Multi-rotor UAV Research Articles

Air Route Design of Multi-Rotor UAVs for Urban Air Mobility

UAVs will present significant air traffic in the urban airspace in the future, which brings new challenges to urban air traffic management and control. This paper presents an air route design scheme for multi-rotor UAVs in urban airspace to enable UAV operations at orderly levels. The air routes include legs and intersections, which are the three-dimensional channels of UAV flight. Based on the concept of structured and layered urban airspace, the cylindrical pipeline flight leg is designed, and the operation concept, characteristic parameters and flight procedures of along-road and roundabout intersections are proposed. By defining UAV conflict risk and intersection service level metrics, the operation situation of UAVs is quantitatively evaluated. Taking an urban transportation scenario as a case, the proposed route design scheme is simulated in different scale UAV operating scenarios. The results show that the number of UAVs at the intersection is positively correlated with the conflict probability, the number of crossing routes is negatively correlated with the intersection passing rate, and the UAV arrival rate is positively correlated with the intersection average passing time. The along-road type intersection is suitable for the area with fewer crossing routes and sparse UAVs, while the roundabout type intersection is adapted for the area with more crossing routes and dense UAVs. This research provides a new idea for urban UAV air route design, which is helpful in promoting the standardized management of UAVs and accelerating the integration of UAVs into urban airspace.

Autonomous Multirotor UAV Search and Landing on Safe Spots Based on Combined Semantic and Depth Information From an Onboard Camera and LiDAR

Autonomous Multirotor UAV Search and Landing on Safe Spots Based on Combined Semantic and Depth Information From an Onboard Camera and LiDAR

Design and Development of A Semi-airborne Transient Electromagnetic System

The semi-airborne transient electromagnetic method (SATEM) is a geophysical method that combines the advantages of high-power ground-based transmitting and efficient airborne observation. In recent years, the threshold for SATEM application significantly reduced through the integration of the multi-rotor UAVs and the systems obtained by simple redevelopment of existing ground-based systems. However, it is still difficult to obtain good detection results with such systems for the following specific reasons: in terms of system design, there is a lack of method for estimating the key parameters of the system, resulting in the designed system being unable to effectively meet the requirements of the SATEM method and observation scenarios; in terms of technology, there is a lack of effective means of ensuring a high signal-to-noise ratio (high-current transmitting and effective suppression of major external noise) while meeting the demand for distortion-free observation. In response to these issues, a system bandwidth estimation method based on the analysis of information-carrying capacity is proposed. On this basis, the solutions for large current and fast turn-off transmitter and for overcoming major external noise. Through a survey located in a karst landform area, and the overall application performance of the system is discussed based on the inversion results.

Analysis of test results of the P-20 multi-rotor UAV when spraying pesticides in precision agriculture

Abstract The article provides an overview of the current results of using multi-rotor UAVs when spraying pesticides in precision agriculture. It is noted that, as part of improving the technology for using UAVs, it is necessary to continue work on optimizing and testing the parameters of UAV transport and technological cycles together with a reasonable choice of special reagents and their combinations to improve the spraying effect and increase the level of use of pesticides. This will directly affect the improvement of the quality and efficiency of precision farming systems. The analysis presented in the work is based on testing the parameters of the transport and technological cycle of a multi-rotor UAV of the P-20 type, which was used to spray pesticides on cotton fields to combat aphids and spider mites on cotton at the flowering and boll setting stages. The results are considered that make it possible to analyze and compare the laws of uniform and penetrating deposition and drift of droplets in cotton canopies at different UAV flight altitudes. It has been shown that spraying small volumes of agrochemicals using UAVs at low altitudes differs from operations performed using modern manned aircraft or boom sprayers. The flight altitude of the UAV has a significant impact on the deposition and drift of droplets. The results of studies related to elucidating the effect of defoliant adsorption on cotton leaves are also noted. The adsorption of acetamiprid and spirodiclofen was studied. Methods for statistical processing of the obtained experimental data are presented, in particular, to test the significance of the influence of flight altitude on the experimental results.

Structural design and analysis of multi-rotor UAV

Structural design and analysis of multi-rotor UAV

Filter-based adaptive backstepping attitude control for multi-rotor UAVs with parametric uncertainty, external disturbance and input saturation

Filter-based adaptive backstepping attitude control for multi-rotor UAVs with parametric uncertainty, external disturbance and input saturation

Precise Motion Compensation of Multi-Rotor UAV-Borne SAR Based on Improved PTA

In recent years, with the miniaturization of high-precision position and orientation systems (POS), precise motion errors during SAR data collection can be calculated based on high-precision POS. However, compensating for these errors remains a significant challenge for multi-rotor UAV-borne SAR systems. Compared with large aircrafts, multi-rotor UAVs are lighter, slower, have more complex flight trajectories, and have larger squint angles, which result in significant differences in motion errors between building targets and ground targets. If the motion compensation is based on ground elevation, the motion error of the ground target will be fully compensated, but the building target will still have a large residual error; as a result, although the ground targets can be well-focused, the building targets may be severely defocused. Therefore, it is necessary to further compensate for the residual motion error of building targets based on the actual elevation on the SAR image. However, uncompensated errors will affect the time–frequency relationship; furthermore, the ω-k algorithm will further change these errors, resulting in errors in SAR images becoming even more complex and difficult to compensate for. To solve this problem, this paper proposes a novel improved precise topography and aperture-dependent (PTA) method that can precisely compensate for motion errors in the UAV-borne SAR system. After motion compensation and imaging processing based on ground elevation, a secondary focus is applied to defocused buildings. The improved PTA fully considers the coupling of the residual error with the time–frequency relationship and ω-k algorithm, and the precise errors in the two-dimensional frequency domain are determined through numerical calculations without any approximations. Simulation and actual data processing verify the effectiveness of the method, and the experimental results show that the proposed method in this paper is better than the traditional method.

Energy harvesting and ANFIS modeling of a PVDF/GO-ZNO piezoelectric nanogenerator on a UAV

Abstract The aim of this study is to energy harvesting and modeling from a soft and flexible nanogenerator based on nanofibers (PVDF/ZnO-RGO) on the arm of a multi-rotor UAV. The nanogenerator modeling was done to improve the usability of piezoelectric nanogenerators to provide part of the electric energy required by drones. Tests were conducted on the experimental setup and practical flight conditions. In each test and a period of 30 s, the vibration signals of the UAV arm were stored, and the vibration data extracted in the time domain and the electrical voltage of the nanogenerator were obtained through the vibrations of the drone, and its values were recorded in each experiment. After signal processing, the best vibration features were used for modeling. In the modeling process, some data were considered for training and testing in ANFIS network. The ANFIS inputs included the selected vibration features and the outputs included the stored voltage. The modeling results were analyzed based on increasing the amplitude of vibrations and the duration of exposure of the nanogenerator to vibration led to higher voltage generation. The maximum voltage recorded during the 30-s flight time of the tested UAV was 64 mV at 3.54, 49.8, and 0.48 Hz along the x, y, and z axes, respectively. The results showed that the application of energy harvesting on different vibration systems could be improved through modeling. It can be used to predict the amount of electric energy produced according to the vibrations of the drone in the future.

Adaptive Multi-Surface Sliding Mode Control with Radial Basis Function Neural Networks and Reinforcement Learning for Multirotor Slung Load Systems

While using multirotor UAVs for transport of suspended payloads, there is a need for stability along the desired path, in addition to avoidance of any excessive payload oscillations, and a good level of precision in maintaining the desired path of the vehicle. However, due to the nonlinear and underactuated nature of the system, in addition to the presence of mismatched uncertainties, the development of a control system for this application poses an interesting research problem. This paper proposes a control architecture for a multirotor slung load system by integrating a Multi-Surface Sliding Mode Control, aided by a Radial Basis Function Neural Network, with a Deep Q-Network Reinforcement Learning agent. The former will be used to ensure asymptotic tracking stability, while the latter will be used to suppress payload oscillations. First, we will present the dynamics of a multirotor slung load system, represented here as a quadrotor with a single pendulum load suspended from it. We will then propose a control method in which a multi-surface sliding mode controller, based on an adaptive RBF Neural Network for trajectory tracking of the quadrotor, works in tandem with a Deep Q-Network Reinforcement Learning agent whose reward function aims to suppress the oscillations of the single pendulum slung load. Simulation results demonstrate the effectiveness and potential of the proposed approach in achieving precise and reliable control of multirotor slung load systems.

A High-Gain Observer Approach to Robust Trajectory Estimation and Tracking for a Multi-Rotor Uav

Abstract Using the context of trajectory estimation and tracking for multi-rotor UAVs, we explore the challenges in applying high-gain observers to highly dynamic systems. The multi- rotor will operate in the presence of external disturbances and modeling errors. At the same time, the reference trajectory is unknown and generated from a reference system with unknown or partially known dynamics. We assume the only measurements that are available are the position and orientation of the multi-rotor and the position of the reference sys- tem. We adopt an extended high-gain observer (EHGO) estimation framework to estimate the unmeasured multi-rotor states, modeling errors, external disturbances, and the reference trajectory. We design a robust output feedback con- troller for trajectory tracking that comprises a feedback linearizing controller and the EHGO. The proposed control method is rigorously analyzed to establish its stability properties. Finally, we illustrate our theoretical results through numerical simulation and experimental validation in which a multi-rotor tracks a moving ground vehicle with an unknown trajectory and dynamics and successfully lands on the vehicle while in motion.

Reinforcement learning based autonomous multi-rotor landing on moving platforms

Multi-rotor UAVs suffer from a restricted range and flight duration due to limited battery capacity. Autonomous landing on a 2D moving platform offers the possibility to replenish batteries and offload data, thus increasing the utility of the vehicle. Classical approaches rely on accurate, complex and difficult-to-derive models of the vehicle and the environment. Reinforcement learning (RL) provides an attractive alternative due to its ability to learn a suitable control policy exclusively from data during a training procedure. However, current methods require several hours to train, have limited success rates and depend on hyperparameters that need to be tuned by trial-and-error. We address all these issues in this work. First, we decompose the landing procedure into a sequence of simpler, but similar learning tasks. This is enabled by applying two instances of the same RL based controller trained for 1D motion for controlling the multi-rotor’s movement in both the longitudinal and the lateral directions. Second, we introduce a powerful state space discretization technique that is based on i) kinematic modeling of the moving platform to derive information about the state space topology and ii) structuring the training as a sequential curriculum using transfer learning. Third, we leverage the kinematics model of the moving platform to also derive interpretable hyperparameters for the training process that ensure sufficient maneuverability of the multi-rotor vehicle. The training is performed using the tabular RL method Double Q-Learning. Through extensive simulations we show that the presented method significantly increases the rate of successful landings, while requiring less training time compared to other deep RL approaches. Furthermore, for two comparison scenarios it achieves comparable performance than a cascaded PI controller. Finally, we deploy and demonstrate our algorithm on real hardware. For all evaluation scenarios we provide statistics on the agent’s performance. Source code is openly available at https://github.com/robot-perception-group/rl_multi_rotor_landing.

UAV Detect and Avoid from UTM-Dependent Surveillance

A hierarchical unmanned aircraft system (UAS) traffic management (UTM) system has deployed 45 ground transceiver stations (GTS) for UAS services in Taiwan. This UTM system covers most areas for UAV-dependent surveillance using ADS-B Like technology. UTM Controller can monitor all UAV flights under transparent surveillance in low airspace. Controller-initiated UAV “detect and avoid” (DAA) mechanism assists UAV separation to ensure flight safety on UTM for small multirotor UAVs. From similar concept to traffic alert and collision avoidance system (TCAS) for the manned aircraft system, the UTM software executes DAA functions to generate approach alerts to UTM Controller. Conflict is detected by heading arrow extrapolation from multiple approaching UAVs by their time to conflict (TTC) on icons. Traffic advisory (TA) and resolution advisory (RA) are pronounced on UTM console to controllers. The less priority UAV pilot will receive the controller-pilot communication (CPC) to perform avoidance resolution. In UTM, the surveillance data period is broadcasting at 5~8 seconds on LoRa (long-range wide-area network) chip. Referring to TA=48 seconds and RA=24 seconds, the signal delay in ADS-B Like system to UTM server is about 0.5 seconds and CPC response is measured about 3~5 seconds. From real flight tests, the RA is enough for the less priority pilot to maneuver UAV for avoidance. From real flight tests, the proposed DAA mechanism based on UTM-dependent surveillance is feasible to resolve multiple approaches. The developing UTM system using ADS-B Like technology is also examined of high availability with redundant reliability and performance stability for flight safety.

A Survey of Offline- and Online-Learning-Based Algorithms for Multirotor Uavs

Multirotor UAVs are used for a wide spectrum of civilian and public domain applications. Their navigation controllers include onboard sensor suites that facilitate safe, autonomous or semi-autonomous multirotor flight, operation, and functionality under nominal and detrimental conditions and external disturbances, even when flying in uncertain and dynamically changing environments. During the last decade, given the available computational power, different learning-based algorithms have been derived, implemented, and tested to navigate and control, among other systems, multirotor UAVs. Learning algorithms have been and are used to derive data-driven based models, to identify parameters, to track objects, to develop navigation controllers, and to learn the environments in which multirotors operate. Learning algorithms combined with model-based control techniques have proven beneficial when applied to multirotors. This survey summarizes the research published since 2015, dividing algorithms, techniques, and methodologies into offline and online learning categories and then further classifying them into machine learning, deep learning, and reinforcement learning sub-categories. An integral part and focus of this survey is on online learning algorithms as applied to multirotors, with the aim to register the type of learning techniques that are either hard or almost hard real-time implementable, as well as to understand what information is learned, why, how, and how fast. The outcome of the survey offers a clear understanding of the recent state of the art and of the type and kind of learning-based algorithms that may be implemented, tested, and executed in real time.

Coordinated transportation of tethered multi-rotor UAVs based on differential graphical games

This paper investigates the stability control of the tethered multi-rotor unmanned aerial vehicles (T-MRUAVs) system in cargo delivery, where precise payload trajectory control is deemed unnecessary, with a focus on enhancing controller simplicity and robustness. To achieve this objective, it is imperative to employ an optimal formation tracking control, ensuring not only the symmetrical positioning of UAVs concerning the payload but also the achievement of distributed optimal solutions. Therefore, a formation tracking control strategy based on differential graphical games is designed to ensure optimized performance metrics and minimized suspended load swing. The designed control framework includes an outer loop and an inner loop control, which respectively achieve formation tracking and attitude stability. In addition to maintaining equilibrium states, each UAV is required to track the desired trajectory. Consequently, the formation tracking control consists of a gravity compensation term at the equilibrium point and a formation tracking control term based on differential graphical games. Then, a PID control strategy is displayed for the inner loop control, and the altitude control adopts feedback linearization. Finally, numerical simulations have validated the effectiveness of the designed algorithm.

Enhancement of multirotor UAV conceptual design through Machine Learning algorithms

Designing an efficient and optimized multirotor UAV requires laborious trade-off analyses, involving numerous design variables and mission requirement parameters, especially during the early conceptual design phase. The large number of unknown parameters, as well as the associated design effort often leads to non-optimal designs, for the sake of time efficiency. This work presents the implementation of a machine learning (ML) framework to assist and expedite the conceptual design phase of multirotor UAVs. The framework utilizes information from a comprehensive database of commercial lightweight multirotor UAVs. The database contains an extensive collection of crucial sizing parameters, performance metrics, and features associated with foldability and indoor guidance (e.g., obstacle avoidance sensors). These attributes specifically pertain to multirotor UAVs weighing less than 2kg, which exhibit diverse design and performance characteristics. The proposed ML framework employs multiple regression models (e.g. k-nearest neighbors regression, multi-layer perceptron regression) to predict the sizing parameters during a multirotor UAV’s conceptual design phase. This enables designers to make quick informed decisions, while also significantly reducing computational time and effort. Finally, the ML framework’s predictive capability is validated by comparing the predicted values with real-world data from an “unseen” test dataset.

CFD aided investigation of a three-blade propeller in multirotor UAV applications

In the recent years a rapid increase of multirotor UAVs in the commercial market is observed resulting in a large number of motor/propeller concepts and thrust architectures. The limited availability of data for the aerodynamic performance of the motor/propeller system often leads to a non-optimal operation on multirotor UAVs design points. Since experimental investigations are both cost- and time-demanding, the accurate CFD modeling of UAV propellers is crucial and highly supportive in the early design phases of multirotor UAVs. In the current study, a CFD framework is employed for the performance investigation of a small-scale three-blade propeller on a lightweight micro quadrotor UAV, designed for indoor search and rescue operations. More specifically, two widely implemented methods for propeller modeling are examined, namely the Multiple Reference Frame (MRF) and the Sliding Mesh (SM). Several operating points are investigated, corresponding to different propeller rotating speeds (RPM) and Reynolds numbers. The accuracy of each method is evaluated by comparing the CFD results with those obtained from literature experimental data. Finally, the uncertainty of the computational methods is quantified through Richardson’s extrapolation method.

Preliminary design of a multirotor UAV for indoor search and rescue applications

Multirotor UAVs have become an essential tool in a wider range of applications, including among others disaster management, and search and rescue (SAR) operations. Typically, these systems operate outdoors, with their guidance and positioning being based primarily on GPS. This work is focused on the design and optimization of a multirotor UAV specifically tailored for indoor SAR applications, where GPS signal is unavailable, and obstacles are prevalent. The design incorporates a lightweight frame structure, in order to increase the UAV’s payload capability. This is necessary, since the UAV requires multiple obstacle recognition and avoidance sensors, as well as thermal and optical cameras, to successfully accomplish its mission objectives in a GPS-denied environment. Towards this goal, various trade studies were conducted including different motor/propeller configurations and airframe FEM analyses. The aerodynamic performance of the UAV is evaluated also, using dedicated CFD analyses that incorporate the effect of propellers. Lastly, a prototype of the designed configuration is produced using additive manufacturing methods and initial flight tests of the UAV are performed.

Observer-based adaptive control for slung payload stabilization with a fully-actuated multirotor UAV

This article presents an observer-based adaptive sliding mode controller for a fully-actuated hexacopter unmanned aerial vehicle, designed for trajectory tracking in a perturbed environment while carrying a cable-suspended payload. Based on the unavailability of a payload swing sensor, an extended high-gain observer is designed to provide full-state and disturbance estimation including payload motion. Such disturbances are compensated into the control loop to dampen the oscillations, thus improving the flight performance of the hexacopter driven by the adaptive control, providing robustness against bounded perturbations and chattering reduction. The stability analysis of the observer-based controller is guaranteed through Lyapunov theory. Simulations using a multibody emulator demonstrate time reduction in payload dampening while controlling the aircraft trajectory, compared to a feedback regulation-based adaptive controller.

Experimental Characterization of Propeller-Induced Flow (PIF) below a Multi-Rotor UAV

The availability of multi-rotor UAVs with lifting capacities of several kilograms allows for a new paradigm in atmospheric measurement techniques, i.e., the integration of research-grade sonic anemometers for airborne turbulence measurements. With their ability to hover and move very slowly, this approach yields unrevealed flexibility compared to mast-based sonic anemometers for a wide range of boundary layer investigations that require an accurate characterization of the turbulent flow. For an optimized sensor placement, potential disturbances by the propeller-induced flow (PIF) must be considered. The PIF characterization can be done by CFD simulations, which, however, require validation. For this purpose, we conducted an experiment to map the PIF below a multi-rotor drone using a mobile array of five sonic anemometers. To achieve measurements in a controlled environment, the drone was mounted inside a hall at a 90° angle to its usual flying orientation, thus leading to the development of a horizontal downwash, which is not subject to a pronounced ground effect. The resulting dataset maps the PIF parallel to the rotor plane from two rotor diameters, beneath, to 10 D, and perpendicular to the rotor plane from the center line of the downwash to a distance of 3 D. This measurement strategy resulted in a detailed three-dimensional picture of the downwash below the drone in high spatial resolution. The experimental results show that the PIF quickly decreases with increasing distance from the centerline of the downwash in the direction perpendicular to the rotor plane. At a distance of 1 D from the centerline, the PIF reduced to less than 4 ms−1 within the first 5 D beneath the drone, and no conclusive disturbance was measured at 2 D out from the centerline. A PIF greater than 4 ms−1 was still observed along the center of the downwash at a distance of 10 D for both throttle settings tested (35% and 45%). Within the first 4 D under the rotor plane, flow convergence towards the center of the downwash was measured before changing to diverging, causing the downwash to expand. This coincides with the transition from the four individual downwash cores into a single one. The turbulent velocity fluctuations within the downwash were found to be largest towards the edges, where the shear between the PIF and the stagnant surrounding air is the largest.

A Deep Learning Approach of Intrusion Detection and Tracking with UAV-Based 360° Camera and 3-Axis Gimbal

Intrusion detection is often used in scenarios such as airports and essential facilities. Based on UAVs equipped with optical payloads, intrusion detection from an aerial perspective can be realized. However, due to the limited field of view of the camera, it is difficult to achieve large-scale continuous tracking of intrusion targets. In this study, we proposed an intrusion target detection and tracking method based on the fusion of a 360° panoramic camera and a 3-axis gimbal, and designed a detection model covering five types of intrusion targets. During the research process, the multi-rotor UAV platform was built. Then, based on a field flight test, 3043 flight images taken by a 360° panoramic camera and a 3-axis gimbal in various environments were collected, and an intrusion data set was produced. Subsequently, considering the applicability of the YOLO model in intrusion target detection, this paper proposes an improved YOLOv5s-360ID model based on the original YOLOv5-s model. This model improved and optimized the anchor box of the YOLOv5-s model according to the characteristics of the intrusion target. It used the K-Means++ clustering algorithm to regain the anchor box that matches the small target detection task. It also introduced the EIoU loss function to replace the original CIoU loss function. The target bounding box regression loss function made the intrusion target detection model more efficient while ensuring high detection accuracy. The performance of the UAV platform was assessed using the detection model to complete the test flight verification in an actual scene. The experimental results showed that the mean average precision (mAP) of the YOLOv5s-360ID was 75.2%, which is better than the original YOLOv5-s model of 72.4%, and the real-time detection frame rate of the intrusion detection was 31 FPS, which validated the real-time performance of the detection model. The gimbal tracking control algorithm for intrusion targets is also validated. The experimental results demonstrate that the system can enhance intrusion targets’ detection and tracking range.