Autonomous Aerial Vehicles Research Articles

Methodologies for landing autonomous aerial vehicles on maritime vessels

One of the key challenges to the safe operation of UAVs at sea is the relative motion that exists between the UAV and ship during landing. The scope of this work is the development and evaluation of methodologies for improving UAV landing performance. The new methodologies are known as the Signal Prediction Algorithm (SPA), Active Heave Compensation (AHC) and the Landing Period Indicator (LPI). To promote interoperability, the methodologies do not require any specific ship equipment.To evaluate the methodologies on an existing comprehensive UAV model, ShipMo3D was used to generate 105 sets of ship motion in sea states 2–6, headings 0–180∘ and speeds 6–10 knots. Within the simulation the UAV, equipped with a Light Detection and Ranging (LIDAR) system, measures the ship motion in situ. Using the Signal Prediction Algorithm (SPA), the UAV forecasts the ship motion and potential landing opportunities. The UAV can also use the SPA and the AHC system to maintain a safe hover position above the ship deck and determine landing trajectories with a specific touchdown velocity. The UAV can also employ the novel Landing Period Indicator (LPI) system which calculates an estimate of the ship's energy and determines opportune times for safe landings.The results indicate that the methodologies can improve the landing performance of autonomous helicopters. For the 105 sets of ship motion, using the combination of the SPA, AHC, and LPI improved landing success by up to 34% when compared to a common landing controller.

GEOMATIC TECHNIQUES FOR THE OPTIMIZATION OF SKI RESOURCES

Abstract. Climate change is already affecting the entire world, with extreme weather conditions such as drought, heat waves, heavy rain, floods and landslides becoming more frequent, including Europe. In according to Paris agreement and relative European announcement of Carbon neutrality (by 2050), the saving of water and energy supplies is a fundamental aspect in the management of resources in production, sports, hospitality facilities and so on. Some methodologies for the optimization of the consumption of natural resources are required. This article describes an activity aimed at measuring, monitoring and analysing the thickness of the snowpack on the ski slopes during the winter season to permit a sustainable approach of snowmaking in alpine ski areas . The authors propose a methodology based on the integration of multitemporal surface (ground/snow) survey by Autonomous Aerial Vehicle (AAV) and low cost GNSS receivers mounted on snow groomers for a RTK (Real Time Kinematic) solution. To obtain a complete snow surface digital models with poor detailed images on ski slopes, some pre-processing techniques have been analysed to locally improve contrast and details with a local high pass filtering. The methodology has been employed in two study areas (Limone Piemonte, Prato Nevoso) located in the province of Cuneo, in the southern alpine area of Piedmont.

Autonomous Aerial Vehicles (AAVs) as a Tool for Improving the Spatial Resolution of Snow Albedo Measurements in Mountainous Regions

We present technical advances and methods to measure effective broadband physical albedo in snowy mountain headwaters using a prototype dual-sensor pyranometer mounted on an Autonomous Aerial Vehicle (an AAV). Our test flights over snowy meadows and forested areas performed well during both clear sky and snowy/windy conditions at an elevation of ~2650 m above mean sea level (MSL). Our AAV-pyranometer platform provided high spatial (m) and temporal resolution (sec) measurements of effective broadband (310–2700 nm) surface albedo. The AAV-based measurements reveal spatially explicit changes in landscape albedo that are not present in concurrent satellite measurements from Landsat and MODIS due to a higher spatial resolution. This AAV capability is needed for validation of satellite snow albedo products, especially over variable montane landscapes at spatial scales of critical importance to hydrological applications. Effectively measuring albedo is important, as annually the seasonal accumulation and melt of mountain snowpack represent a dramatic transformation of Earth’s albedo, which directly affects headwaters’ water and energy cycles.

Precise Landing of Autonomous Aerial Vehicles Using Vector Fields

This letter proposes a strategy for autonomous precision landing of aerial vehicles in fixed pads or runways. Our approach is based on a velocity vector field, which is a closed-loop strategy that guides the vehicle towards the landing point and simultaneously enforces the shape of its trajectory. Relative position between the vehicle and the landing zone, which can be easily determined using visual sensors installed on-board, is the only information necessary to perform the landing. We present vector fields that can be used to land both fixed- and rotary-wing autonomous aircrafts. We also show experiments and simulations with a quadrotor equipped with a monocular camera to illustrate the characteristics of the proposed approach.

Data Comets: Designing a Visualization Tool for Analyzing Autonomous Aerial Vehicle Logs with Grounded Evaluation

AbstractAutonomous unmanned aerial vehicles are complex systems of hardware, software, and human input. Understanding this complexity is key to their development and operation. Information visualizations already exist for exploring flight logs but comprehensive analyses currently require several disparate and custom tools. This design study helps address the pain points faced by autonomous unmanned aerial vehicle developers and operators. We contribute: a spiral development process model for grounded evaluation visualization development focused on progressively broadening target user involvement and refining user goals; a demonstration of the model as part of developing a deployed and adopted visualization system; a data and task abstraction for developers and operators performing post‐flight analysis of autonomous unmanned aerial vehicle logs; the design and implementation of Data Comets, an open‐source and web‐based interactive visualization tool for post‐flight log analysis incorporating temporal, geospatial, and multivariate data; and the results of a summative evaluation of the visualization system and our abstractions based on in‐the‐wild usage. A free copy of this paper and source code are available at osf.io/h4p7g

Efficient Method and Architecture for Real-Time Video Defogging

Real-time video defogging has a huge demand in intelligent transportation, advanced driver assistance systems (ADAS), long-range surveillance, autonomous aerial vehicles and endoscopic surgery. Most of these applications are constrained by stringent frame rate, power and memory budget. Till date, the methods devised for such requirements are very rare. Therefore, this paper proposes an efficient method and very-large-scale integration (VLSI) architecture for resource-constrained embedded system targeting real-time video defogging. The method and architecture are co-designed to achieve high throughput while consuming less resources and power. The architecture is divided into four parts namely, atmospheric light estimation unit, airlight adjustment unit, transmission estimation unit, and pixel restoration unit. The atmospheric light estimation unit employs a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3\times 3$ </tex-math></inline-formula> tile based approach that eliminates the requirement of large buffer memory for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$15\times 15$ </tex-math></inline-formula> support size. The transmission is estimated using dark channel prior and gradient threshold based Gaussian filtering approach. In order to overcome flickering artifacts, an adaptive airlight updating scheme is employed. From the quantitative and qualitative evaluations, it is observed that the proposed method outperforms the existing hardware approaches. Furthermore, the field-programmable gate array (FPGA) and application-specific integrated circuit (ASIC) implementations of the architecture achieve a high throughput of 200 MPixels/s and 600 MPixels/s, respectively. The ASIC implementation dissipates 9.03 mW power at 200MHz. Moreover, the proposed design does not require any external memory such as dynamic random-access memory (DRAM), thus making it suitable for on-chip processing that can be closely integrated with an image sensor.

Exact flow of particles using for state estimations in unmanned aerial systems` navigation

The navigation is a substantial issue in the field of robotics. Simultaneous Localization and Mapping (SLAM) is a principle for many autonomous navigation applications, particularly in the Global Navigation Satellite System (GNSS) denied environments. Many SLAM methods made substantial contributions to improve its accuracy, cost, and efficiency. Still, it is a considerable challenge to manage robust SLAM, and there exist several attempts to find better estimation algorithms for it. In this research, we proposed a novel Bayesian filtering based Airborne SLAM structure for the first time in the literature. We also presented the mathematical background of the algorithm, and the SLAM model of an autonomous aerial vehicle. Simulation results emphasize that the new Airborne SLAM performance with the exact flow of particles using for recursive state estimations superior to other approaches emerged before, in terms of accuracy and speed of convergence. Nevertheless, its computational complexity may cause real-time application concerns, particularly in high-dimensional state spaces. However, in Airborne SLAM, it can be preferred in the measurement environments that use low uncertainty sensors because it gives more successful results by eliminating the problem of degeneration seen in the particle filter structure.

A Hybrid Command Governor Scheme for Rotary Wings Unmanned Aerial Vehicles

In this paper, we develop an obstacle avoidance control scheme for autonomous aerial vehicles. The strategy is based on command governor (CG) ideas that are here extended to take into account nonconvex constraints typically arising in path planning obstacle avoidance problems. As one of its main merits, the collision avoidance task is accomplished by exploiting convex inner approximations of the obstacle-free region and an ad hoc constraints switching procedure which significantly reduces the online computations pertaining to the CG design. Experimental results on the quadrotor Qball-X4 show the applicability and effectiveness of the proposed approach.

Dronument: System for Reliable Deployment of Micro Aerial Vehicles in Dark Areas of Large Historical Monuments

This letter presents a self-contained system for robust deployment of autonomous aerial vehicles in environments without access to global navigation systems and with limited lighting conditions. The proposed system, application-tailored for documentation in dark areas of large historical monuments, uses a unique and reliable aerial platform with a multi-modal lightweight sensory setup to acquire data in human-restricted areas with adverse lighting conditions, especially in areas that are high above the ground. The introduced localization method relies on an easy-to-obtain 3-D point cloud of a historical building, while it copes with a lack of visible light by fusing active laser-based sensors. The approach does not rely on any external localization, or on a preset motion-capture system. This enables fast deployment in the interiors of investigated structures while being computationally undemanding enough to process data online, onboard an MAV equipped with ordinary processing resources. The reliability of the system is analyzed, is quantitatively evaluated on a set of aerial trajectories performed inside a real-world church, and is deployed onto the aerial platform in the position control feedback loop to demonstrate the reliability of the system in the safety-critical application of historical monuments documentation.

Performance Analysis of Medium Altitude Low-Cost Autonomous Quadcopter

In modern engineering, the rapid growth in the development of unmanned aerial vehicles which deserves the major space in the area of intelligence and surveillance operations. Unlike the conventional aircraft, the number of rotors mounted in an autonomous body that determines the propulsive efficiency as well as the maneuvering capabilities of unmanned aerial vehicles. The main objective of this research reveals that the low cost and an autonomous aerial vehicle with four rotors configuration design to achieve the long endurance flight limit. The developed Quadcopter flying model integrated with Arduino autopilot module which effectively controls the various maneuvering actions during the flight. This paper also reveals the entire hardware structure of the designed Quadcopter and its flight dynamic response characteristics. The remotely operated Quadcopter model was subjected to the flight test and the performance parameters have recorded. The flight test was conducted in an open environment in which stability parameters has been observed during onboard. Pitch angle, the Rotational speed of four rotors and the various attitudes of Quadcopter were changed accordingly to maintain the steady level flight. Findings are plotted with respect to the flight endurance.

Learning from animals: How to Navigate Complex Terrains.

We develop a method to learn a bio-inspired motion control policy using data collected from hawkmoths navigating in a virtual forest. A Markov Decision Process (MDP) framework is introduced to model the dynamics of moths and sparse logistic regression is used to learn control policy parameters from the data. The results show that moths do not favor detailed obstacle location information in navigation, but rely heavily on optical flow. Using the policy learned from the moth data as a starting point, we propose an actor-critic learning algorithm to refine policy parameters and obtain a policy that can be used by an autonomous aerial vehicle operating in a cluttered environment. Compared with the moths’ policy, the policy we obtain integrates both obstacle location and optical flow. We compare the performance of these two policies in terms of their ability to navigate in artificial forest areas. While the optimized policy can adjust its parameters to outperform the moth’s policy in each different terrain, the moth’s policy exhibits a high level of robustness across terrains.

A Framework for Anomaly Detection in Time-Driven and Event-Driven Processes using Kernel Traces

Model-checking and verification using Kripke structures and computational tree logic* (CTL*) use abstractions from the model/process/application to create the state-transition graphs that verify the model behavior. This scheme of profiling the performance of a process imports that the depth of the process operation correlates with the level abstraction. However, because of state explosion problems, these abstractions tend to restrict the scope to create manageable execution states. Therefore, for context modeling, this procedure does not generate a fine-grained behavioral model as generated states limit the ability of the abstraction to capture the execution time interactions amongst the processes, the hardware, and the kernel. Hence, in this paper, we present an end-to-end framework that comprises auto-encoders and probabilistic models to understand the behavior of system processes and detect deviant behaviors. We test this framework with a publicly available dataset generated from an autonomous aerial vehicle (UAV) application and the results show that by creating a fine-grained model that exploits previously unharnessed properties of the system calls, we can create a dynamic anomaly detection framework that evolves as the threats change.

Nonlinear Model Predictive Control for the Landing of a Quadrotor on a Marine Surface Vehicle

Abstract The paper addresses the design of a control strategy for a quadrotor autonomous aerial vehicle to land on a target marine surface vehicle. In particular, a Nonlinear Model Predictive Control law that takes into account the marine vehicle trajectory and the sea state has been designed to let the quadrotor land on the target vehicle when this reaches a wave peak. The sea state has been modeled using monochromatic sinusoidal waves, and the maximum wave height and period are taken into account by the quadrotor control law to compute the position and the timing of the next vertical peak in the target vehicle trajectory. The results of numerical simulations performed in ROS/Gazebo environment are shown to validate the control strategy effectiveness.

A Project-Oriented Course on Guidance and Control of Autonomous Aerial Vehicles

In this paper, a project oriented course on guidance and control of autonomous aerial vehicles (UAVs) is presented. The paper describes the different modules of the course and how they are addressed using the project oriented approach. The project uses a quadrotor UAV that is used as the case study during the course. In this course, the students learn how to mathematically model quadrotor UAV flight characteristics, develop feedback control algorithms to enable stable flight control and fuse sensor measurements using Kalman filter techniques to estimate the UAV position and orientation. Students develop these concepts through both simulation and interaction with UAV real measurements. Throughout the course, students build a full 6-degree-of-freedom simulation of controlled quadrotor UAV flight using MATLAB and Simulink.

Secondary Radar Beacons for Local Ad-Hoc Autonomous Robot Localization Systems.

In this paper, we present a detailed analysis and implementation of secondary radar beacons designed for a local ad-hoc localization and landing system (LAOLa) to support the navigation of autonomous ground and aerial vehicles. We discuss a switched linear feedback network as a virtually coherent oscillator and show how to use it as a secondary radar transponder. Further, we present a signal model for the beat signal of the transponder response in an FMCW radar system, which is more detailed than in previously published papers. An actual transponder realization in the 24 ISM band is presented. Its RF performance was evaluated both in the laboratory and in the field. Finally, we put forward some ideas on how to overcome the range measurement inaccuracy inherent in this transponder concept.

Un Controlador Basado en la Fusión de Controladores PVTOL para Navegación 3D de Vehículos Aéreos Autónomos

En este trabajo se propone una arquitectura de control basada en la fusión de las acciones de control generadas por tres controladores sencillos (controladores PVTOL, del inglés Planar Vertical Take-Off and Landing) para guiar vehículos aéreos autónomos en posicionamiento y seguimiento de caminos 3D. Considerando la navegación como una tarea compuesta por el control de la orientación del vehículo en relación al blanco (asociada a un controlador Z-PVTOL), por el control de su movimiento hacia adelante (asociado a un controlador XZ-PVTOL), y por la corrección de cualquier desplazamiento lateral (asociada a un controlador YZ-PVTOL, similar al controlador XZ-PVTOL), nuestro primer abordaje consistió en ejecutar dichas maniobras parciales una por vez, utilizando los controladores PVTOL, conmutando de uno al otro mediante la acción de un supervisor. En este trabajo el abordaje se cambió, pasando a considerarse que los controladores PVTOL actúan simultáneamente y generando la acción de control final mediante la fusión de las acciones de control generadas por ellos, acoplando así suavemente tres controladores muy sencillos. Se presentan resultados experimentales para validar el abordaje propuesta, y su desempeño en vuelo es comparado con el desempeño correspondiente a la estrategia de conmutación.

Guidance control to capture a target using communication between the autonomous aerial vehicle and remote sensors

In this study, the authors introduce a three-dimensional (3D) discrete-time motion camouflage (MC) control to make an autonomous aerial vehicle (AAV) chase a manoeuvring target while not rotating the bearing line between the AAV and the target. This study handles a scenario in which the AAV can measure the target's position using remote sensors (e.g. ground-based laser systems) only. Since there exists communication delay between the remote sensors and the AAV, time delay exists between the time instant when the target's position is measured and the time instant when the AAV applies controllers based on sensor measurements. Considering a scenario where the time delay is accessible, the authors develop a 3D MC control based on the time delay explicitly. They prove that as long as several assumptions are satisfied, the angular rate of bearing line is exactly zero at all sampling indexes while the distance between the target and the AAV monotonically decreases as time elapses. The effectiveness of the 3D discrete-time MC control is verified utilising extensive MATLAB simulations.

Mitigating ground effect on mini quadcopters with model reference adaptive control

Mitigating ground effect becomes a big challenge for autonomous aerial vehicles when they are flying in close proximity to the ground. This paper aims to develop a precise model of ground effect on mini quadcopters, provide an advanced control algorithm to counter the model uncertainty and, as a result, improves the command tracking performance when the vehicle is in the ground effect region. The mathematical model of ground effect has been established through a series of experiments and validated by a flight test. The experiments show that the total thrust generated by rotors increases linearly as the vehicle gets closer to the ground, which is different from the commonly-used ground effect model for a single rotor vehicle. In addition, the model switches from a piecewise linear to a quadratic function when the rotor to rotor distance is increased. A control architecture that utilizes the model reference adaptive controller (MRAC) has also been designed, where MRAC is added to the altitude loop. The performance of the proposed control algorithm has been evaluated through a set of flight tests on a mini quadcopter platform and compared with a traditional proportional–integral–derivative (PID) controller. The results demonstrate that MRAC dramatically improves the tracking performance of altitude command and can reduce the rise time by $$80\%$$ under the ground effect.

Cooperative Optimization of UAVs Formation Visual Tracking

The use of unmanned vehicles to perform tiring, hazardous, repetitive tasks, is becoming a reality out of the academy laboratories, getting more and more interest for several application fields from the industrial, to the civil, to the military contexts. In particular, these technologies appear quite promising when they employ several low-cost resource-constrained vehicles leveraging their coordination to perform complex tasks with efficiency, flexibility, and adaptation that are superior to those of a single agent (even if more instrumented). In this work, we study one of said applications, namely the visual tracking of an evader (target) by means of a fleet of autonomous aerial vehicles, with the specific aim of focusing on the target so as to perform an accurate position estimation while concurrently allowing a wide coverage over the monitored area so as to limit the probability of losing the target itself. These clearly conflicting objectives call for an optimization approach that is here developed: by considering both aforementioned aspects and the cooperative capabilities of the fleet, the designed algorithm allows controling in real time the single fields of view so as to counteract evasion maneuvers and maximize an overall performance index. The proposed strategy is discussed and finally assessed through the realistic Gazebo-ROS simulation framework.

TrackerBots: Autonomous unmanned aerial vehicle for real‐time localization and tracking of multiple radio‐tagged animals

AbstractAutonomous aerial robots provide new possibilities to study the habitats and behaviors of endangered species through the efficient gathering of location information at temporal and spatial granularities not possible with traditional manual survey methods. We present a novel autonomous aerial vehicle system—TrackerBots—to track and localize multiple radio‐tagged animals. The simplicity of measuring the received signal strength indicator (RSSI) values of very high frequency (VHF) radio‐collars commonly used in the field is exploited to realize a low‐cost and lightweight tracking platform suitable for integration with unmanned aerial vehicles (UAVs). Due to uncertainty and the nonlinearity of the system based on RSSI measurements, our tracking and planning approaches integrate a particle filter for tracking and localizing and a partially observable Markov decision process for dynamic path planning. This approach allows autonomous navigation of a UAV in a direction of maximum information gain to locate multiple mobile animals and reduce exploration time and, consequently, conserve on‐board battery power. We also employ the concept of search termination criteria to maximize the number of located animals within power constraints of the aerial system. We validated our real‐time and online approach through both extensive simulations and field experiments with five VHF radio‐tags on a grassland plain.