Drones, Volume 6, Issue 6 (June 2022) – 19 articles

The cooperation of multiple unmanned aerial vehicles (Multi-UAV) can effectively solve the area coverage problem. However, developing an online multi-UAV coverage approach remains a challenge due to energy constraints and environmental dynamics. In this paper, we design a comprehensive framework for area coverage with multiple energy-limited UAVs in dynamic environments, which we call MCTA (Multi-UAV Coverage through Two-step Auction). Specifically, the online two-step auction mechanism is proposed to select the optimal action. Then, an obstacle avoidance mechanism is designed by defining several heuristic rules. After that, considering energy constraints, we develop the reverse auction mechanism to balance workload between multiple UAVs. Comprehensive experiments demonstrate that MCTA can achieve a high coverage rate while ensuring a low repeated coverage rate and average step deviation in most circumstances. Full article

Crop seedling detection is an important task in the seedling stage of crops in fine agriculture. In this paper, we propose a high-precision lightweight object detection network model based on a multi-activation layer and depth-separable convolution module to detect crop seedlings, aiming to improve the accuracy of traditional artificial intelligence methods. Due to the insufficient dataset, various image enhancement methods are used in this paper. The dataset in this paper was collected from Shahe Town, Laizhou City, Yantai City, Shandong Province, China. Experimental results on this dataset show that the proposed method can effectively improve the seedling detection accuracy, with the F1 score and mAP reaching 0.95 and 0.89, respectively, which are the best values among the compared models. In order to verify the generalization performance of the model, we also conducted a validation on the maize seedling dataset, and experimental results verified the generalization performance of the model. In order to apply the proposed method to real agricultural scenarios, we encapsulated the proposed model in a Jetson logic board and built a smart hardware that can quickly detect seedlings. Full article

Efficient and quick yield prediction is of great significance for ensuring world food security and crop breeding research. The rapid development of unmanned aerial vehicle (UAV) technology makes it more timely and accurate to monitor crops by remote sensing. The objective of this study was to explore the method of developing a novel yield index (YI) with wide adaptability for yield prediction by fusing vegetation indices (VIs), color indices (CIs), and texture indices (TIs) from UAV-based imagery. Six field experiments with 24 varieties of rice and 21 fertilization methods were carried out in three experimental stations in 2019 and 2020. The multispectral and RGB images of the rice canopy collected by the UAV platform were used to rebuild six new VIs and TIs. The performance of VI-based YI (MAPE = 13.98%) developed by quadratic nonlinear regression at the maturity stage was better than other stages, and outperformed that of CI-based (MAPE = 22.21%) and TI-based (MAPE = 18.60%). Then six VIs, six CIs, and six TIs were fused to build YI by multiple linear regression and random forest models. Compared with heading stage (R² = 0.78, MAPE = 9.72%) and all stage (R² = 0.59, MAPE = 22.21%), the best performance of YI was developed by random forest with fusing VIs + CIs + TIs at maturity stage (R² = 0.84, MAPE = 7.86%). Our findings suggest that the novel YI proposed in this study has great potential in crop yield monitoring. Full article

Low-altitude flight in mountainous terrains is a difficult flight task applied in both military and civilian fields. The helicopter has to maintain low altitude to realize search and rescue, reconnaissance, penetration, and strike operations. It contains complex environment perception, multilevel decision making, and multi-objective flight control; thus, flight is currently mainly conducted by human pilots. In this work, a control framework is implemented to realize autonomous flight for unmanned helicopter operations in an unknown mountainous environment. The identification of targets and threats is introduced using a deep neural network. A 3D vector field histogram method is adopted for local terrain avoidance based on airborne Lidar sensors. In particular, we propose an intuitive direct-viewing method to judge and change the visibilities of the helicopter. On this basis, a finite state machine is built for decision making of the autonomous flight. A highly realistic simulation environment is established to verify the proposed control framework. The simulation results demonstrate that the helicopter can autonomously complete flight missions including a fast approach, threat avoidance, cover concealment, and circuitous flight operations similar to human pilots. The proposed control framework provides an effective solution for complex flight tasks and expands the flight control technologies for high-level unmanned helicopter operations. Full article

In recent years, the increasing number of unmanned aerial vehicles (UAVs) in the low-altitude airspace have not only brought convenience to people’s work and life, but also great threats and challenges. In the process of UAV detection and tracking, there are common problems such as target deformation, target occlusion, and targets being submerged by complex background clutter. This paper proposes an anti-occlusion UAV tracking algorithm for low-altitude complex backgrounds by integrating an attention mechanism that mainly solves the problems of complex backgrounds and occlusion when tracking UAVs. First, extracted features are enhanced by using the SeNet attention mechanism. Second, the occlusion-sensing module is used to judge whether the target is occluded. If the target is not occluded, tracking continues. Otherwise, the LSTM trajectory prediction network is used to predict the UAV position of subsequent frames by using the UAV flight trajectory before occlusion. This study was verified on the OTB-100, GOT-10k and integrated UAV datasets. The accuracy and success rate of integrated UAV datasets were 79% and 50.5% respectively, which were 10.6% and 4.9% higher than those of the SiamCAM algorithm. Experimental results show that the algorithm could robustly track a small UAV in a low-altitude complex background. Full article

The high demand for distilled agave products reduces wild populations. The use of geospatial technologies such as unmanned aerial vehicles (UAVs) offer enormous benefits in spatial and temporal resolution and lower costs than traditional direct field observation techniques for natural resource monitoring. The objective was to estimate the green biomass (Wt) of Agave durangensis Gentry using high-resolution images obtained by a UAV in Nombre de Dios, Durango. Random sampling was performed in the agave area. A Pearson correlation analysis was performed, followed by a regression analysis. The results showed that NDVI was the most correlated (r = 0.65). The regression analysis showed that the model obtained explains 59% (RMSE = 32.06 kg) of the total variability in the estimation of green biomass (Wt) of agave using images derived from the UAV. The best estimate was achieved with B1, B2, NDVI, GNDVI, EVI2, and SAVI as predictor variables. High-resolution images were shown to be a tool for estimating Wt of Agave durangensis Gentry. Full article

Recently, unmanned aerial vehicles (UAVs), also known as drones, have come in a great diversity of several applications such as military, construction, image and video mapping, medical, search and rescue, parcel delivery, hidden area exploration, oil rigs and power line monitoring, precision farming, wireless communication and aerial surveillance. The drone industry has been getting significant attention as a model of manufacturing, service and delivery convergence, introducing synergy with the coexistence of different emerging domains. UAVs offer implicit peculiarities such as increased airborne time and payload capabilities, swift mobility, and access to remote and disaster areas. Despite these potential features, including extensive variety of usage, high maneuverability, and cost-efficiency, drones are still limited in terms of battery endurance, flight autonomy and constrained flight time to perform persistent missions. Other critical concerns are battery endurance and the weight of drones, which must be kept low. Intuitively it is not suggested to load them with heavy batteries. This study highlights the importance of drones, goals and functionality problems. In this review, a comprehensive study on UAVs, swarms, types, classification, charging, and standardization is presented. In particular, UAV applications, challenges, and security issues are explored in the light of recent research studies and development. Finally, this review identifies the research gap and presents future research directions regarding UAVs. Full article

A hybrid model-based and data-driven framework is proposed in this paper for autonomous coastline surveillance using an unmanned aerial vehicle. The proposed approach comprises three individual neural network-assisted modules that work together to estimate the state of the target (i.e., shoreline) to contribute to its identification and tracking. The shoreline is first detected through image segmentation using a Convolutional Neural Network. The part of the segmented image that includes the detected shoreline is then fed into a CNN real-time optical flow estimator. The position of pixels belonging to the detected shoreline, as well as the initial approximation of the shoreline motion, are incorporated into a neural network-aided Extended Kalman Filter that learns from data and can provide on-line motion estimation of the shoreline (i.e., position and velocity in the presence of waves) using the system and measurement models with partial knowledge. Finally, the estimated feedback is provided to a Partitioned Visual Servo tracking controller for autonomous multirotor navigation along the coast, ensuring that the latter will always remain inside the onboard camera field of view. A series of outdoor comparative studies using an octocopter flying along the shoreline in various weather and beach settings demonstrate the effectiveness of the suggested architecture. Full article

The rapid growth of data traffic due to the demands of new services and applications poses new challenges to the wireless network. Unmanned aerial vehicles (UAVs) can be a solution to support wireless networks during congestion, especially in scenarios where the region has high traffic peaks due to the temporal and spatial flow of users. In this paper, an intelligent machine-learning-based system is proposed to deploy UAV base stations (UAV-BS) to temporarily support the mobile network in regions suffering from the congestion effect caused by the high density of users. The system includes two main steps, the load prediction algorithm (LPA) and the UAV-BSs clustering and positioning algorithm (UCPA). In LPA, the load history generated by the mobile network is used to predict which macrocells are congested. In UCPA, planning is performed to calculate the number of UAV BSs needed based on two strategies: naïve and optimized, in addition to calculating the optimal positioning for each device requested to support the overloaded macrocells. For prediction, we used two models, generalized regression neural networks (GRNN) and random forest, and the results showed that both models were able to make accurate predictions, and the random forest model was better with an accuracy of over 85%. The results showed that the intelligent system significantly reduced the overhead of the affected macrocells, improved the quality of service (QoS), and reduced the probability of blocking users, as well as defined the preventive scheduling for the UAV BSs, which benefited the scheduling and energy efficiency. Full article

This work proposes a simplified control method to stabilize the model of a nonlinear Planar Vertical Take-Off and Landing (PVTOL) system when a constant force is applied in the horizontal axis. Since the stability analysis is based on a Lyapunov function, exponential stability is guaranteed when the initial conditions fall inside a domain of attraction that is also specified. The performance of the suggested control algorithm is demonstrated using numerical simulations. Full article

In the present work a dynamic simulation model for a quadcopter drone is developed and validated through experimental flight data. The aerodynamics of the rotors is modeled with the blade element theory combined with the Peters and He dynamic wake model, using an appropriate number of states. The aerodynamic forces and moments thus calculated feed the dynamic equations of a drone and an aeroacoustics model, to obtain an estimate of the noise generated during the flight. Loading and thickness noise are calculated as a time domain solution of the wave equation (Farassat 1A formulation), with mobile sources in stagnant flow. The results of numerical simulations are compared with experimental data recorded during flights performed at the Aerospace Italian Research Center (CIRA), both for the flight dynamics and the aeroacoustics models. To customize the model to the drone used, a laser scanner is used to obtain the geometric characteristics of the blades and the XFOIL program is used to calculate the blade profile aerodynamic coefficients. Full article

Coastal environments are some of the most dynamic environments in the world. As they are constantly changing, so are the technologies and techniques we use to map and monitor them. The rapid advancement of sUAS-based remote sensing calls for rigorous field and processing workflows so that more reliable and consistent sUAS projects of coastal environments are carried out. Here, we synthesize the best practices to create sUAS photo-based surveying and processing workflows that can be used and modified by coastal scientists, depending on their project objective. While we aim to simplify the complexity of these workflows, we note that the nature of this work is a craft that carefully combines art, science, and technology. sUAS LiDAR is the next advancement in mapping and monitoring coastal environments. Therefore, future work should consider synthesizing best practices to develop rigorous field and data processing workflows used for sUAS LiDAR-based projects of coastal environments. Full article

In this paper, the multiple autonomous underwater vehicles (AUVs) task allocation (TA) problem in ocean current environment based on a novel reinforcement learning approach is studied. First, the ocean current environment including direction and intensity is established and a reward function is designed, in which the AUVs are required to consider the ocean current, the task emergency and the energy constraints to find the optimal TA strategy. Then, an automatic policy amendment algorithm (APAA) is proposed to solve the drawback of slow convergence in reinforcement learning (RL). In APAA, the task sequences with higher team cumulative reward (TCR) are recorded to construct task sequence matrix (TSM). After that, the TCR, the subtask reward (SR) and the entropy are used to evaluate TSM to generate amendment probability, which adjusts the action distribution to increase the chances of choosing those more valuable actions. Finally, the simulation results are provided to verify the effectiveness of the proposed approach. The convergence performance of APAA is also better than DDQN, PER and PPO-Clip. Full article

Monitoring intertidal habitats, such as oyster reefs, salt marshes, and mudflats, is logistically challenging and often cost- and time-intensive. Remote sensing platforms, such as unoccupied aircraft systems (UASs), present an alternative to traditional approaches that can quickly and inexpensively monitor coastal areas. Despite the advantages offered by remote sensing systems, challenges remain concerning the best practices to collect imagery to study these ecosystems. One such challenge is the range of spatial resolutions for imagery that is best suited for intertidal habitat monitoring. Very fine imagery requires more collection and processing times. However, coarser imagery may not capture the fine-scale patterns necessary to understand relevant ecological processes. This study took UAS imagery captured along the Gulf of Mexico coastline in Florida, USA, and resampled the derived orthomosaic and digital surface model to resolutions ranging from 3 to 31 cm, which correspond to the spatial resolutions achievable by other means (e.g., aerial photography and certain commercial satellites). A geographic object-based image analysis (GEOBIA) workflow was then applied to datasets at each resolution to classify mudflats, salt marshes, oyster reefs, and water. The GEOBIA process was conducted within R, making the workflow open-source. Classification accuracies were largely consistent across the resolutions, with overall accuracies ranging from 78% to 82%. The results indicate that for habitat mapping applications, very fine resolutions may not provide information that increases the discriminative power of the classification algorithm. Multiscale classifications were also conducted and produced higher accuracies than single-scale workflows, as well as a measure of uncertainty between classifications. Full article

Edge devices (EDs) carry limited energy, but 6th generation mobile networks (6G) communication will consume more energy. The unmanned aerial vehicle (UAV)-aided wireless communication network can provide communication links to EDs without a signal. However, with the time-lag system, the EDs cannot dynamically adjust the emission energy because the dynamic UAV coordinates cannot be accurately acquired. In addition, the fixed emission energy makes the EDs have poor endurance. To address this challenge, in this paper, we propose a deep learning-based energy optimization algorithm (DEO) to dynamically adjust the emission energy of the ED so that the received energy of the mobile relay UAV is, as much as possible, equal to the sensitivity of the receiver. Specifically, the edge server provides the computing platform and uses deep learning (DL) to predict the location information of the relay UAV in dynamic scenarios. Then, the ED emission energy is adjusted according to the predicted position. It enables the ED to communicate reliably with the mobile relay UAV at minimum energy. We analyze the performance of a variety of predictive networks under different time-delay systems through experiments. The results show that the Weighted Mean Absolute Percentage Error (WMAPE) of this algorithm is 0.54%, 0.80% and 1.15% under the effect of a communication delay of 0.4 s, 0.6 s and 0.8 s, respectively. Full article

A novel cooperative strategy for distributed unmanned aerial vehicle (UAV) swarms with different functions, namely the mission chain-driven unmanned aerial vehicle swarms cooperation method, is proposed to allow the fast search and timely rescue of injured human targets in a wide-area outdoor environment. First, a UAV-camera unit is exploited to detect the suspected human target combined with improved deep learning technology. Then, the target location information is transferred to a self-organizing network. Then, the special bio-radar-UAV unit was released to recheck the survivals through a respiratory characteristic detection algorithm. Finally, driven by the location and vital sign status of the injured, a nearby emergency-UAV unit will perform corresponding medical emergency missions, such as dropping emergency supplies. Experimental results show that this strategy can identify the human targets autonomously from the outdoor environment effectively, and the target detection, target sensing, and medical emergency mission chain is completed successfully relying on the cooperative working mode, which is meaningful for the future search-rescue mission of outdoor injured human targets. Full article

Operations and maintenance of Offshore Wind Turbines (OWTs) are challenging, with manual operators constantly exposed to hazardous environments. Due to the high task complexity associated with the OWT, the transition to unmanned solutions remains stagnant. Efforts toward unmanned operations have been observed using Unmanned Aerial Vehicles (UAVs) and Unmanned Underwater Vehicles (UUVs) but are limited mostly to visual inspections only. Collaboration strategies between unmanned vehicles have introduced several opportunities that would enable unmanned operations for the OWT maintenance and repair activities. There have been many papers and reviews on collaborative UVs. However, most of the past papers reviewed collaborative UVs for surveillance purposes, search and rescue missions, and agricultural activities. This review aims to present the current capabilities of Unmanned Vehicles (UVs) used in OWT for Inspection, Maintenance, and Repair (IMR) operations. Strategies to implement collaborative UVs for complex tasks and their associated challenges are discussed together with the strategies to solve localization and navigation issues, prolong operation time, and establish effective communication within the OWT IMR operations. This paper also briefly discusses the potential failure modes for collaborative approaches and possible redundancy strategies to manage them. The collaborative strategies discussed herein will be of use to researchers and technology providers in identifying significant gaps that have hindered the implementation of full unmanned systems which have significant impacts towards the net zero strategy. Full article

Drone use has been rapidly increasing in protected areas in North America, and potential impacts on terrestrial megafauna have been largely unstudied. We evaluated behavioral responses to drones on two terrestrial charismatic species, feral horse (Equus caballus) and bison (Bison bison), at Theodore Roosevelt National Park (North Dakota, United States) in 2018. Using a Trimble UX5 fixed-wing drone, we performed two flights at 120 m above ground level (AGL), one for each species, and recorded video footage of their behaviors prior to, during, and after the flight. Video footage was analyzed in periods of 10 s intervals, and the occurrence of a behavior was modeled in relation to the phase of the flights (prior, during, and after). Both species displayed behavioral responses to the presence of the fixed-wing drone. Horses increased feeding (p-value < 0.05), traveling (p-value < 0.05), and vigilance (p-value < 0.05) behaviors, and decreased resting (p-value < 0.05) and grooming (p-value < 0.05). Bison increased feeding (p-value < 0.05) and traveling (p-value < 0.05) and decreased resting (p-value < 0.05) and grooming (p-value < 0.05). Neither species displayed escape behaviors. Flying at 120 m AGL, the drone might have been perceived as low risk, which could possibly explain the absence of escape behaviors in both species. While we did not test physiological responses, our behavioral observations suggest that drone flights at the altitude we tested did not elicit escape responses, which have been observed in ground surveys or traditional low-level aerial surveys. Our results provide new insights for guidelines about drone use in conservation areas, such as the potential of drones for surveys of feral horses and bison with low levels of disturbance, and we further recommend the development of in situ guidelines in protected areas centered on place-based knowledge, besides existing standardized guidelines. Full article

Uncrewed aerial vehicles (UAVs) are continuously gaining popularity in a wide spectrum of applications, while their positioning and navigation most often relies on Global Navigation Satellite Systems (GNSS). However, numerous conditions and practices require UAV operation in GNSS-denied environments, including confined spaces, urban canyons, vegetated areas and indoor places. For the purposes of this study, an integrated UAV navigation system was designed and implemented which utilizes GNSS, visual, depth and inertial data to provide real-time localization. The implementation is built as a package for the Robotic Operation System (ROS) environment to allow ease of integration in various systems. The system can be autonomously adjusted to the flight environment, providing spatial awareness to the aircraft. This system expands the functionality of UAVs, as it enables navigation even in GNSS-denied environments. This integrated positional system provides the means to support fully autonomous navigation under mixed environments, or malfunctioning conditions. Experiments show the capability of the system to provide adequate results in open, confined and mixed spaces. Full article