Unmanned aerial vehicles supporting imagery intelligence using the structured light technology

Unmanned Aerial Vehicles (UAVs) are used in various applications, including crowd management, crime prevention, accident detection, and rescue operations. However, since UAVs perform their tasks independently, some UAV applications are dynamic and geographically distributed, which may require extensive real-time processing capabilities. Thus, processing UAV data locally can be challenging due to their limited computing capabilities. To overcome such limitations, fog and cloud computing can facilitate UAV application development by providing additional resource capacities when needed. Despite this, designing sophisticated and efficient UAV task offloading strategies that collaborate with fog and cloud technologies considering their service latency and energy consumption, is rarely addressed in the literature. Therefore, a collaborative offloading strategy for UAV applications is presented in this work, leveraging fog and cloud computing advantages and capabilities. This approach aims to minimize UAVs’ service latency and energy consumption, as well as provide the required resources and services in real time. In addition, task offloading decisions are formulated using the Mixed-Integer Linear Programming (MILP) model to reduce the energy consumption of the entire UAV-fog-cloud system by optimizing the allocation of computation resources and communication requested by each UAV. The simulation results demonstrate that the proposed strategy can significantly reduce UAV service latency by 15.38%, 35.29%, and 59.26%, as well as decrease overall energy consumption (including processing and networking) by 3.3%, 7.37%, and 12% when compared to alternative standalone strategies (namely UAV, fog, and cloud).

The current research represents a first step towards developing a decentralized network of small autonomous, intelligent and inexpensive unmanned aerial vehicles (UAV), which could be used for a variety of scientific missions where measurements from a distributed network of nodes could significantly improve the assessment. The paper presents an adaptive airborne sensing platform (e.g., UAV) that could be used to fuse the information acquired through multiple sensing modalities (obtained from sensors located close to or inside the phenomena) to monitor environmental processes over space and time. The sensing platform is able to continuously reconfigure its trajectory according to the circumstances (e.g., continuously evolving scientific phenomena) to optimize the location of individual sensing waypoints. As proof of concept and validation, we will apply the proposed sensing approach to monitoring volcanic plumes. The paper focuses on (i) developing a Bayesian statistical method to create sensor fusion algorithms that is used to model the plume and guide the UAV in an optimal way (ii) implementing a distributed task allocation scheme which is able guide all the UAVs efficiently and optimize the UAV paths for various criteria.

As the application areas of unmanned aerial vehicles (UAVs) continue to expand, the importance of UAV task allocation becomes increasingly evident. A highly effective and efficient UAV task assignment method can significantly enhance the quality of task completion. However, traditional heuristic algorithms often perform poorly in complex and dynamic environments, and existing auction-based algorithms typically fail to ensure optimal assignment results. Therefore, this paper proposes a more rigorous and comprehensive mathematical model for UAV task assignment. By introducing task path decision variables, we achieve a mathematical description of UAV task paths and propose collaborative action constraints. To balance the benefits and efficiency of task assignment, we introduce a novel method: the Adaptive Sampling-Based Task Rationality Review Algorithm (ASTRRA). In the ASTRRA, to address the issue of high-value tasks being easily overlooked when the sampling probability decreases, we propose an adaptive sampling strategy. This strategy increases the sampling probability of high-value targets, ensuring a balance between computational efficiency and maximizing task value. To handle the coherence issues in UAV task paths, we propose a task review and classification method. This method involves reviewing issues in UAV task paths and conducting classified independent auctions, thereby improving the overall task assignment value. Additionally, to resolve the crossover problems between UAV task paths, we introduce a crossover path exchange strategy, further optimizing the task assignment scheme and enhancing the overall value. Experimental results demonstrate that the ASTRRA exhibits excellent performance across various task scales and dynamic scenarios, showing strong robustness and effectively improving task assignment outcomes.

This paper presents the relationship between energy and signal integrity of Unmanned Aerial Vehicles (UAVs) with Synthetic Aperture Radar (SAR) capabilities for a more informed off-the-shelf selection design. The energy is expressed in the UAV and SAR power consumption, whereas signal integrity is expressed in Bit-Error Rate (BER). Low-end, mid-end, and highend—categorized in terms of their market cost—commercial UAVs and SAR modules were considered. The energy relations are based on the UAV flight path and power ratings. The signal processing and decomposition of the reconstructed objects for the model simulations are based on a transmit channel-receive chain of 16-quadrature-amplitude, low-density parity check, Rayleigh response, and single-antenna directivity done through Nvidia Sionna. The results indicate that far-reaching flight spatial path scanning is achieved with high-end UAVs and low-cost SARs, but the accuracy is relatively low. Higher accuracy is achieved with low-end UAVs with high-cost SARs. Further, the results point out that selecting UAV-SAR is critical in terms of energy and accuracy for a specific target application. The outcomes show tradeoffs in the selection design.

Synthetic Aperture Radar (SAR) systems deployed on Unmanned Aerial Vehicles (UAVs) are emerging as a highly promising solution for a wide range of future remote sensing and monitoring applications. The integration of SAR’s well-established capabilities for high-resolution imaging and change detection with the flexibility, maneuverability, and cost-effectiveness of modern UAV platforms unlocks significant potential for innovative sensing missions. However, the practical implementation of SAR on UAVs presents considerable challenges, primarily due to the inherent limitations in UAV flight stability and precise navigation, which degrade image quality. As a result, robust autofocusing techniques are essential to correct motion-induced phase errors and ensure coherent image reconstruction. While numerous autofocusing methods have been proposed in the literature, most are designed under assumptions that do not hold for low-flying UAV platforms. Specifically, conventional algorithms often overlook the strong dependence of the SAR imaging geometry on both azimuth and range positions of the targets, which becomes pronounced at low altitudes. In this study, we introduce a novel autofocusing approach termed Geometry-Aware Entropy Minimization (GAME). The proposed algorithm reconstructs three-dimensional positioning errors by explicitly modeling the spatially variant acquisition geometry along the UAV trajectory. This geometry-aware formulation enables accurate autofocus correction under the challenging conditions associated with low-altitude UAV SAR acquisitions. The effectiveness of the GAME algorithm is demonstrated using real X-band SAR data collected by an octacopter platform, confirming its applicability and performance in practical scenarios.

In unmanned aerial vehicle (UAV) synthetic aperture radar (SAR), step-frequency mode is usually used due to the limitation of size and weight. Due to the small size and weight, the trajectory deviation is so serious that the motion compensation becomes very complex. In order to estimate and compensate the motion error in UAV SAR, a two-step motion compensation approach is proposed in this paper. Firstly, the global positioning system (GPS) and inertial navigation system (INS) data is used for the coarse compensation to eliminate the major motion error. Secondly, fine phase error compensation based on raw SAR data is processed to circumvent the residual phase error. The fine phase error is estimated by weighted phase gradient autofocus (WPGA) in each sub-pulse of step-frequency mode. Then the weight least square (WLS) method is used to obtain the final focusing phase from the estimated phase of sub-pulse. Real data experiments show that the proposed method is suitable for UAV SAR motion compensation.

Forward velocity extraction is a very important process for obtaining a high-quality unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) image. Because of the constraints of low flying altitude and small platform size, the flight path of the UAV is easily disturbed by the atmospheric turbulence. The complex motion error of the UAV's flight path makes the forward velocity difficult to be extracted from raw SAR data. To address this problem, an adaptive notch filtering (ANF)-based approach for forward velocity extraction is proposed. Based on the kinetic characteristics of the UAV, the variation of Doppler centroid frequency is analyzed and exploited to remove most components of the cross-track acceleration in the low-frequency range. Then, by regarding the forward velocity component as a narrow-band component, ANF processing is employed to extract it from the estimated Doppler rate. Comparing with the methods reported in the literature, the ANF method can achieve higher accuracy and efficiency due to its excellent notching performance and strong suppression for narrow-band signals. Promising results from raw data experiments are presented to demonstrate the validity and superiority of the proposed method.

Unmanned aerial vehicles (UAVs) play an essential role in emergency cases and adverse environments for applications like disaster detection and mine exploration. To process the massive volume of sensing data generated by various sensory payloads in these applications, existing works either compress deep learning (DL) models to conduct onboard computing, or offload raw data back to the resourceful ground station with the help of relay UAVs due to base station damage. However, the former sacrifices the inference accuracy of DL models (up to 10% accuracy loss), while the latter achieves high accuracy at the cost of significant latency, due to limited wireless communication resources in the multi-hop transmission. To address the problem, exploiting the resources of the UAV swarm including both task UAVs and relay UAVs, we build up an <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u>ll-<underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</u>ky <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u>utonomous com<underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</u>uting (ASAP) system to autonomously conduct collaborative computing in the swarm, to achieve both high accuracy and low latency of sensing data processing. In detail, we first propose a novel UAV swarm-native collaborative computing architecture, considering the general hierarchy and clustering structure of UAV swarms, as well as the characteristic of DL model execution. We then design an elastic efficient task scheduler to allocate computing tasks for UAVs, and update the scheduling scheme online when some UAVs are unavailable, with the aid of a lightweight and accurate DL inference performance predictor. Finally, we design an adaptive inter-UAV data compressor, to adapt to the limited and dynamic communication resources between UAVs. Experiment results on 24 airborne computers and five real-world UAVs show that, the proposed system can perform collaborative computing in a timely manner and effectively deal with situations when some UAVs become unavailable.

Leveraging their inherent mobility and rapid deployment capabilities, Unmanned Aerial Vehicles (UAVs) can deliver computing services to ground devices with enhanced speed and flexibility. Consequently, UAV-based Mobile Edge Computing (MEC) networks have garnered significant research interest and attention. However, in contrast to ground edge servers, UAVs functioning as computing nodes face a unique set of challenges. Their decision-making processes must not only address user computational demands but also concurrently consider critical factors such as energy consumption and optimal deployment strategies. To tackle these challenges, we introduce a multi-UAV deployment and cooperative scheduling strategy designed to comprehensively optimize deployment locations, task latency, overall energy consumption, and the load distribution across the UAV network. Specifically, we decompose the overarching optimization problem into two distinct sub-problems. For the deployment problem, we propose a clustering algorithm rooted in the modularity-improving Louvain method, aiming to minimize both mobile device transmission power and UAV energy consumption. Then, we present a reinforcement learning-based algorithm capable of making dynamic scheduling decisions that adapt to system fluctuations. Finally, we validate the practicality and effectiveness of our integrated deployment and scheduling scheme through rigorous experimental evaluation.

Recent advancements in technology have led to a great interest in the use of Unmanned Aerial Vehicles (UAVs) for a vast array of applications such as real time site monitoring, target search and destroy and UAVs being used as mobile sinks to collect data from Internet of Things (IoT) devices. This is mainly due to their autonomy, high mobility, ease of deployment and affordable nature. A group of UAVs can be used collectively to bring a coordinated effort in task execution, allowing more tasks to be completed in a wider area and in the shortest possible time. However, using multiple UAVs presents some challenges for efficient cooperation. UAVs are resource constrained due to being battery powered and this limits the permissible flight time. Therefore, it is necessary to intelligently manage their operation given the limited resources and other constraints associated with the mission. In this paper, we propose a multi-objective UAV task assignment model to support spatio-temporally distributed events raised by static IoT devices, using a discrete Non- Dominated Sorting Genetic Algorithm II (NSGA-II). This model assigns the most suitable UAV(s) to serve at the different event locations ensuring that none of the constraints are violated. The performance of the algorithm was evaluated through numerical simulations and compared to a similar implementation using Mixed Integer Linear Programming (MILP). Results show an improvement of 7.9% in the total energy consumption for all UAVs while ensuring that all the temporal constraints are not violated.

Unmanned aerial vehicle (UAV) which carries a synthetic aperture radar (SAR) to obtain high-resolution images are used by more and more scenes. However, due to the light weight and small size UAV platform, the UAV SAR system is very sensitive to turbulence, and the resulting motion impact will give the UAV an unknown force to change the acceleration in the azimuth direction, resulting in the UAV non-uniform motion in the azimuth direction. This makes the azimuth sampling position of UAV's non-uniform, resulting in azimuth ambiguity in the imaging results. In this paper, we use an equivalent alternative method, in which the azimuthal nonuniform motion of UAV caused by turbulence is equivalent to azimuthal nonuniform sampling. In this paper, the non-uniform fast Fourier transform (NUFFT) and omega-k algorithm is used to simulate the model.

The capability of imaging and surveying ground areas with airborne and spaceborne sensors has a very high priority in many applications, both in the civilian and military sectors. One of the most essential uses of this capability is in disaster monitoring, where up-to-date, reliable images and data are vital for the undertaking and coordinating of rescue actions. Sensors in space are very accurate but typically have a long revisit time, and thus their use for continuous monitoring is limited. Manned aircrafts equipped with optical, infrared, and synthetic aperture radar (SAR) are costly, and their operation in poor weather conditions (like heavy storms) are limited due to concerns over pilot safety. Unmanned aerial vehicles (UAV) are ideal candidates for the safe and cheap surveillance and imaging of such areas. They can provide continuous monitoring at very low cost, be deployed very quickly, and pose no risk of the loss of life in the case of a platform malfunction caused by heavy weather conditions, technical problems, or human error. The disadvantage of small, unmanned platforms is that they only offer limited space for payloads and have a low electrical power supply. This lays down the conditions for the design of an airborne sensor for small- and medium-sized UAVs. The cheapest solution for UAV surveillance is the use of visual light cameras. Optical cameras are cheap, light, and require a low supply of power, but their usage is limited to the daytime and good weather conditions. The presence of heavy rain, clouds, fog, or smoke can significantly reduce their imaging distance, sometimes down to just a few meters. Far better results can be obtained using much longer electromagnetic waves. The use of far infrared (e.g. in the 10 μm region) can provide thermal information and can be used for the detection of people in both day and night conditions, but it does not provide satisfactory images of land and infrastructures. The use of millimeter and centimeter microwaves with active illumination, combined with SAR technology, can provide high resolution images in all weather conditions, day and night, at a distance of several kilometers.

무인항공기는 전투뿐만 아니라, 정찰, 관측 탐사 등 여러 분야에서 이용할 수 있고, 전천후 영상 수집이 가능한 SAR(Synthetic Aperture Radar) 기술과 함께 기존의 감시정찰 체계가 수행할 수 없던 임무영역까지로 그 능력이 확장될 것으로 보인다. 오늘날, 고효율 집적기술의 발전과 시스템 경량화 기술의 발전에 힘입어 무인항공기에도 경량의 SAR Sensor를 탑재하려는 연구와 수요가 증가하고 있다. 이에 따라, 본 논문에서는 SAR 시스템을 구성하는 핵심 모듈인 광대역 첩신호발생기를 DDS 디지털 소자 기반으로 개발하여 무인항공기용 신호 발생기와 송수신장비의 개발과정 및 결과를 기술하였다. UAVs(Unmanned Aerial Vehicle) can be used in variant fields fornot only combat, but also recon, observation and exploration. Moreover, UAVs capacity can be expanded to impossible missions for existing surveillance system such as SAR(Synthetic Aperture Radar) technology that collecting images from all weather conditions. In recent days, with development of highly efficient IC and lightened system technology, there are significant increase of researches and demands to make SAR sensor as a payload of UAV. Therefore, this paper contains development process and results of small signal generator and RF device as a core module of SAR system based on the digital device of DDS.

In recent years, the utilization of unmanned aerial vehicles (UAVs) has surged in a variety of applications, including weather monitoring, emergency search and rescue operations, and smart agriculture. In these applications, UAVs are instrumental in executing data collection tasks. However, due to the scarcity of resources such as battery capacity, the duration of work is limited, necessitating the optimization of UAV trajectories and resource allocation. In this paper, we introduce a hierarchical task scheduling and resource allocation scheme for UAV data collection tasks, which incorporates the deep reinforcement learning (DRL) technology into a two-layer training framework. The continuous actions, including UAV trajectories, UAV communication power, and UAV CPU main frequency, are first optimized in the lower layer, while the upper one concentrates on the discrete action space for the target allocation. In particular, the well-trained lower-layer network is integrated into the upper layer, facilitating rapid global reward feedback between the two-layer network and accelerating the training process of the upper-layer network. Experimental results demonstrate that the proposed approach achieves superior performance than the baseline methods in terms of the amount of collected data as well as UAV energy consumption.

With the ongoing advancement of electric power Internet of Things (IoT), traditional power inspection methods face challenges such as low efficiency and high risk. Unmanned aerial vehicles (UAVs) have emerged as a more efficient solution for inspecting power facilities due to their high maneuverability, excellent line-of-sight communication capabilities, and strong adaptability. However, UAVs typically grapple with limited computational power and energy resources, which constrain their effectiveness in handling computationally intensive and latency-sensitive inspection tasks. In response to this issue, we propose a UAV task offloading strategy based on deep reinforcement learning (DRL), which is designed for power inspection scenarios consisting of mobile edge computing (MEC) servers and multiple UAVs. Firstly, we propose an innovative UAV-Edge server collaborative computing architecture to fully exploit the mobility of UAVs and the high-performance computing capabilities of MEC servers. Secondly, we established a computational model concerning energy consumption and task processing latency in the UAV power inspection system, enhancing our understanding of the trade-offs involved in UAV offloading strategies. Finally, we formalize the task offloading problem as a multi-objective optimization issue and simultaneously model it as a Markov Decision Process (MDP). Subsequently, we proposed a task offloading algorithm based on a Deep Deterministic Policy Gradient (OTDDPG) to obtain the optimal task offloading strategy for UAVs. The simulation results demonstrated that this approach outperforms baseline methods with significant improvements in task processing latency and energy consumption.