mixed-integer linear programming model is proposed to establish this innovative mechanism. Both exact (CPLEX) and heuristic (Lagrangian relaxation algorithm) approaches are utilized and compared to solve the model. Computational studies validate the model and solution methods across various problem sizes (small, medium, and large), showing the superior performance of Lagrangian relaxation in optimality gap and CPU time. The model is then applied in a case study using the Lagrangian relaxation algorithm. Results indicate that drone flight range and printing job complexity significantly impact cost performance and on-time delivery. Beyond achieving timely and cost-effective customer satisfaction, this system offers insights into supply chain resilience, enabling companies to enhance service quality and reduce inventory dependence through on-demand spare part production via MAM technology.

Effect of drone flight speed on intensity images in drone-based synthetic aperture radar analysis of mine slopes

ABSTRACT Drone-based synthetic aperture radar (SAR) analysis offers a simple and efficient technique for acquiring observational data from various research areas. However, fundamental research on observation techniques, including the investigation of drone flight parameters, remains insufficient. This study focuses on improving the resolution of intensity images of a single-look complex (SLC) via drone-based SAR observations. By conducting experiments at different flight speeds and analysing the obtained SLC images, we demonstrated that higher drone speeds resulted in enhanced image resolution under the conditions investigated at flight speeds of ≤ 5 m s−1. A comparison with satellite SAR images highlighted the superior performance of drone-based SAR in capturing topographic features. The analysis also suggested that factors including trajectory deviation and Doppler rate affected the resolution and azimuth position. The simulation results supported the experimental findings on the relationship between drone speed and resolution. Our results emphasize the potential of drone-based SAR observations for monitoring slope failures and suggest the importance of the drone flight parameter selection. In the future, drone-based SAR is expected to produce intensity images of slopes and locate slope deformations associated with blasting for mining and earthquakes.

Fully neuromorphic vision and control for autonomous drone flight.

Biological sensing and processing is asynchronous and sparse, leading to low-latency and energy-efficient perception and action. In robotics, neuromorphic hardware for event-based vision and spiking neural networks promises to exhibit similar characteristics. However, robotic implementations have been limited to basic tasks with low-dimensional sensory inputs and motor actions because of the restricted network size in current embedded neuromorphic processors and the difficulties of training spiking neural networks. Here, we present a fully neuromorphic vision-to-control pipeline for controlling a flying drone. Specifically, we trained a spiking neural network that accepts raw event-based camera data and outputs low-level control actions for performing autonomous vision-based flight. The vision part of the network, consisting of five layers and 28,800 neurons, maps incoming raw events to ego-motion estimates and was trained with self-supervised learning on real event data. The control part consists of a single decoding layer and was learned with an evolutionary algorithm in a drone simulator. Robotic experiments show a successful sim-to-real transfer of the fully learned neuromorphic pipeline. The drone could accurately control its ego-motion, allowing for hovering, landing, and maneuvering sideways-even while yawing at the same time. The neuromorphic pipeline runs on board on Intel's Loihi neuromorphic processor with an execution frequency of 200 hertz, consuming 0.94 watt of idle power and a mere additional 7 to 12 milliwatts when running the network. These results illustrate the potential of neuromorphic sensing and processing for enabling insect-sized intelligent robots.

High-precision tracking and positioning for monitoring Holstein cattle.

Enhanced animal welfare has emerged as a pivotal element in contemporary precision animal husbandry, with bovine monitoring constituting a significant facet of precision agriculture. The evolution of intelligent agriculture in recent years has significantly facilitated the integration of drone flight monitoring tools and innovative systems, leveraging deep learning to interpret bovine behavior. Smart drones, outfitted with monitoring systems, have evolved into viable solutions for wildlife protection and monitoring as well as animal husbandry. Nevertheless, challenges arise under actual and multifaceted ranch conditions, where scale alterations, unpredictable movements, and occlusions invariably influence the accurate tracking of unmanned aerial vehicles (UAVs). To address these challenges, this manuscript proposes a tracking algorithm based on deep learning, adhering to the Joint Detection Tracking (JDT) paradigm established by the CenterTrack algorithm. This algorithm is designed to satisfy the requirements of multi-objective tracking in intricate practical scenarios. In comparison with several preeminent tracking algorithms, the proposed Multi-Object Tracking (MOT) algorithm demonstrates superior performance in Multiple Object Tracking Accuracy (MOTA), Multiple Object Tracking Precision (MOTP), and IDF1. Additionally, it exhibits enhanced efficiency in managing Identity Switches (ID), False Positives (FP), and False Negatives (FN). This algorithm proficiently mitigates the inherent challenges of MOT in complex, livestock-dense scenarios.

Plot-level satellite imagery can substitute for UAVs in assessing maize phenotypes across multistate field trials.

Abstract Accurate genotype-specific early yield estimates at fields and plots offer potential benefits to farmers in optimizing their agronomic practices, breeders in screening thousands of varieties, and policymakers in decision-making contributing to the improvement of agriculture and food production systems. Effective approaches to track plant growth and predict yield require large datasets of remote sensing and ground truth data collected across multiple environments. Low-altitude drone flights are increasingly being used to collect data from field evaluations of new crop varieties, while satellite imagery is being explored to track yield and management practices at the regional scales. Despite their lower spatial resolution, satellite platforms exhibit logistical and technical advantages in scalability and accessibility, and could facilitate plot-level predictions, especially with steadily improving spatial resolution. However, genotype-specific, plot-level, high-resolution satellite images from multiple environments with ground truth measurements are not yet publicly available. Here we generated, described, and evaluated over 20,000 plot-level images of over 80 hybrid maize varieties grown across the US corn belt under various management practices collected from (near simultaneous) satellite and drone flights integrated with ground truth yield measurement. Of the six baseline models examined, models employing data collected from satellite images often matched or exceeded the performance of models employing drone images for both within and cross-environment yield prediction. Large, multi-environment, genetically diverse datasets such as those generated in this study, along with more complex models could help unlock the power of satellite imagery as an important addition to the tool of farmers, plant geneticists, breeders, and policymakers.

Direction-finding for unmanned aerial vehicles using radio frequency methods

Nowadays, drones, also known as unmanned aerial vehicles (UAVs), are used for a wide range of purposes in the civil, industrial, and military domains. The greater the number of UAVs, the more serious the potential security risks correlated to them. To address these risks, it became necessary to develop systems that can detect, locate, and possibly neutralize such an object. The detection can be performed using different sensors such as imaging, radar, acoustic, and radio frequency (RF) sensors. The localization of the target drone, which can be done using direction finding (DF) methods, is an essential part of such a system. Thus, an RF-based DF testbed is designed and experimentally assessed in this paper. The testbed is implemented using software defined radio (SDR) platforms from the Universal Software Radio Peripheral (USRP) family as hardware equipment, and the MUSIC algorithm for processing the signals received on four different phase-coherent RF channels. A DJI Mavic Air UAV is used as target. For assessing the DF estimation performance, the direction obtained from the drone flight record is compared with the one estimated using the proposed DF testbed. Two different scenarios, a static one and a dynamic one, are considered to highlight the effectiveness of the proposed DF testbed across various conditions. The obtained results indicate an average estimation error of 1.15 degrees from the drone's actual position, in the case of the static scenario, and of 1.86 degrees in the case of the dynamic scenario, results which are on parity with or surpassed those of other drone DF systems described in the literature. The paper also outlines the benefits and challenges of the current approach.

Geomorphology of the Maladeta massif (Central Pyrenees): the traces of the last remaining glaciers

ABSTRACT We present a 1:15,000 geomorphological map of the Maladeta massif in the Central Pyrenees. Our methodology includes fieldwork, the analysis of aerial photographs from 1956 to 2015, and the use of drone flights from 2020 to 2023. The study area consists mainly of granodiorite but also has outcrops of limestone, shales, and quartzites at lower elevations. The landscape of the Maladeta massif is primarily shaped by glacial and periglacial processes, with fluvial, karstic, and hillslope dynamics manifesting in the lower regions. The assessment of moraine thickness has facilitated the determination of maximum glacier thickness during the Little Ice Age (LIA) and has shown that there is no correlation between ice thickness and glacier extent. After the LIA, the massif experienced a continuous glacier retreat. Currently, only 21.4% of the glacier area observed in 1956 remains.

Impact of climate change on underground transport infrastructure

Globally, underground transport infrastructures in many countries face significant challenges owing to extreme weather events, such as heavy rainfall, extreme temperature changes, and rising sea levels. Consequently, it is critical to perform a comprehensive examination of the diverse effects of these climatic phenomena on underground transport infrastructures and their ability to mitigate such consequences. This study focuses on assessing the impact of climate change on underground transport infrastructures using innovative monitoring tools. Wireless sensor networks (WSNs) and do it yourself (DIY) drones will be deployed to monitor these assets to detect potential problems at an early stage. This approach involves collecting real-time data through WSNs and capturing high-quality images using drone flights. The objective is to achieve an efficient response to extreme weather events and to enhance our understanding of the impact of climate change on underground transport infrastructures.

FEASIBILITY STUDY OF GNSS-ENABLED GCP MARKER FOR CONTROL ESTABLISHMENT FOR DRONE SURVEYING

Abstract. This study explores the potential of GNSS-enabled Ground Control Point (GCP) markers as an alternative to traditional survey-grade receivers in observing GCPs for drone surveys. This is because an average drone flight and the observation period for rapid static are both 15 minutes. A GNSS-enabled GCP is already in the market through Propeller Aeropoints but is not available in the Philippines. Previous independent studies on the accuracy of such markers are limited as it is only focused on the accuracy. There are other considerations in using this technique such as the practicality of the marker, its usability with public Continuously Operating Reference Station (CORS) networks such as PAGeNet, and whether using it in conjunction with passive monuments will significantly affect the accuracy. The researchers designed and assembled a low-cost alternative using a u-blox module to determine the markers’ applicability and effectiveness. This study was limited to using three (3) GCPs to simulate the minimum number required to georeference in 2D. Accuracy remains an important criterion so observations will be compared with known positions. Three drone surveys were conducted to further evaluate its performance. The marker achieved 3rd Order geodetic control and demonstrated up to one standard deviation of error using statistical analysis. However, the lack of control points affected the accuracy observed in the checkpoints. This study focused on low-cost GNSS-enabled GCP marker since the commercial Aeropoints markers are not vailable in the country. Implementing this methodology in drone surveying could potentially increase survey efficiency in the Philippines and aid in the country's cadastral mapping efforts.

Modeling waves of a strike drones swarm for a massive attack on enemy targets

This article solves the relevant task of studying military operations on a massive wave attack by a swarm of strike drones against enemy targets in a combat zone (CZ) of a military conflict. This research solves operational and tactical tasks for planning active actions on the battlefield by applying a massive strike on enemy targets by a swarm of combat drones (kamikaze drones). Therefore, the topic of the proposed publication is relevant; it analyzes and explores the sequence of logistical military actions to plan a massive attack using waves of strike drones. The purpose of this study is to create a set of models for applied information technology that will allow planning logistical actions for the effective use of a strike drone swarm to conduct a massive wave attack on enemy targets. This article analyzes problems associated with the formation of a swarm of drones, splitting the swarm into groups, and forming waves for active combat operations on the battlefield. Enemy targets will be attacked by strike drones in the waves formation, which makes it possible to carry out further successful operational and tactical actions. This study assesses the combat capability of a swarm of drones required to defeat enemy targets in a military conflict zone. It creates a systematic representation of the sequence of actions taken to plan a massive drone swarm attack, considering the combat capability and number of drones. With the help of military experts, the composition of the actual CZs is formed, in which the targets must be hit in the first place. The swarm of drones is rationally divided into groups that are directed to a set of targets in the CZs, considering the combat capability of drones to defeat targets. Waves of strike drones are planned to maximize damage to enemy targets. This research analyzes possible directions of drone movement toward enemy targets despite military threats. The models form flight routes of drones swarming to enemy targets under the conditions of possible anti-drone actions. An agent model was created using the Any Logic platform to simulate drone flight and form the routes of the drone swarm groups. This article presents an illustrated example of planning logistical actions to use waves of strike drone swarms for a massive attack on enemy targets. The scientific novelty of this study is related to solving the relevant problem of preparing and planning logistical actions for a massive attack on enemy targets using waves of strike drones by creating a set of optimization and simulation models that contribute to the effectiveness of further military attack operations on the battlefield. The results of the study could be used by military leaders to plan the use of a drone swarm to launch a massive attack on enemy targets in a military conflict zone.

A Self-Temperature Compensation Barometer Based on All-Quartz Resonant Pressure Sensor.

This paper reports a self-temperature compensation barometer based on a quartz resonant pressure sensor. A novel sensor chip that contains a double-ended tuning fork (DETF) resonator and a single-ended tuning fork (SETF) resonator is designed and fabricated. The two resonators are designed on the same diaphragm. The DETF resonator works as a pressure sensor. To reduce the influence of the temperature drift, the SETF resonator works as a temperature compensation sensor, which senses the instantaneous temperature of the DETF resonator. The temperature compensation method based on polynomial fitting is studied. The experimental results show that the accuracy is 0.019% F.S. in a pressure range of 200~1200 hPa over a temperature range of -20 °C~+60 °C. The absolute errors of the barometer are within ±23 Pa. To verify its actual performance, a drone flight test was conducted. The test results are consistent with the actual flight trajectory.

Synergistic morphology and feedback control for traversal of unknown compliant obstacles with aerial robots

Animals traverse vegetation by direct physical interaction using their entire body to push aside and slide along compliant obstacles. Current drones lack this interaction versatility that stems from synergies between body morphology and feedback control modulated by sensing. Taking inspiration from nature, we show that a task-oriented design allows a drone with a minimalistic controller to traverse obstacles with unknown elastic responses. A discoid sensorized shell allows to establish and sense contacts anywhere along the shell and facilitates sliding along obstacles. This simplifies the formalization of the control strategy, which does not require a model of the interaction with the environment, nor high-level switching conditions for alternating between pushing and sliding. We utilize an optimization-based controller that ensures safety constraints on the robot’s state and dampens the oscillations of the environment during interaction, even if the elastic response is unknown and variable. Experimental evaluation, using a hinged surface with three different stiffness values ranging from 18 to 155.5 N mm rad−1, validates the proposed embodied aerial physical interaction strategy. By also showcasing the traversal of isolated branches, this work makes an initial contribution toward enabling drone flight across cluttered vegetation, with potential applications in environmental monitoring, precision agriculture, and search and rescue.

MTL-PIE: A multi-task learning based drone pilot identification and operation evaluation scheme

As one of the most promising industries, consumer-grade Unmanned Aerial Vehicles (UAVs), also known as drones, have changed our lives. Although significant progress in drones has been made, adversary impersonation attacks still pose severe risks to flying drones. In addition, authorized pilot miss-operations also has become a critical factor leading to drone flight accidents. To validate the pilot's legal status and remind the authorized pilot about their miss-operations, we propose a multi-task learning-based drone pilot identification and operation evaluation scheme named MTL-PIE. Specifically, we first present qualitative and quantitative guidelines to evaluate pilot operation proficiency. Then, we design a pilot identification module and an operation evaluation module to resist pilot impersonation attacks and assess pilot operation proficiency, respectively. Finally, we propose a soft-parameter sharing mechanism to transfer knowledge between two modules and a dynamic weight-adjusting algorithm to prevent domain-dominant problems. Numerical results show that MTL-PIE can verify pilot legal status with an accuracy of 95.36% (outperforming our previous work with a margin of 2%-3%) and act as assessors to evaluate pilot operation proficiency with an accuracy of 94.47%. Note that MTL-PIE needs only 35 ms to verify pilot legal status and assess pilot operation proficiency; it has great potential to reduce drone flight accidents.

PEMANFAATAN LARGE LANGUAGE MODELS CHATGPT-4 UNTUK KONTROL DRONE: STUDI KASUS DJI TELLO

This study explores the potential of using ChatGPT-4, a Large Language Model (LLM) from OpenAI, for creating flight trajectories for drones. The primary focus is on the DJI Tello drone, which is controlled through Python code to execute various flying commands. Unlike traditional approaches that involve direct programming in Python, this research utilizes ChatGPT-4 to automatically generate Python computer programs capable of instructing the drone. Results indicate ChatGPT-4's intriguing capability to produce the necessary code for flying the drone according to given commands. This suggests that LLMs like ChatGPT-4 can be employed to determine drone flight trajectories using human language, facilitating ease of use compared to traditional computer programming languages.

Digital twin of multi-model drone detection system on Airsim for RF and vision modalities

Drones have become more prevalent in recent years and are used for both beneficial and malicious purposes. As a result, protecting restricted areas from unauthorized drone activities has become crucial. However, some researchers face challenges in developing drone detection systems due to the high costs of necessary equipment. This paper presents an innovative solution by creating an Airsim Graphical User Interface (GUI) tool compatible with the Unreal Engine. This tool enables the simulation of drone flights and creation of image and radio frequency (RF) datasets for drone detection in a simulation environment. Our approach involves modeling the measurement devices such as cameras to capture image data and software defined radio (SDR) receiver to capture RF signals as raw in-phase and quadrature (IQ) data. Moreover, users can manage automated route planning for drones, recording configurations, and different cameras and RF configurations. Researchers can now generate datasets with various images and RF configurations without the need for physical drones, cameras, or SDRs, enabling experimentation with different drone detection models. Furthermore, we proposed models for drone detection systems by using generated datasets from the proposed dataset generation system.

Evaluation of the Performance of Gabion Walls as a High-Energy Rockfall Protection System Using 3D Numerical Analysis: A Case Study

Cases of rockfalls that occurred on a slope in the Selendi District of Manisa Province are evaluated in this article. Field studies are evaluated and different measures are examined to reduce rockfall risk. Drone flights are used to evaluate previous studies and obtain 1/1000 scale digital topographic maps. These maps are used to create a 3D (three-dimensional) solid model of the project site, and on-site surveys are conducted to identify source rock locations and free blocks that pose a risk. Those 3D analyses are used to determine the paths, jump heights, and energies of the blocks in motion. The data from the 3D maps are used to determine the most appropriate remediation methods. The structural behavior of the recommended gabion wall, which is designed at a certain height and width as a result of rockfalls, has been examined. Structural behavior is determined by simulation based on the finite element model. Within the scope of this study, the ANSYS Workbench program is used. The “Explicit Dynamics” analysis type in ANSYS Workbench was chosen to examine the rockfall effect.

Learning Hybrid Policies for MPC with Application to Drone Flight in Unknown Dynamic Environments

In recent years, drones have found increased applications in a wide array of real-world tasks. Model predictive control (MPC) has emerged as a practical method for drone flight control, owing to its robustness against modeling errors/uncertainties and external disturbances. However, MPC’s sensitivity to manually tuned parameters can lead to rapid performance degradation when faced with unknown environmental dynamics. This paper addresses the challenge of controlling a drone as it traverses a swinging gate characterized by unknown dynamics. This paper introduces a parameterized MPC approach named hyMPC that leverages high-level decision variables to adapt to uncertain environmental conditions. To derive these decision variables, a novel policy search framework aimed at training a high-level Gaussian policy is presented. Subsequently, we harness the power of neural network policies, trained on data gathered through the repeated execution of the Gaussian policy, to provide real-time decision variables. The effectiveness of hyMPC is validated through numerical simulations, achieving a 100% success rate in 20 drone flight tests traversing a swinging gate, demonstrating its capability to achieve safe and precise flight with limited prior knowledge of environmental dynamics.

Verification of drone formation relative positioning technology based on Beidou

High precision drone formation flight requires real-time knowledge of the three-dimensional position and velocity information of the carrier. The Beidou navigation system, due to its all-weather and high-precision characteristics, can provide good relative position results for drones. However, when drones move at high speeds and in complex environments, The Beidou navigation receiver may experience various errors, leading to poor positioning results or even inability to locate. This paper provides targeted technical design for the reliability of Beidou relative positioning in complex environments during drone flight, monitors navigation data in real time to improve the fixed reliability of overall ambiguity, and uses BeiDou signal simulator to simulate and generate observation data for analysis and processing. The test results show that this method improves the continuity and reliability of the fixed ambiguity of Beidou, and can meet the requirements of real-time formation flight of drone.

Using Eye-Tracking for Adaptive Human-Machine Interfaces for Pilots: A Literature Review and Sample Cases

This paper explores the potential of eye-tracking technology in adaptive human-machine interfaces for pilots in aviation. We argue that an interface able to adjust its layout and elements based on pilots’ real-time eye-tracking data can prevent errors and enhance their performance. The study presents a literature review on the use of eye-tracking for various pilot cases, including flight simulator games, drone pilots, and cockpit pilots. Results in most cases showed that eye-tracking has been employed to improve interactions, enhance spatial awareness, guide pilots’ gaze to relevant areas, and provide insights into pilots’ information processing and task load. The paper discusses two sample cases demonstrating the potential of eye-tracking in adaptive human-machine interfaces. In the first case, during challenging drone simulations, eye-tracking identified areas where an adaptive human-machine interface could aid navigation and reduce cognitive load. In the second one, based on real drone flights, when signal loss incidents occurred, eye-tracking data showed that the interface should adapt to pilots’ needs by providing critical information to help them to improve situational awareness. The paper concludes that eye-tracking technology has significant potential in adaptive human-machine interfaces for aviation, emphasising the importance of refining these technologies to meet pilots’ specific needs and enhance flight safety.

Automatic Detection of Abnormal Drone Flight State by Measuring Changes in Images Captured by a Drone-Mounted Camera

Automatic Detection of Abnormal Drone Flight State by Measuring Changes in Images Captured by a Drone-Mounted Camera