Terrestrial Vehicles Research Articles

Latency Analysis of Drone-Assisted C-V2X Communications for Basic Safety and Co-Operative Perception Messages

Drone-assisted radio communication is revolutionizing future wireless networks, including sixth-generation (6G) and beyond, by providing unobstructed, line-of-sight links from air to terrestrial vehicles, enabling robust cellular cehicle-to-everything (C-V2X) communication networks. However, addressing communication latency is imperative, especially when considering autonomous vehicles. In this study, we analyze different types of delay and the factors impacting them in drone-assisted C-V2X networks. We specifically investigate C-V2X Mode 4, where multiple vehicles utilize available transmission windows to communicate the frequently collected sensor data with an embedded drone server. Through a discrete-time Markov model, we assess the medium access control (MAC) layer performance, analyzing the trade-off between data rates and communication latency. Furthermore, we compare the delay between cooperative perception messages (CPMs) and periodically transmitted basic safety messages (BSMs). Our simulation results emphasize the significance of optimizing BSM and CPM transmission intervals to achieve lower average delay as well as utilization of drones’ battery power to serve the maximum number of vehicles in a transmission time interval (TTI). The results also reveal that the average delay heavily depends on the packet arrival rate while the processing delay varies with the drone occupancy and state-transition rates for both BSM and CPM packets. Furthermore, an optimal policy approximates a threshold-based policy in which the threshold depends on the drone utilization and energy availability.

UAV-Mounted RIS-Aided Mobile Edge Computing System: A DDQN-Based Optimization Approach

Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) are increasingly employed in mobile edge computing (MEC) systems to flexibly modify the signal transmission environment. This is achieved through the active manipulation of the wireless channel facilitated by the mobile deployment of UAVs and the intelligent reflection of signals by RISs. However, these technologies are subject to inherent limitations such as the restricted range of UAVs and limited RIS coverage, which hinder their broader application. The integration of UAVs and RISs into UAV–RIS schemes presents a promising approach to surmounting these limitations by leveraging the strengths of both technologies. Motivated by the above observations, we contemplate a novel UAV–RIS-aided MEC system, wherein UAV–RIS plays a pivotal role in facilitating communication between terrestrial vehicle users and MEC servers. To address this challenging non-convex problem, we propose an energy-constrained approach to maximize the system’s energy efficiency based on a double-deep Q-network (DDQN), which is employed to realize joint control of the UAVs, passive beamforming, and resource allocation for MEC. Numerical results demonstrate that the proposed optimization scheme significantly enhances the system efficiency of the UAV–RIS-aided time division multiple access (TDMA) network.

A Novel Ant Colony-inspired Coverage Path Planning for Internet of Drones

Coverage Path Planning (CPP) is a fundamental problem widely used in various applications such as monitoring, patrol, mapping, and sweeping. Several types of robots can perform CPP, which has become very popular with the use of multiple drones. The Internet of Drones (IoD) is an emerging technology that aims to facilitate a multi-drone environment for various applications. In this context, coordination and organization of drones are crucial to avoid collisions and congestion in the airspace. One way to achieve this is by using well-defined airways. CPP tasks are often performed using multiple drones to cover a given area, which can be integrated into the IoD system. However, existing CPP solutions for drones do not fully consider collaboration capabilities between them, specific energy constraints, and the dynamic IoD scenario. To address these challenges, this work proposes an Ant Colony Optimization Algorithm for CPP in IoD (AntIoD), considering energy consumption. This scenario is particularly challenging due to the unique characteristics of being a mix of CPP for terrestrial vehicles with road constraints and CPP for drone networks. We compared our approach, AntIoD, with three recent solutions from the literature. The results reveal that AntIoD outperformed them in all simulated scenarios by at least 10% in terms of energy efficiency when compared to the best scenarios of the other algorithms.

Advances in the Application of Small Unoccupied Aircraft Systems (sUAS) for High-Throughput Plant Phenotyping

High-throughput plant phenotyping (HTPP) involves the application of modern information technologies to evaluate the effects of genetics, environment, and management on the expression of plant traits in plant breeding programs. In recent years, HTPP has been advanced via sensors mounted on terrestrial vehicles and small unoccupied aircraft systems (sUAS) to estimate plant phenotypes in several crops. Previous reviews have summarized these recent advances, but the accuracy of estimation across traits, platforms, crops, and sensors has not been fully established. Therefore, the objectives of this review were to (1) identify the advantages and limitations of terrestrial and sUAS platforms for HTPP, (2) summarize the different imaging techniques and image processing methods used for HTPP, (3) describe individual plant traits that have been quantified using sUAS, (4) summarize the different imaging techniques and image processing methods used for HTPP, and (5) compare the accuracy of estimation among traits, platforms, crops, and sensors. A literature survey was conducted using the Web of ScienceTM Core Collection Database (THOMSON REUTERSTM) to retrieve articles focused on HTPP research. A total of 205 articles were obtained and reviewed using the Google search engine. Based on the information gathered from the literature, in terms of flexibility and ease of operation, sUAS technology is a more practical and cost-effective solution for rapid HTPP at field scale level (>2 ha) compared to terrestrial platforms. Of all the various plant traits or phenotypes, plant growth traits (height, LAI, canopy cover, etc.) were studied most often, while RGB and multispectral sensors were most often deployed aboard sUAS in HTPP research. Sensor performance for estimating crop traits tended to vary according to the chosen platform and crop trait of interest. Regardless of sensor type, the prediction accuracies for crop trait extraction (across multiple crops) were similar for both sUAS and terrestrial platforms; however, yield prediction from sUAS platforms was more accurate compared to terrestrial phenotyping platforms. This review presents a useful guide for researchers in the HTPP community on appropriately matching their traits of interest with the most suitable sensor and platform.

Relaying Data With Joint Optimization of Energy and Delay in Cluster-Based UAV-Assisted VANETs

Vehicular networks are known for their dynamic topology, high mobility, and frequent disconnections. Unmanned aerial vehicles (UAVs) have been recently used as instant communication relays to bridge the communication gaps between terrestrial vehicles to improve connectivity in vehicular networks and overcome the aforementioned problems. Despite the existing work in the literature where each vehicle connects directly to UAVs, this work studies how clustering and different densities of vehicles affect delay and energy efficiency in a UAV-based vehicular network integrated with 5G technology. Consequently, this work addresses the problem of UAV enabling vehicular ad-hoc networks (VANETs) in a highway scenario, where UAVs serve as an effective complement to forward data packets between vehicles in the absence of sufficient fixed infrastructures in an emergency situation. The main objective of this work is to minimize the delay while maximizing the energy efficiency by minimizing the power consumption and maximizing the total data rate under realistic conditions, while nonorthogonal multiple access (NOMA) is also adopted as an alternative answer for the effective utilization of limited bandwidth. The free-flowing traffic follows a Poisson stochastic process where each vehicle is assigned a random speed selected from a truncated Gaussian distribution. To this end, a novel modification of fast global <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -means is adopted to partition vehicles, allowing communication between clusters by vehicle-to-vehicle links, while data packets between clusters are relayed through UAVs. By computing the convex approximation of the objective function and the constraints, the original problem with the mixed-integer, nonconvex, and nonlinear form is solved by the proposed iterative inner penalty function algorithm. Finally, extensive simulations are conducted to validate the superiority of the proposed method in terms of various metrics. The results indicate that the relaying task in the proposed UAV-assisted VANET based on 5G technology is perfectly suited to enhance the network connectivity.

Guest Editorial Special Issue on Antenna Array Enabled Space/Air/Ground Communications and Networking

With the rapid development of electronic and information technologies, the Internet of Everything (IoE) has become one of the trendiest topics in both academia and industry. Therein, many types of space/air/ground platforms need to be connected to networks for breaking down the isolation of information islands and providing various services. Space/air/ground platforms, such as satellites, unmanned aerial vehicles (UAVs), airships, balloons, terrestrial vehicles, and high-speed trains (HSTs) have emerged for accomplishing various complex tasks. Wireless communication is one of the most important technologies to support the real-time delivery of control commands and mission-related data. On the other hand, the space-air-ground integrated network has become a promising paradigm for the six-generation (6G) mobile communication network, where the aerospace and terrestrial vehicles may need to connect to existing mobile cellular networks or act as base stations (BSs) or relays to assist terrestrial wireless communications. To meet the ever-increasing demands of high capacity, wide coverage, low latency, and strong robustness for communications, it is promising to adopt large-scale antenna arrays at the transceivers to obtain considerable array gains and improve the channel quality. Antenna array-enabled beamforming technologies can facilitate spectrum reuse, interference mitigation, coverage enhancement, and physical-layer security. Antenna arrays can also be used to promote the sensing capability of space/air/ground networks, where the sensing information may be carefully processed to assist communications. However, enabling antenna array for space/air/ground communication networks poses specific, distinctive, and tricky challenges in antenna array design, physical layer, multiple access control layer, and network layer. As a result, numerous new research issues require to be addressed, which cover a wide range of disciplines including communication theory, network theory, antenna theory, signal processing, protocol design, resource allocation, optimization, hardware implementation, and experimentation.

Detection and Blind Channel Estimation for UAV-Aided Wireless Sensor Networks in Smart Cities Under Mobile Jamming Attack

Unmanned aerial vehicles (UAVs) can be integrated into wireless sensor networks (WSNs) for smart city applications in several ways. Among them, a UAV can be employed as a relay in a "store-carry and forward" fashion by uploading data from ground sensors and metering devices and, then, downloading it to a central unit. However, both the uploading and downloading phases can be prone to potential threats and attacks. As a legacy from traditional wireless networks, the jamming attack is still one of the major and serious threats to UAV-aided communications, especially when also the jammer is mobile, e.g., it is mounted on a UAV or inside a terrestrial vehicle. In this paper, we investigate anti-jamming communications for UAV-aided WSNs operating over doubly-selective channels in the downloading phase. In such a scenario, the signals transmitted by the UAV and the malicious mobile jammer undergo both time dispersion due to multipath propagation effects and frequency dispersion caused by their mobility. To suppress high-power jamming signals, we propose a blind physical-layer technique that jointly detects the UAV and jammer symbols through serial disturbance cancellation based on symbol-level post-sorting of the detector output. Amplitudes, phases, time delays, and Doppler shifts - required to implement the proposed detection strategy - are blindly estimated from data through the use of algorithms that exploit the almost-cyclostationarity properties of the received signal and the detailed structure of multicarrier modulation format. Simulation results corroborate the anti-jamming capabilities of the proposed method, for different mobility scenarios of the jammer.

Human activities disturb haul out and nursing behavior of Pacific harbor seals at Punta Banda Estuary, Mexico.

Humans frequently interact with Pacific harbor seals (Phoca vitulina richardii) at Punta Banda Estuary, Baja California, Mexico, due to the high incidence of recreational activities people undertake there. The immediate effect of these interactions is that seals flush to the water, reducing their time on land and, probably, increasing their energy expenditure. On-land observations were used to study the impact of different sources of disturbance on seal behavior and evaluate their effect on the amount of time dedicated to nursing over three pupping seasons, (2015–2017), with 0.58–0.81 disturbance events/hour recorded over the entire sampling period. Terrestrial vehicles were the source with the highest disturbance rate (number of disturbance events/h), followed closely by pedestrians. However, the proportion of seals affected was highest when pedestrians were the disturbance source. Recovery events (seals hauling out after flushing) occurred after 34% of disturbance events, after less than half of which the same number of hauled-out seals as there were prior to the disturbance were observed. Recovery time varied among the years studied, of which 2017 saw the longest recovery time. In addition, pedestrians were the disturbance source with the longest recovery time. Given that resting on land is essential for pup survival, which depends on both the establishment of the mother-pup bond from birth and its maintenance throughout nursing, flushing behavior may have significant implications for the entire colony during the nursing season. We recorded a decrease in nursing duration, which did not return to the same level even after recovery and the resumption of nursing. Terrestrial vehicles were found to be the disturbance source that shortened nursing events most significantly.

ANALYSIS OF THE PERFORMANCE OF AN UWB-BASED COOPERATIVE POSITIONING FOR DIFFERENT CAR PLATOON CONFIGURATIONS

Abstract. The increasing interest in autonomous vehicles motivates the researches aiming at developing reliable positioning system also in conditions challenging for the Global Navigation Satellite Systems (GNSS), such as in urban canyons, tunnels, under quite dense vegetation. The uso of Ultra Wide-Band (UWB) systems is among the quite well known methods for providing reasonable positioning results without exploiting GNSS. UWB systems are typically used indoors, however their use can be of interest also outdoors, in particular when the need is to ensure good positioning results over a quite small area. This paper investigates the use of UWB systems for positioning in the case of terrestrial vehicles, and, more specifically, it focuses on checking the influence of car platoon configurations on the performance of an UWB cooperative positioning system. In the considered tests, where a high percentage of UWB communications was successful, the obtained results show that the car configuration can have a quite remarkable impact on the positioning performance, doubling the obtained median error.

MONOLITh: a soft non-pneumatic foam robot with a functional mesh skin for use in delicate environments

MONOLITh is a bioinspired, untethered crawling soft robot. The body is made from a lightweight reticulated foam that provides passive shape restoration and supports the internally embedded components (motors, battery, wireless controller). DC motors pull tendons attached to an external fabric that distributes forces, and novel differential friction elements enable forward locomotion. This robot is capable of traveling at a maximum speed of 0.1 body lengths/sec, lifting 100% of its body weight, while remaining 95% soft materials by volume. We expect that the design principles and materials used to make this low cost and scalable robot will lead to the development of useful, and commercially viable, terrestrial or extraterrestrial vehicles.

Dispatch of UAVs for Urban Vehicular Networks: A Deep Reinforcement Learning Approach

Due to the dynamic nature of connectivity in terrestrial vehicular networks, it is of great benefit to deploy unmanned aerial vehicles (UAVs) in these networks to act as relays. As a result, a remarkable number of studies have exploited UAVs to bridge the communication gaps between terrestrial vehicles, and sometimes despite their unoptimized mobility, their restricted communication coverage, and their limited energy resources. However, it was noted that for an intermittently connected vehicular network, UAVs could not cover all sparse areas all the time. Even worse, when deploying enough UAVs to cover all these areas, the probability of inter-UAV collisions increases, and it will be complex to control their movements efficiently. Consequently, it is required to dispatch an organized and intelligent group of UAVs to perform communication relays in the long term while keeping their connectivity, minimizing their average energy consumption, and providing an efficient coverage strategy. To meet these requirements, we propose a deep reinforcement learning (DRL) framework, called DISCOUNT (Dispatch of UAVs for Urban VANETs). Extensive simulations have been conducted to evaluate the performance of the proposed framework. It has been shown that the proposed framework significantly outperforms two commonly-used baseline techniques and some reinforcement learning methods in terms of energy consumption, coverage, and routing performances.

Experimental Assessment of UWB and Vision-Based Car Cooperative Positioning System

The availability of global navigation satellite systems (GNSS) on consumer devices has caused a dramatic change in every-day life and human behaviour globally. Although GNSS generally performs well outdoors, unavailability, intentional and unintentional threats, and reliability issues still remain. This has motivated the deployment of other complementary sensors in such a way that enables reliable positioning, even in GNSS-challenged environments. Besides sensor integration on a single platform to remedy the lack of GNSS, data sharing between platforms, such as in collaborative positioning, offers further performance improvements for positioning. An essential element of this approach is the availability of internode measurements, which brings in the strength of a geometric network. There are many sensors that can support ranging between platforms, such as LiDAR, camera, radar, and many RF technologies, including UWB, LoRA, 5G, etc. In this paper, to demonstrate the potential of the collaborative positioning technique, we use ultra-wide band (UWB) transceivers and vision data to compensate for the unavailability of GNSS in a terrestrial vehicle urban scenario. In particular, a cooperative positioning approach exploiting both vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) UWB measurements have been developed and tested in an experiment involving four cars. The results show that UWB ranging can be effectively used to determine distances between vehicles (at sub-meter level), and their relative positions, especially when vision data or a sufficient number of V2V ranges are available. The presence of NLOS observations is one of the principal factors causing a decrease in the UWB ranging performance, but modern machine learning tools have shown to be effective in partially eliminating NLOS observations. According to the obtained results, UWB V2I can achieve sub-meter level of accuracy in 2D positioning when GNSS is not available. Combining UWB V2I and GNSS as well V2V ranging may lead to similar results in cooperative positioning. Absolute cooperative positioning of a group of vehicles requires stable V2V ranging and that a certain number of vehicles in the group are provided with V2I ranging data. Results show that meter-level accuracy is achieved when at least two vehicles in the network have V2I data or reliable GNSS measurements, and usually when vehicles lack V2I data but receive V2V ranging to 2–3 vehicles. These working conditions typically ensure the robustness of the solution against undefined rotations. The integration of UWB with vision led to relative positioning results at sub-meter level of accuracy, an improvement of the absolute positioning cooperative results, and a reduction in the number of vehicles required to be provided with V2I or GNSS data to one.

Performance analysis of MEMS-based inertial measurement units in terrestrial vehicles

In the recent period, Inertial Measurement Units (IMUs) are widely employed in many applications, such as smartphones, robotics, Unmanned Aerial vehicles, automotive and self‑driving vehicles, artificial intelligence, and numerous others. However, the dynamical metrological performances and the reliability analysis when these microelectronic devices operate under real environmental conditions are not sufficiently covered by scientific literature. Starting from standard tests for automotive applications, to emulate the real operating conditions of IMUs, a new test plan based on sine sweep vibration profiles has also been developed, including different service conditions characterized by the presence of a sinusoidal component with the addition of a random vibration noise typical of automotive scenarios. In-depth analysis has been carried out in the time and frequency domains leading to the employment of suitable figures of merit, highlighting the effects of mechanical stress on the metrological performances of microelectromechanical sensors. The developed test plan could be used to investigate if sinusoidal vibrations at specific frequencies influence the correct operation of low-cost platforms in typical automotive applications. The experimental results have confirmed the suitability of the proposed figures of merit in analyzing the effects of vibrations typical of the automotive context on the IMUs operating. In particular, they have allowed investigating the axes cross-sensitivity of triaxial systems, spurious responses, and unexpected behaviors due to the devices' non-ideality.

The YTU dataset and recurrent neural network based visual-inertial odometry

Visual Simultaneous Localization and Mapping (VSLAM) and Visual Odometry (VO) are fundamental problems to be properly tackled for enabling autonomous and effective movements of vehicles/robots supported by vision-based positioning systems. This study presents a publicly shared dataset for SLAM investigations: a dataset collected at the Yildiz Technical University (YTU) in an outdoor area by an acquisition system mounted on a terrestrial vehicle. The acquisition system includes two cameras, an inertial measurement unit, and two GPS receivers. All sensors have been calibrated and synchronized. To prove the effectiveness of the introduced dataset, this study also applies Visual Inertial Odometry (VIO) on the KITTI dataset. Also, this study proposes a new recurrent neural network-based VIO rather than just introducing a new dataset. In addition, the effectiveness of this proposed method is proven by comparing it with the state-of-the-arts ORB-SLAM2 and OKVIS methods. The experimental results show that the YTU dataset is robust enough to be used for benchmarking studies and the proposed deep learning-based VIO is more successful than the other two traditional methods.

ASSESSMENT OF CAR COLLABORATIVE POSITIONING WITH UWB AND VISION

Abstract. During the last decades the role of positioning and navigation systems is drastically changed in the everyday life of common people, influencing people behavior even multiple times each day. One of the most common applications of this kind of systems is that of terrestrial vehicle navigation: the use of GPS in the automotive navigation sector started thirty years ago, and, nowadays, it commonly assists drivers in reaching most of their non-standard destinations. Despite the popularity of global navigation satellite systems (GNSS), their usability is quite limited in certain working conditions, such as in urban canyons, in tunnels and indoors. While the latter case is typically not particularly interesting for the automotive sector, the first two scenarios represent important cases of interest for automotive navigation. In addition to the market request for increasing the usability of navigation systems on consumer devices, the recent increasing eagerness for autonomous driving is also attracting a lot of researchers’ attention on the development of alternative positioning systems, able to compensate for the unavailability or unreliability of GNSS. In accordance with the motivations mentioned above, this paper focuses on the development of a positioning system based on collaborative positioning between vehicles with UltraWide-Band devices and vision. To be more specific, this work focuses on assessing the performance of the developed system in successfully accomplishing three tasks, associated to different levels of gathered information: 1) assessing distance between vehicles, 2) determining the vehicle relative positions, 3) estimating the absolute car positions. The obtained results show that a) UWB can be reliably used (error of few decimeters error) to assess distances when vehicles are relatively close to each other (e.g. less than 40 m), b) the combination of UWB and vision allows to obtain good results in the computation of relative positions between vehicles, c) UWB-based collaborative positioning can be used for determining the absolute vehicle positions if a sufficient number of UWB range measurements can be ensured (sub-meter error for vehicles connected with a static UWB infrastructure, whereas error at meter level for those exploiting only vehicle-to-vehicle UWB communications).

Hybrid LoRa-IEEE 802.11s Opportunistic Mesh Networking for Flexible UAV Swarming

Unmanned Aerial Vehicles (UAVs) and small drones are nowadays being widely used in heterogeneous use cases: aerial photography, precise agriculture, inspections, environmental data collection, search-and-rescue operations, surveillance applications, and more. When designing UAV swarm-based applications, a key “ingredient” to make them effective is the communication system (possible involving multiple protocols) shared by flying drones and terrestrial base stations. When compared to ground communication systems for swarms of terrestrial vehicles, one of the main advantages of UAV-based communications is the presence of direct Line-of-Sight (LOS) links between flying UAVs operating at an altitude of tens of meters, often ensuring direct visibility among themselves and even with some ground Base Transceiver Stations (BTSs). Therefore, the adoption of proper networking strategies for UAV swarms allows users to exchange data at distances (significantly) longer than in ground applications. In this paper, we propose a hybrid communication architecture for UAV swarms, leveraging heterogeneous radio mesh networking based on long-range communication protocols—such as LoRa and LoRaWAN—and IEEE 802.11s protocols. We then discuss its strengths, constraints, viable implementation, and relevant reference use cases.

Measuring Canopy Geometric Structure Using Optical Sensors Mounted on Terrestrial Vehicles: A Case Study in Vineyards

Smart and precision agriculture concepts require that the farmer measures all relevant variables in a continuous way and processes this information in order to build better prescription maps and to predict crop yield. These maps feed machinery with variable rate technology to apply the correct amount of products in the right time and place, to improve farm profitability. One of the most relevant information to estimate the farm yield is the Leaf Area Index. Traditionally, this index can be obtained from manual measurements or from aerial imagery: the former is time consuming and the latter requires the use of drones or aerial services. This work presents an optical sensing-based hardware module that can be attached to existing autonomous or guided terrestrial vehicles. During the normal operation, the module collects periodic geo-referenced monocular images and laser data. With that data a suggested processing pipeline, based on open-source software and composed by Structure from Motion, Multi-View Stereo and point cloud registration stages, can extract Leaf Area Index and other crop-related features. Additionally, in this work, a benchmark of software tools is made. The hardware module and pipeline were validated considering real data acquired in two vineyards—Portugal and Italy. A dataset with sensory data collected by the module was made publicly available. Results demonstrated that: the system provides reliable and precise data on the surrounding environment and the pipeline is capable of computing volume and occupancy area from the acquired data.

Multi-sensor imagery rectification and registration for herbicide testing

The use of multi-spectral sensors has been focused on several agricultural tasks, yet it is necessary to further assess this approach to achieve sufficient precision to carry out adequately these. Metric information from these images is traditionally derived by photogrammetric techniques, but with a major limitation: photographed objects should be static while the photographs are being taken, but plants are generally in movement because of wind and this causes the photogrammetric process to be unable to generate the necessary information to make any metric measurement. To bypass this, metric information can be derived via rectification, using only one photograph.This work aims to develop a band co-registration method with agricultural purposes, based on rectified images taken from different sensors usually mounted on UAVs or terrestrial vehicles, studying its accuracy in a quantitative way. All multispectral information co-registered in a precise way will allow the calculation or development of new radiometric and even geometric indices that will help to improve efficiency in many tasks related to agriculture.Images taken from a multi-spectral (green, near infra-red, red and red edge) and a thermal camera were used to apply the developed methodology. First, a digital elevation model describing the displacement produced by distortion due to the sensor lens was obtained and applied to each of the studied pictures to correct this distortion. Then, distortion due to conic perspective present in the photographs was corrected, taking into account the homology relationship between the photographed object and the picture. To carry out these tasks, several computers programs were developed. Subsequently, the edges of the five bands corresponding to 250 plants were digitalised and their areas were measured. Furthermore, the intersection of the five bands of each plant was calculated, and an index (AI) indicating the fraction of the area of each band, which was out of the common area edge of the five bands, was calculated for each plant. The average value of this index for each band ranged from 0.22 to 0.24, with no statistically significant differences between them, indicating a high accuracy of the proposed methodology.

Tracking and Relative Localization of Drone Swarms With a Vision-Based Headset

We address the detection, tracking, and relative localization of the agents of a drone swarm from a human perspective using a headset equipped with a single camera and an Inertial Measurement Unit (IMU). We train and deploy a deep neural network detector on image data to detect the drones. A joint probabilistic data association filter resolves the detection problems and couples this information with the headset IMU data to track the agents. In order to estimate the drones' relative poses in 3D space with respect to the human, we use an additional deep neural network that processes image regions of the drones provided by the tracker. Finally, to speed up the deep neural networks' training, we introduce an automated labeling process relying on a motion capture system. Several experimental results validate the effectiveness of the proposed approach. The approach is real-time, does not rely on any communication between the human and the drones, and can scale to a large number of agents, often called swarms. It can be used to spatially task a swarm of drones and also employed without a headset for formation control and coordination of terrestrial vehicles.

Digital Documentation and a 3-D Model of Beaufort Castle via RTK GNSS, Terrestrial Laser Scanner and UAS-based Photogrammetry

Three-dimensional digital technology is important in the maintenance and monitoring of archeological sites. This paper focuses on using a combination of terrestrial laser scanning and unmanned aerial vehicle (Phantom 4 pro) photogrammetry to establish a three-dimensional model and associated digital documentation of Beaufort castle (Arnoun, South Lebanon). The overall discrepancy between the two technologies was sufficient for the generation of convergent data. Thus, the terrestrial laser scanning and phantom 4 photogrammetry data were aligned and merged post-conversion into compatible extensions. A three-D dimensional (3D) model, with planar and perpendicular geometries, based on the hybrid datapoint cloud was developed. This study demonstrates the potential of using the integration of terrestrial laser scanning and photogrammetry in 3D digital documentation and spatial analysis of the Lebanese archeological sites.