Enhancing UAV elevator actuator model using multibody dynamics simulation

This paper proposes an improved actuator system model for UAV elevators using multibody dynamics simulation. The multibody dynamics simulation employs the Simscape Multibody, module in MATLAB coupled with Simulink to model the servo and hinge moment calculation. The actuator system comprises an electrical servo and mechanical components, including arms, push rods, horns, and the elevator. The electrical servo is modeled using a PID controller and a simplified motor model. The multibody dynamics simulation is employed to capture the dynamics of the mechanical components, coupled with the electrical servo through torque delivery to the mechanical components. The simulation is applied to the elevator of a medium altitude long endurance (MALE) UAV with a Maximum Take Off Weight of 1300 Kg. Generating these quantities provide a benefit in capturing the operational envelope of the servo to be compared to its limitations. Given the features of this simulation, it is proposed to extend the research by integrating this method with flight dynamics simulation

Keeping pace with technology: drones, disturbance and policy deficiency

This paper analyses regulatory responses to rapid intensification of the use of drones/remotely piloted aircraft (RPA) in the context of wildlife protection. Benefits and disadvantages of the technology to wildlife are examined, before three key limitations in policy and law are identified: failure to address wildlife disturbance in RPA regulation; reliance upon insufficiently comprehensive existing wildlife protection legislation to manage disturbance effects; and limited species-specific research on disturbance. A New Zealand case study further reveals an inconsistent regulatory approach struggling to keep pace with innovation, inadequate regulatory capture of environmental effects due to exemption as “aircraft”, and no recognition that specific geographical locations, such as coastal areas, distinguished by recreational pressures and high numbers of threatened species require special consideration. Recommendations include acknowledging the impact on wildlife in policy, gap analysis of legal arrangements for protection from disturbance (including airspace), and adoption of minimum approach distances to threatened species

UAV Modeling and Simulation at Normal and Abnormal Conditions

The main objective of this thesis is to develop new capabilities within the West Virginia University (WVU) unmanned aerial systems (UAS) simulation environment for the design and analysis of fault tolerant control laws on small sized unmanned aerial vehicles (UAVs). An aerodynamic model for an electric powered UAV is developed using a vortex lattice method implemented within the computational design package Tornado. One-dimensional look-up tables are developed for the main stability and control derivatives, which are then used to calculate linear aerodynamic forces and moments for the nonlinear aircraft equations of motion. Flight data are used for model verification and tuning. The characteristics under normal and abnormal operation of various types of sensors typically used for UAV control are classified under nine functional categories. A general and comprehensive framework for sensor modeling is defined as a sequential alteration of the exact value of the measurand corresponding to each functional category. Simple mathematical and logical algorithms are formulated and used in this process. Each functional category is characterized by several parameters, which may be maintained constant or may vary during simulation. The user has maximum flexibility in selecting values for the parameters within and outside sensor design ranges. These values can be set to change at pre-defined moments, such that permanent and intermittent scenarios can be simulated. The aircraft and sensor models are then integrated with the WVU UAS simulation environment, which is created using MATLAB/Simulink for the computational part and FlightGear for the visualization of the aircraft and scenery. A simple user-friendly graphical interface is designed to allow for detailed simulation scenario setup.;The functionality of the developed models is illustrated through a limited analysis of the effects of sensor abnormal operation on the trajectory tracking performance of autonomous UAV. A composite metric is used for aircraft performance assessment based on both trajectory tracking errors and control activity. The targeted sensors are the gyroscopes providing angular rate measurements and the global positioning system providing position and velocity information. These sensors are instrumental in the inner and outer control loops, respectively, which characterize the typical control architecture for autonomous trajectory tracking.;Due to its generality and flexibility, the proposed sensor model provides detailed insight into the dynamic implications of sensor functionality on the performance of control algorithms. It facilitates the investigation of the synergistic interactions between sensors and control systems and may lead to improvements in both areas

Determining estuarine seagrass density measures from low altitude multispectral imagery flown by remotely piloted aircraft

Seagrass is the subject of significant conservation research. Seagrass is ecologically important and of significant value to human interests. Many seagrass species are thought to be in decline. Degradation of seagrass populations are linked to anthropogenic environmental issues. Effective management requires robust monitoring that is affordable at large scale. Remote sensing methods using satellite and aircraft imagery enable mapping of seagrass populations at landscape scale. Aerial monitoring of a seagrass population can require imagery of high spatial and/or spectral resolution for successful feature extraction across all levels of seagrass density. Remotely piloted aircraft (RPA) can operate close to the ground under precise flight control enabling repeated surveys in high detail with accurate revisit-positioning. This study evaluates a method for assessing intertidal estuarine seagrass (Zostera muelleri) presence/absence and coverage density using multispectral imagery collected by a remotely piloted aircraft (RPA) flying at 30 m above the estuary surface (2.7 cm ground sampling distance). The research was conducted at Wharekawa Harbour on the eastern coast of the Coromandel Peninsula, North Island, New Zealand. Differential drainage of residual ebb waters from the surface of an estuary at low tide creates a mosaic of drying sediment, draining surface and static shallow pooling that has potential to interfere with spectral observations. The field surveys demonstrated that despite minor shifts in the spectral coordinates of seagrass and other surface material, there was no apparent difference in image classification outcome from the time of bulk tidal water clearance to the time of returning tidal flood. For the survey specification tested, classification accuracy increased with decreasing segmentation scale. Pixel-based image analysis (PBIA) achieved higher classification accuracy than object-based image analysis (OBIA) assessed at a range of segmentation scales. Contaminating objects such as shells and detritus can become aggregated within polygon objects when OBIA is applied but remain as isolated objects under PBIA at this image resolution. There was clear separability of spectra for seagrass and sediment, but shell and detritus confounded the classification of seagrass density in some situations. High density seagrass was distinct from sediment, but classification error arose for sparse seagrass. Three classifiers (linear discriminant analysis, support vector machine and random forest) and three feature selection options (no selection, collinearity reduction and recursive feature elimination) were assessed for effect on classification performance. The random forest classifier yielded the highest classification accuracy, with no accuracy benefit gained from collinearity reduction or recursive feature elimination. Spectral vegetation indices and texture layers substantially improved classification accuracy. Object geometry made a negligible contribution to classification accuracy using mean-shift segmentation at this image-scale. The method achieved classification of seagrass density with up to 84% accuracy on a three-tier end-member class scale (low, medium, and high density) when using training data formed using visual interpretation of ground reference photography, and up to 93% accuracy using precisely measured seagrass leaf-area. Visual interpretation agreed with precisely measured seagrass leaf area 88% of the time with some misattribution at mid-density. Visual interpretation was substantially faster to apply than measuring the leaf area. A decile class scale for seagrass density correlated with actual leaf area measures more than the three-tier scale, however, was less accurate for absolute class attribution. The research demonstrates that seagrass feature extraction from RPA-flown imagery is a feasible and repeatable option for seagrass population monitoring and environmental reporting. Further calibration is required for whole- and multi-estuary application