UAV System Research Articles

Energy Efficient UAV Flight Control Method in an Environment with Obstacles and Gusts of Wind

This article presents an energy-efficient method of controlling unmanned aircraft (fixed-wing UAVs), which consists of three groups of algorithms: aerial vehicle route planning, in-flight control, and algorithms to correct the preplanned flight trajectory. All algorithms shall take into account the existence of obstacles that the UAV must avoid and wind gusts in the UAV’s area of operation. Tests were carried out on the basis of the UAV mathematical model, stabilization and navigation algorithms, and Dryden turbulence model, considering the parameters of the UAV’s propulsion system. The work includes a detailed description of constructing a network of connection that is used to plan a UAV mission. It presents the algorithm for determining the actual distances between the different points in the field of action, which takes into account the existence of obstacles. The algorithm shall be based on methods for determining the flight trajectory on a hexagonal grid. It presents the developed proprietary UAV path planning algorithm based on a model from a group of algorithms of mixed integer linear problem (MILP) optimization. It presents the manner in which the pre-prepared flight path was used by UAV controllers that supervised the flight along the preset path. It details the architecture of contemporary unmanned aerial vehicles, which have embedded capability to realize autonomous missions, which require the integration of UAV systems into the route planning algorithms set out in the article. Particular attention has been paid to the planning and implementation methods of UAV missions under conditions where wind gusts are present, which support the determination of UAV flight routes to minimize the vehicle’s energy consumption. The models developed were tested within a computer architecture based on ARM processors using the hardware-in-the-loop (HIL) technique, which is commonly used to control unmanned vehicles. The presented solution makes use of two computers: FCC (flight control computer) based on a real-time operating system (RTOS) and MC (mission computer) based on Linux and integrated with the Robot Operating System (ROS). A new contribution of this work is the integration of planning and monitoring methods for the implementation of missions aimed at minimizing energy consumption of the vehicle, taking into account wind conditions.

A Systematic Design of an Unmanned Aerial Vehicle for Surveillance Applications

In the past few years, the demand for Unmanned Aerial Vehicles, more commonly known as drones, has increased exponentially. This is mostly because they have huge applications in various areas like mapping, surveillance, payload delivery, aerial photography, wildlife surveillance, etc. VTOL, or Vertical Takeoff and Landing is a concept in which an aircraft or a UAV takes off and lands vertically. This concept has its own advantages over traditional fixed wing takeoff and landing systems. It does not require a large strip of land for takeoff, and the required lift can be generated from the thrust produced by the motors. It also enables a better and smoother landing than conventional fixed-wing systems. This paper presents a design for a quadcopter UAV system which can be used for surveillance in various disciplines. A unique concept of a hybrid frame has been introduced. This largely improves the stability and reduces vibrations to a great extent. Surveillance is carried out using Python, which detects objects using OpenCV and the YOLO algorithm

A Real-Time and Multi-Sensor-Based Landing Area Recognition System for UAVs

This paper presents a real-time and multi-sensor-based landing area recognition system for UAVs, which aims to enable UAVs to land safely on open and flat terrain and is suitable for comprehensive unmanned autonomous operation. The landing area recognition system for UAVs is built on the combination of a camera and a 3D LiDAR. The problem is how to fuse the image and point cloud information and realize the landing area recognition to guide the UAV landing autonomously and safely. To solve this problem, firstly, we use a deep learning method to realize the landing area recognition and tracking from images. After that, we project 3D LiDAR point cloud data into camera coordinates to obtain the semantic label of each point. Finally, we use the 3D LiDAR point cloud data with the semantic label to build the 3D environment map and calculate the most suitable area for UAV landing. Experiments show that the proposed method can achieve accurate and robust recognition of landing area for UAVs.

Colored Petri Net Modelling and Evaluation of Drone Inspection Methods for Distribution Networks.

The UAV industry is developing rapidly and drones are increasingly used for monitoring industrial facilities. When designing such systems, operating companies have to find a system configuration of multiple drones that is near-optimal in terms of cost while achieving the required monitoring quality. Stochastic influences such as failures and maintenance have to be taken into account. Model-based systems engineering supplies tools and methods to solve such problems. This paper presents a method to model and evaluate such UAV systems with coloured Petri nets. It supports a modular view on typical setup elements and different types of UAVs and is based on UAV application standards. The model can be easily adapted to the most popular flight tasks and allows for estimating the monitoring frequency and determining the most appropriate grouping and configuration of UAVs, monitoring schemes, air time and maintenance periods. An important advantage is the ability to consider drone maintenance processes. Thus, the methodology will be useful in the conceptual design phase of UAVs, in monitoring planning, and in the selection of UAVs for specific monitoring tasks.

Microservices for autonomous UAV inspection with UAV simulation as a service

Autonomous UAV systems are increasingly touted as the dominant future paradigm for inspecting civil infrastructure. Within this study, we have designed and developed a cloud system for high-level path planning, monitoring, and testing of autonomous UAV missions for inspecting infrastructures such as power lines, power towers, bridges, and railways. The software architecture is based on identified system’s functional and non-functional requirements. The cloud system is intended for UAV inspection operators and therefore should support multiple concurrent users. The microservice architecture has assured independence between functionalities, allowing independent scaling resulting in the fast processing time of near-optimal route calculation for UAVs reaching inspection targets. Furthermore, the independence between the services facilitates feature addition and future development. The system robustness is assured by containerizing services and continuously deploying to the Kubernetes cluster distributed across multiple worker nodes. Kubernetes scaling properties have enabled multiple concurrent users regardless of heavy computations for inspection paths. Application load testing has resulted in low processing time when individual services are scaled. Calculated inspection paths are validated for real-world inspection by employing the UAV Gazebo simulation based on a 3D dynamic model with an onboard flight controller, leading the UAV through the waypoints provided by the cloud system. By containerizing the simulation and deploying it within the cluster, we have enabled developers and users to test paths before sending the real-world UAVs to the inspection.

A Reasonable Alternative System for Searching UAVs in the Local Area.

UAVs, used for professional purposes, often intervene in unfamiliar terrain and challenging conditions. Unlike recreational UAVs, such professional and specialised UAVs are very expensive to develop and operate, and their value is not negligible. Due to the nature of operations in an unknown or dangerous environment, there are also situations with forced interruption and termination of the flight mission or a collision with the environment. Locating a lost vehicle presents a new challenge for UAV operators. The possibilities of today’s localised commercial aircraft in distress (COSPASS/SARSAT systems) are undesirable for selective special-purpose drones. The optimisation of the location in the event of an emergency or catastrophic landing may be justified by a social or other condition, where the user wants to search for the device by a system other than the one experienced for rescuing people, ideally on their reserved frequencies. The article proposes a new approach to solving the problem based on the design of a terrestrial localisation system based on the methods of processing and correlation of the obtained data by the physical principle of the Doppler effect and its own system adaptation. This creates an innovative concept of a targeting system based on the broadcasting of distress (VHF) signal by crashed UAV. This signal is captured and evaluated by the IDVOR system, making it possible to determine the direction in which the searched UAV is placed. In order to determine the difference between standard targeting systems of the UAV, which use information about position (exact coordinates (x,y,z)), the IDVOR system is able to determine direction, independent of other systems in every “enemy” or “inhospitable” territory.

RTSDM: A Real-Time Semantic Dense Mapping System for UAVs

Intelligent drones or flying robots play a significant role in serving our society in applications such as rescue, inspection, agriculture, etc. Understanding the scene of the surroundings is an essential capability for further autonomous tasks. Intuitively, knowing the self-location of the UAV and creating a semantic 3D map is significant for fully autonomous tasks. However, integrating simultaneous localization, 3D reconstruction, and semantic segmentation together is a huge challenge for power-limited systems such as UAVs. To address this, we propose a real-time semantic mapping system that can help a power-limited UAV system to understand its location and surroundings. The proposed approach includes a modified visual SLAM with the direct method to accelerate the computationally intensive feature matching process and a real-time semantic segmentation module at the back end. The semantic module runs a lightweight network, BiSeNetV2, and performs segmentation only at key frames from the front-end SLAM task. Considering fast navigation and the on-board memory resources, we provide a real-time dense-map-building module to generate an OctoMap with the segmented semantic map. The proposed system is verified in real-time experiments on a UAV platform with a Jetson TX2 as the computation unit. A frame rate of around 12 Hz, with a semantic segmentation accuracy of around 89% demonstrates that our proposed system is computationally efficient while providing sufficient information for fully autonomous tasks such as rescue, inspection, etc.

Wireless-Sensor Communication Based Wireless-Charging Coil Positioning System for UAVs With Maximum Power Point Tracking

Drone is a piece of power-hungry equipment whose battery drains within the first few minutes of its takeoff, and the user has to physically change the battery to a fully charged one. Implementation of wireless power transfer (WPT) in unmanned aerial vehicles has started becoming a reality. However, the technology is prone to many problems, especially misalignment between transmitter and receiver coils. The WPT system faces a sudden decline in power transfer with few millimeters of lateral misalignment between the transmitter and the receiver coil. The proposed sensor-based tracking system creates a two-way communication between the transmitter and the receiver coil systems for exchanging maximum power point information. The transmitter coil is adjusted directly beneath the receiver coil for maximum power transfer. The proposed WPT charging system reduces the misalignment issue by a significant factor and achieves maximum coupling efficiency. At 10 mm vertical distance, 0 mm lateral displacement between transmitter and receiver coils, the system achieved 91.3% power transfer efficiency by wirelessly receiving 70 W of power. The system offers practical, autonomous charging use cases in surveying, agriculture, and mapping applications.

A Data-Driven Dynamic Obstacle Avoidance Method for Liquid-Carrying Plant Protection UAVs

Autonomous sprayer UAVs are one of the most used aerial machines in modern agriculture. During flight missions, some common narrow obstacles appear in the flying zone. These are non-detectable from satellite images and one of the biggest challenges for autonomous sprayer UAVs in farmland. This work introduces an obstacle avoidance architecture specifically for sprayer UAVs. This architecture has generality in the spraying UAV problem, and it reduces the reliance on the global mapping of farmland. This approach computes the avoiding path based on the onboard sensor fusion system in real-time. Moreover, it autonomously determines the transition of several maneuver states using the current spraying liquid data and the UAV dynamics data obtained by offline system identification. This approach accurately tracks the avoidance path for the nonlinear time-variant spraying UAV systems. To verify the performance of the approach, we performed multiple simulations with different spraying missions, and the method demonstrated a high spraying coverage of more than 98% while successfully avoiding all vertical obstacles. We also demonstrated the adaptability of our control architecture; the safe distance between the UAV and obstacles can be changed by specifying the value of a high-level parameter on the controller. The proposed method adds value to precision agriculture, reduces mission time, and maximizes the spraying area coverage.

Motor-Level N-MPC for Cooperative Active Perception With Multiple Heterogeneous UAVs

This letter introduces a cooperative control framework based on Nonlinear Model Predictive Control (NMPC) for solving an Active Information Acquisition problem (AIA) using a system of multiple multirotor UAVs equipped with onboard sensors. The observation task of the NMPC is a minimum-uncertainty pose estimation of a moving feature which is observed by the multi-UAV system, using a cooperative Kalman filter. The controller considers a full nonlinear model of the multirotors – including the motor-level actuation units and their real constraints in terms of maximum torque – and embeds the Kalman filter estimation uncertainty in its prediction. The framework allows and exploits heterogeneity in the actuation and sensing systems by considering a generic model of UAV – including both quadrotors and tilted-propeller multirotors – and a generic model of range-and-bearing sensor with arbitrary rate and field of view. The capability of the proposed framework to reduce the cooperative estimation uncertainty of a static or a moving feature, thus leading the system to optimal sensing configurations, is demonstrated through Gazebo simulations and real experiments. The software is provided open-source.

Design of the Gradually Defect Patch Fractal Array Antenna for Unmanned Aerial Vehicle

This paper creatively proposes a new fractal iterative structure of a gradual defect patch and combines it with a rectangular array structure. Aiming at the performance requirements of the UAV system for multi-band compatible antennas, a gradual defect patch fractal array is designed. The radiation performance of the fractal array antenna was simulated by the method of moments, and antenna samples were made and the radiation performance was tested. Both simulation and actual measurement results show that this antenna has a working bandwidth of 5.5 GHz or more, a minimum return loss as low as -44 dB and excellent omnidirectional radiation capability. This antenna is compatible with the GPS system frequency band, Beidou system frequency band, all working frequency bands of the second to fifth generation mobile communication systems and the three working frequency bands of the RFID system, and is expected to be widely used as an unmanned aerial vehicle system antenna.

Improving UAV Radio Control System with 433 MHz Radio Wave Using Lo-Ra based on QCZEK Model Communication System

UAV must have a reliable control system considering the UAV is an unmanned vehicle that relies on human control on the ground. Most of control system used in UAVs uses a 2.4 GHz as it frequency. This frequency has disadvantaged for UAV because it is mainly used for Wi-Fi communication system so when users of UAV’s fly in urban areas where full of Wi-Fi system, there will be interference within UAV control system and Wi-Fi, so failsafe will often occur. From the theory, different frequencies produce different criteria for radiofrequency communication system to synchronize and manage and secure communication between transmitter and receiver. In this study, we will discuss the application of 433 MHz frequency for the control system for UAV. The goal of this research is to establish a reliable control system of UAV when used for the long-range mission. To make this control system, we use the Lo-Ra module with STM32 based Microcontroller designed for QCZEK TX/RX model communication. After several testing on the ground and the air, the purpose system serve 14 % longer distance than TBS system or market build system and 6 times longer distances than 2.4Ghz frequencies.

Precise Target Geo-Location of Long-Range Oblique Reconnaissance System for UAVs.

High-precision, real-time, and long-range target geo-location is crucial to UAV reconnaissance and target strikes. Traditional geo-location methods are highly dependent on the accuracies of GPS/INS and the target elevation, which restricts the target geo-location accuracy for LRORS. Moreover, due to the limitations of laser range and the common, real time methods of improving the accuracy, such as laser range finders, DEM and geographic reference data are inappropriate for long-range UAVs. To address the above problems, a set of work patterns and a novel geo-location method are proposed in this paper. The proposed method is not restricted by conditions such as the accuracy of GPS/INS, target elevation, and range finding instrumentation. Specifically, three steps are given, to perform as follows: First, calculate the rough geo-location of the target using the traditional method. Then, according to the rough geo-location, reimage the target. Due to errors in GPS/INS and target elevation, there will be a re-projection error between the actual points of the target and the calculated projection ones. Third, a weighted filtering algorithm is proposed to obtain the optimized target geo-location by processing the reprojection error. Repeat the above process until the target geo-location estimation converges on the true value. The geo-location accuracy is improved by the work pattern and the optimization algorithm. The proposed method was verified by simulation and a flight experiment. The results showed that the proposed method can improve the geo-location accuracy by 38.8 times and 22.5 times compared with traditional methods and DEM methods, respectively. The results indicate that our method is efficient and robust, and can achieve high-precision target geo-location, with an easy implementation.

High-performance intrusion detection system for networked UAVs via deep learning

High-performance intrusion detection system for networked UAVs via deep learning

EuroDRONE, a European Unmanned Traffic Management Testbed for U-Space

EuroDRONE is an Unmanned Traffic Management (UTM) demonstration project, funded by the EU’s SESAR organization, and its aim is to test and validate key UTM technologies for Europe’s ‘U-Space’ UTM program. The EuroDRONE UTM architecture comprises cloud software (DroNav) and hardware (transponder) to be installed on drones. The proposed EuroDRONE system is a Highly Automated Air Traffic Management System for small UAVs operating at low altitudes. It is a sophisticated, self-learning system based on software and hardware elements, operating in a distributed computing environment, offering multiple levels of redundancy, fail-safe algorithms for conflict prevention/resolution and assets management. EuroDRONE focuses its work on functionalities which involve the use of new communication links, the use of vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) technology to communicate information between drones and operators for safe and effective UTM functionality. Practical demonstrations that took place in Patras/Messolonghi in 2019 are presented and show the benefits and shortcomings of near-term UTM implementation in Europe.

Fault-tolerant control via four-leg inverter of a full-electric propulsion system for lightweight fixed-wing UAVs

The work deals with the development and the performance characterization of a novel control strategy for the detection, isolation and accommodation of coil faults in a three-phase Permanent Magnet Synchronous Motor (PMSM), used to drive the propeller of a modern lightweight fixed-wing UAV. The health-monitoring algorithms on motor currents (used to detect the open-circuit fault and to activate the control reconfiguration) are based on a slope method, associated to the evaluation of the current phasor trajectory in the Clarke plane. Actually, when an open-circuit fault occurs in PMSM driven by a standard three-leg converter, the typical circular trajectory of the current phasor in the Clarke plane collapses into a linear track and relevant torque ripples are generated. On the other hand, if the PMSM is driven by a four-leg converter, a control reconfiguration can be applied: the fourth leg of the power bridge is in stand-by when the system operates without faults, but it is enabled to regulate the current flowing at the central point of the Y connection of the 3-phase PMSM. The performances of the fault-tolerant algorithms are assessed via detailed nonlinear simulation of the propulsion system (including propeller loads, electrical faults, mechanical transmission compliance, digital signal processing and sensors errors). The results demonstrate that the health-monitoring algorithms and the fault-tolerant control strategies permit to obtain extremely small detection and isolation latencies, and negligible performance degradation in terms PMSM torque.

Design of Multi-band Compatible Trigeminal Monopole Array Antenna for Unmanned Aerial Vehicle

Compared with the performance index of UAV system antenna, a compatible trigeminal monopole array antenna is proposed. This antenna uses photosensitive resin as the substrate material, and can be made with UAV shell using 3D printing technology, realizing the integration of the antenna system and UAV. The antenna uses a trigeminal monopole antenna as the basic radiation unit, which is arranged in accordance with the Sierpinski fractal structure to form fractal radiation array. While enhancing the radiation through the array, it ensures that the antenna has a larger working bandwidth. In radiation performance test, the antenna is operating over a frequency spectrum of 1.038-11.916 GHz, and the minimum S11 is -37.92 dB. The proposed antenna has multi-band compatibility, better radiation performance, and greater performance redundancy.

Design of a Fixed-Wing UAV Controller Combined Fuzzy Adaptive Method and Sliding Mode Control

To overcome the complexity of the coupled nonlinear model of a fixed-wing UAV system and the uncertainty caused by a large number of interference factors, a control algorithm combining fuzzy adaptive control and sliding mode variable structure control was proposed. The controller algorithm mainly relies on the sliding mode variable structure control method to solve the control problem of the strongly coupled complex nonlinear system. Based on sliding mode control, a fuzzy adaptive method is introduced to reduce the chattering problem of the traditional sliding mode control, and the uncertain parameters and unknown functions caused by external disturbances are approximated by this method. In this study, two types of fuzzy adaptive sliding mode controller were designed according to the different object ranges of the fuzzy adaptive algorithm. In addition, the stability of the controllers was verified using the Lyapunov method. Finally, numerical simulations are performed to demonstrate the effectiveness of the proposed controllers by comparing with the traditional sliding mode controller.

Smart building demolition and waste management frame with image-to-BIM

Increasing urbanization has resulted in a large number of building demolition, generating massive volumes of construction and demolition waste (CDW) in many countries. However, adjustments and deterioration to existing structures are poorly documented, yielding a data gap before the demolition work. Additionally, the entire demolition process relies heavily on on-site operations, pre-demolition audit and efficient construction and demolition waste management (CDWM). To improve the efficiency and informatization of building demolition and CDWM, this article proposes a smart framework based on Image-to-BIM technique. Within this frame, a combined UAV and handheld camera system is presented to collect interior and exterior images of to-be-demolished buildings automatically and completely, in which camera motion path planning is accomplished by considering the target region. The proposed algorithm could detect the line features in the image-based reconstruction model and extracts geometric data to get the required BIM model. Additional frame components dedicated to demolition planning and waste management enable schedule optimizing, safety assessment, CDW quantity take-off, and economic estimation using the data from the BIM model. The application of the framework in a masonry structure showcases how the integrated system improves the information flow during building deconstruction and CDWM, realizing a potentially smart deconstruction activity.

A Distributed Optimal Formation Control for Multi-Agent System of UAVs

A Distributed Optimal Formation Control for Multi-Agent System of UAVs