Autonomous Take-off Research Articles

Autonomous landing of a quadrotor on a moving platform using motion capture system

This paper investigates the challenging problem of the autonomous landing of a quadrotor on a moving platform in a non-cooperative environment. The limited sensing ability of quadrotors often hampers their utilization for autonomous landing, especially in GPS-denied areas. The performance of motion capture systems (MCSs) in many application areas is the motivation to utilize them for the autonomous take-off and landing of the quadrotor in this research. An autonomous closed-loop vision-based navigation, tracking, and control system is proposed for quadrotors to perform landing based upon Model Predictive Control (MPC) by utilizing multi-objective functions. The entire process is posed as a constrained tracking problem to minimize energy consumption and ensure smooth maneuvers. The proposed approach is fully autonomous from take-off to landing; whereas, the movements of the landing platform are pre-defined but still unknown to the quadrotor. The landing performance of the quadrotor is tested and evaluated for three different movement patterns: static, square-shaped, and circular-shaped. Through experimental results, the pose error between the quadrotor and the platform is measured and found to be less than 30 cm.

Classification of Soybean Genotypes as to Calcium, Magnesium, and Sulfur Content Using Machine Learning Models and UAV–Multispectral Sensor

Making plant breeding programs less expensive, fast, practical, and accurate, especially for soybeans, promotes the selection of new soybean genotypes and contributes to the emergence of new varieties that are more efficient in absorbing and metabolizing nutrients. Using spectral information from soybean genotypes combined with nutritional information on secondary macronutrients can help genetic improvement programs select populations that are efficient in absorbing and metabolizing these nutrients. In addition, using machine learning algorithms to process this information makes the acquisition of superior genotypes more accurate. Therefore, the objective of the work was to verify the classification performance of soybean genotypes regarding secondary macronutrients by ML algorithms and different inputs. The experiment was conducted in the experimental area of the Federal University of Mato Grosso do Sul, municipality of Chapadão do Sul, Brazil. Soybean was sown in the 2019/20 crop season, with the planting of 103 F2 soybean populations. The experimental design used was randomized blocks, with two replications. At 60 days after crop emergence (DAE), spectral images were collected with a Sensifly eBee RTK fixed-wing remotely piloted aircraft (RPA), with autonomous takeoff control, flight plan, and landing. At the reproductive stage (R1), three leaves were collected per plant to determine the macronutrients calcium (Ca), magnesium (Mg), and sulfur (S) levels. The data obtained from the spectral information and the nutritional values of the genotypes in relation to Ca, Mg, and S were subjected to a Pearson correlation analysis; a PC analysis was carried out with a k-means algorithm to divide the genotypes into clusters. The clusters were taken as output variables, while the spectral data were used as input variables for the classification models in the machine learning analyses. The configurations tested in the models were spectral bands (SBs), vegetation indices (VIs), and a combination of both. The combination of machine learning algorithms with spectral data can provide important biological information about soybean plants. The classification of soybean genotypes according to calcium, magnesium, and sulfur content can maximize time, effort, and labor in field evaluations in genetic improvement programs. Therefore, the use of spectral bands as input data in random forest algorithms makes the process of classifying soybean genotypes in terms of secondary macronutrients efficient and important for researchers in the field.

Delayed Kalman filter for vision-based autonomous flight in ocean environments

This paper presents the developments of flight hardware and software for a multirotor unmanned aerial vehicle, performing autonomous take-off and landing on a moving vessel in ocean environments. The proposed flight hardware is composed of a general-purpose computing module connected to a low-cost inertial measurement, an real-time kinematics GPS, motor speed controller, and a camera, through a custom-made printed circuit board. The flight software is developed in C++ with multi-threading such that the multiple tasks of control, estimation, and communication are executed simultaneously. The proposed flight system is verified with autonomous flight experiments on a United States Naval Academy research vessel operating in Chesapeake Bay. Two types of flight experiments are performed: autonomous flight utilizing real-time kinematics GPS for relative positioning, and vision-based autonomous flight, both for shipboard launch and landing.

Autonomous 3D Position Control for a Safe Single Motor Micro Aerial Vehicle

We present the Maneuverable Piccolissimo 2 (MP2), an autonomous, controllable, single motor micro aerial vehicle (MAV). The small size of MP2 makes it safe to operate in the presence of humans, and its simple design facilitates the creation of large swarms of capable MAVs. MP2 is equipped with on-board sensing capabilities and uses active environmental beacons to compute its three-dimensional position and yaw orientation. Its novel design enables autonomous takeoff, flight, and landing while maintaining a small, simple form factor. We describe a feedback controller and demonstrate its feasibility in a series of flight tests that display position holding, step response, and path following capabilities. The results indicate that MP2 is capable of controlled autonomous 3D flight with only one actuator.

Aerodynamics of Landing Maneuvering of an Unmanned Aerial Vehicle in Close Proximity to a Ground Vehicle

<div class="section abstract"><div class="htmlview paragraph">Autonomous takeoff and landing maneuvers of an unmanned aerial vehicle (UAV) from/on a moving ground vehicle (GV) have been an area of active research for the past several years. For military missions requiring repeated flight operations of the UAV, precise landing ability is important for autonomous docking into a recharging station, since such stations are often mounted on a ground vehicle. The development of precise and efficient control algorithms for this autonomous maneuvering has two key challenges; one is related to flight aerodynamics and the other is related to a precise detection of the landing zone. The aerodynamic challenges include understanding the complex interaction of the flows over the UAV and GV, potential ground effects at the proximity of the landing surface, and the impact of the variations in the surrounding wind flow and ambient conditions. While a large body of work in this area can be found on the control aspect of the UAV landing and takeoff maneuvers, research on the aerodynamic aspects of such maneuvers is non-existent. This paper presents an in-depth computational fluid dynamics (CFD) based aerodynamic characterization of the transient flow fields associated with the landing of a hobby-model quadcopter (the UAV) on an idealized road vehicle (the GV), the 35-degree slant angle Ahmed body. Transient improved delayed detached eddy simulations (IDDES) are carried out using the commercial CFD code STAR-CCM+. Our study indicates that the pressure field is the first flow property that gets impacted by the proximity of the UAV to the GV.</div></div>

Vehicle-Mounted Platform for Autonomous Take-off and Landing of Unmanned Aerial Vehicles

By expounding and proving the technical background of and demand for the platform for the take-off and landing of unmanned aerial vehicles (UAVs), and by analyzing the necessity in combination with current domestic and overseas situations, this paper reveals how a vehicle-mounted platform for take-off and landing of UAVs consisting of multiple connecting rods was developed. Meanwhile, the feasibility of the technology of the vehicle-mounted platform for take-off and landing consisting of multiple connecting rods was verified from multiple aspects by utilizing precision transmission technology, a general mechanical model, and a proof-of-principle prototype.

Machine Learning in the Classification of Soybean Genotypes for Primary Macronutrients’ Content Using UAV–Multispectral Sensor

Using spectral data to quantify nitrogen (N), phosphorus (P), and potassium (K) contents in soybean plants can help breeding programs develop fertilizer-efficient genotypes. Employing machine learning (ML) techniques to classify these genotypes according to their nutritional content makes the analyses performed in the programs even faster and more reliable. Thus, the objective of this study was to find the best ML algorithm(s) and input configurations in the classification of soybean genotypes for higher N, P, and K leaf contents. A total of 103 F2 soybean populations were evaluated in a randomized block design with two repetitions. At 60 days after emergence (DAE), spectral images were collected using a Sensefly eBee RTK fixed-wing remotely piloted aircraft (RPA) with autonomous take-off, flight plan, and landing control. The eBee was equipped with the Parrot Sequoia multispectral sensor. Reflectance values were obtained in the following spectral bands (SBs): red (660 nm), green (550 nm), NIR (735 nm), and red-edge (790 nm), which were used to calculate the vegetation index (VIs): normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), green normalized difference vegetation index (GNDVI), soil-adjusted vegetation index (SAVI), modified soil-adjusted vegetation index (MSAVI), modified chlorophyll absorption in reflectance index (MCARI), enhanced vegetation index (EVI), and simplified canopy chlorophyll content index (SCCCI). At the same time of the flight, leaves were collected in each experimental unit to obtain the leaf contents of N, P, and K. The data were submitted to a Pearson correlation analysis. Subsequently, a principal component analysis was performed together with the k-means algorithm to define two clusters: one whose genotypes have high leaf contents and another whose genotypes have low leaf contents. Boxplots were generated for each cluster according to the content of each nutrient within the groups formed, seeking to identify which set of genotypes has higher nutrient contents. Afterward, the data were submitted to machine learning analysis using the following algorithms: decision tree algorithms J48 and REPTree, random forest (RF), artificial neural network (ANN), support vector machine (SVM), and logistic regression (LR, used as control). The clusters were used as output variables of the classification models used. The spectral data were used as input variables for the models, and three different configurations were tested: using SB only, using VIs only, and using SBs+VIs. The J48 and SVM algorithms had the best performance in classifying soybean genotypes. The best input configuration for the algorithms was using the spectral bands as input.

Precision Landing Tests of Tethered Multicopter and VTOL UAV on Moving Landing Pad on a Lake.

Autonomous take-off and landing on a moving landing pad are extraordinarily complex and challenging functionalities of modern UAVs, especially if they must be performed in windy environments. The article presents research focused on achieving such functionalities for two kinds of UAVs, i.e., a tethered multicopter and VTOL. Both vehicles are supported by a landing pad navigation station, which communicates with their ROS-based onboard computer. The computer integrates navigational data from the UAV and the landing pad navigational station through the utilization of an extended Kalman filter, which is a typical approach in such applications. The novelty of the presented system is extending navigational data with data from the ultra wide band (UWB) system, and this makes it possible to achieve a landing accuracy of about 1 m. In the research, landing tests were carried out in real conditions on a lake for both UAVs. In the tests, a special mobile landing pad was built and based on a barge. The results show that the expected accuracy of 1 m is indeed achieved, and both UAVs are ready to be tested in real conditions on a ferry.

Guidance Law for Autonomous Takeoff and Landing of Unmanned Helicopter on Mobile Platform Based on Asymmetric Tracking Differentiator

For some flight missions, such as autonomous landing on mobile platforms, the demands on indicators such as target-tracking accuracy and so on are relatively high. To achieve this, a guidance system with excellent precision is necessary. An asymmetric tracking differentiator based on a tracking differentiator is proposed to establish the guidance system. On the basis of the proposed asymmetric tracking differentiator, an altitudinal and horizontal helicopter guidance system structure is designed. In this paper, a guidance law is designed in order to meet the accuracy and precision requirements in the autonomous landing and transition process. Apart from that, a plane-motion-guidance law is also designed to realize static and dynamic point tracking, linear route tracking and circular route tracking to improve the trajectory smoothness and accuracy. Finally, simulations of the autonomous landing process on moving platforms, including three stages, namely approaching, tracking and landing, are completed. The application effects and precision of the autonomous landing guidance algorithm under different wave heights and period conditions are analyzed through the obtained simulation curves.

Research on Aerial Autonomous Docking and Landing Technology of Dual Multi-Rotor UAV

This paper studies the cooperative control of multiple unmanned aerial vehicles (UAVs) with sensors and autonomous flight capabilities. In this paper, an architecture is proposed that takes a small quadrotor as a mission UAV and a large six-rotor as a platform UAV to provide an aerial take-off and landing platform and transport carrier for the mission UAV. The design of a tracking controller for an autonomous docking and landing trajectory system is the focus of this research. To examine the system's overall design, a dual-machine trajectory-tracking control simulation platform is created via MATLAB/Simulink. Then, an autonomous docking and landing trajectory-tracking controller based on radial basis function proportional-integral-derivative control is designed, which fulfills the trajectory-tracking control requirements of the autonomous docking and landing process by efficiently suppressing the external airflow disturbance according to the simulation results. A YOLOv3-based vision pilot system is designed to calibrate the rate of the aerial docking and landing position to eight frames per second. The feasibility of the multi-rotor aerial autonomous docking and landing technology is verified using prototype flight tests during the day and at night. It lays a technical foundation for UAV transportation, autonomous take-off, landing in the air, and collaborative networking. In addition, compared with the existing technologies, our research completes the closed loop of the technical process through modeling, algorithm design and testing, virtual simulation verification, prototype manufacturing, and flight test, which have better realizability.

Technology of Autonomous Take-Off and Landing for the Modern Flight and Navigation Complex of an Unmanned Aerial Vehicle

Technology of Autonomous Take-Off and Landing for the Modern Flight and Navigation Complex of an Unmanned Aerial Vehicle

Autonomous ship deck landing of a quadrotor UAV using feed-forward image-based visual servoing

Autonomous takeoff and landing are crucial for unmanned aerial vehicles (UAVs) to perform various missions automatically. However, navigation sensors with low accuracy such as global positioning system (GPS) are limited in autonomous takeoff and landing for marine applications when ships are moving fast and oscillating by sea waves. In this study, an image-based visual servoing (IBVS) technique, which was originally used to land a UAV on a static target, is extended for a moving target. In particular, the ship velocity is estimated and used as a feed-forward term in the IBVS controller. To accurately estimate the velocity of the ship, velocity data from the GPS on the ship, image information obtained from the camera in the UAV, and dynamic model of the ship are combined using the Kalman filter framework. Besides, considering the under-actuated nature of the UAV and oscillation of the ship, a virtual plane concept, adaptive IBVS gain and feature shape compensation are introduced. Lastly, to apply the IBVS controller to the moving ship deck landing, a robust and safe autonomous landing procedure, starting from the approach to the touchdown phase, is also developed. The proposed autonomous landing system is validated via simulations and various real-world flight experiments simulating situations in which the ship is moving fast and oscillated by sea waves. In the flight experiments, the UAV lands successfully on the landing pad with the average touchdown error of 0.2m while the ship is oscillating at the Sea State 4 and is moving faster than 5m/s.

Quadrotor Autonomous Landing on Moving Platform

This paper introduces a quadrotor's autonomous take-off and landing system on a moving platform. The designed system addresses three challenging problems: fast pose estimation, restricted external localization, and effective obstacle avoidance. Specifically, first, we design a landing recognition and positioning system based on the AruCo marker to help the quadrotor quickly calculate the relative pose; second, we leverage a gradient-based local motion planner to generate collision-free reference trajectories rapidly for the quadrotor; third, we build an autonomous state machine that enables the quadrotor to complete its take-off, tracking and landing tasks in full autonomy; finally, we conduct experiments in simulated, real-world indoor and outdoor environments to verify the system's effectiveness and demonstrate its potential.

Development of a Bird-like Flapping-wing Aerial Vehicle with Autonomous Take-off and Landing Capabilities

Development of a Bird-like Flapping-wing Aerial Vehicle with Autonomous Take-off and Landing Capabilities

Autonomous Quadrotor Landing on Inclined Surfaces Using Perception-Guided Active Asymmetric Skids

We present an autonomous quadrotor capable of safely landing on sloped surfaces up to 40 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> , intended for emergency scenarios where the only terrain available for landing may be sloped. This system uses a downward-facing depth perception system to determine the direction, angle, and smoothness of the slope. Two robotic landing skids of different lengths actively conform to the slope in order to maintain level body attitude upon landing. We developed an analytical model to design leg lengths, conform to the slope surface angle, and ensure both zero tilt angle of the quadrotor body and clearance of the propellers from the surface. The selection of skid angles is framed as an optimization to match the slope angle while maximizing the buffer between the propellers and the surface. An eigenvalue decomposition of the point cloud covariance matrix provides a surface normal vector, which is used to determine the proper skid angle and yaw angle of the quadrotor. The ratio of the eigenvalues is used to determine whether the surface is sufficiently smooth for safe landing. The proposed system and method were validated in a motion capture environment by conducting five autonomous takeoff and land missions over different sloped surfaces ranging from 0 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> to 40 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> . The detected slope angle and direction of all trials were within 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> and 3.3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> , respectively, of ground truth. No failures or crashes occurred during testing, which demonstrates the viability and robustness of this system for use in real-world scenarios.

Local positioning system for autonomous vertical take-off and landing using ultra-wide band measurement ranging system

An autonomous vertical take-off and landing (VTOL) must be supported with an accurate positioning system, especially for autonomous take-off, landing, and other tasks in small area. This paper presents a novel method of small local outdoor positioning system for localizing the area of dropping and landing of autonomous VTOL by utilizing the low-cost precision ultra-wide band (UWB) ranging system. We compared symmetrical single sided-two way ranging (SSS-TWR), symmetrical double sided-two way ranging (SDS-TWR), and asymmetrical double sided-two way ranging (ADS-TWR) methods to get precision ranging measurement on UWB radio module. ADS-TWR was superior to others by resulting in minimum distance error. The ADS-TWR average error was 1.38 % (35.88 cm), SDS-TWR average error was 1.83 % (47.58 cm), and SSS-TWR average error was 2.73 % (70.98 cm). Furthermore, the trilateration method was utilized to obtain the local position of the autonomous VTOL. The trilateration method successfully implemented autonomous VTOL quadcopter positioning in a small local outdoor area (20 m x 30 m). Autonomous VTOL has been able to drop seven payloads in seven areas (2 m x 2 m) and landed in the home position (3 m x 3 m) successfully.

Control Design for the Autonomous Horizontal Takeoff Phase of the Reusable Launch Vehicles

The control system design for the reusable launch vehicles (RLVs), especially in the autonomous horizontal takeoff phase, is a highly challenging task. Significant issues arise due to the high nonlinearity, large uncertainties of aerodynamic coefficients as well as strong coupling among axes of the airframe. This paper studies autonomous takeoff control problem of the RLVs by the means of trajectory linearization control (TLC) and model predictive control (MPC) theory. The six degree of freedom dynamic model is firstly established, and the flight strategy of takeoff and climb stage is provided through the characteristic analysis of RLVs. Furthermore, the guidance law for the climbing phase is proposed via the TLC method against the high nonlinearity, and a speed based gain-schedule strategy is given under the consideration of both aerodynamic force and friction force. In order to eliminate the ground effect interference, an improved model predictive control approach is presented by introducing the online parameter estimation of the ground effect interaction coefficient, and a coupled model predictive controller is designed by introducing the feedback of sideslip angle into the roll control channel to eliminate the coupling effect. Finally, the performance of the design method for autonomous takeoff control of RLVs is demonstrated through the comparison simulation analysis.

Active Vertical Takeoff of an Aquatic UAV

To extend the mission duration of smaller unmanned aerial vehicles, this letter presents a solar recharge approach that uses lakes as landing, charging, and standby areas. The Sherbrooke University Water-Air VEhicle (SUWAVE) is a small aircraft capable of vertical takeoff and landing on water. A second-generation prototype has been developed with new capabilities: solar recharging, autonomous flight, and a larger takeoff envelope using an actuated takeoff strategy. A 3D dynamic model of the new takeoff maneuver is conceived to understand the major forces present during this critical phase. Numerical simulations are validated with experimental results from real takeoffs made in the laboratory and on lakes. The final prototype is shown to have accomplished repeated cycles of autonomous takeoff, followed by assisted flight and landing, without any human physical intervention between cycles.

Flight Validation of a System for Autonomous Rotorcraft Multilift

A system for rotorcraft dual lift was developed and flight-tested using two Yamaha RMAX helicopters equipped with customized avionics and sling–load hardware. Important features of the system were tested, including precision dual-lift maneuvering, load tension equalization, and load motion damping. Flight-test results are presented that include autonomous takeoff and landing and an oval racetrack pattern flying at 5 m/s carrying 171% of the rated maximum load for one aircraft. Results of formation changes while flying oval patterns are also presented as well as results on active control to equalize the sling tensions between the two aircraft. In addition, sling–load feedback results to increase the sling damping in hover are presented. Finally, the feasibility of migrating the concept to full-scale is shown in simulation using two UH-60 models in a dual-lift configuration carrying 6000 lb at 80 kt.

Robust Visual-Aided Autonomous Takeoff, Tracking, and Landing of a Small UAV on a Moving Landing Platform for Life-Long Operation

Robot cooperation is key in Search and Rescue (SaR) tasks. Frequently, these tasks take place in complex scenarios affected by different types of disasters, so an aerial viewpoint is useful for autonomous navigation or human tele-operation. In such cases, an Unmanned Aerial Vehicle (UAV) in cooperation with an Unmanned Ground Vehicle (UGV) can provide valuable insight into the area. To carry out its work successfully, such as multi-robot system requires the autonomous takeoff, tracking, and landing of the UAV on the moving UGV. Furthermore, it needs to be robust and capable of life-long operation. In this paper, we present an autonomous system that enables a UAV to take off autonomously from a moving landing platform, locate it using visual cues, follow it, and robustly land on it. The system relies on a finite state machine, which together with a novel re-localization module allows the system to operate robustly for extended periods of time and to recover from potential failed landing maneuvers. Two approaches for tracking and landing are developed, implemented, and tested. The first variant is based on a novel height-adaptive PID controller that uses the current position of the landing platform as the target. The second one combines this height-adaptive PID controller with a Kalman filter in order to predict the future positions of the platform and provide them as input to the PID controller. This facilitates tracking and, mainly, landing. Both the system as a whole and the re-localization module in particular have been tested extensively in a simulated environment (Gazebo). We also present a qualitative evaluation of the system on the real robotic platforms, demonstrating that our system can also be deployed on real robotic platforms. For the benefit of the community, we make our software open source.