Towards Reduced-Order Models for Online Motion Planning and Control of UAVs in the Presence of Wind

Abstract This paper describes a model reduction strategy for obtaining a computationally efficient prediction of a fixed-wing UAV performing waypoint navigation under steady wind conditions. The strategy relies on the off-line generation of time parametrized trajectory libraries for a set of flight conditions and reduced order basis functions functions for determining intermediate locations. It is assumed that the UAV has independent bounded control over the airspeed and altitude, and consider a 2D slice of the operating environment. We found that the reduced-order model finds intermediate positions within 10% and at speeds of 10x faster than clock-time (even in wind conditions in excess of 50% of the UAV's forward airspeed) when compared against simulation results using a medium-fidelity flight dynamics model. The potential of this strategy for online planning operations is highlighted

Advanced photogrammetry to assess lichen colonization in the hyper-arid Namib Desert

The hyper-arid central region of the Namib Desert is characterized by quartz desert pavement terrain that is devoid of vascular plant covers. In this extreme habitat the only discernible surface covers are epilithic lichens that colonize exposed surfaces of quartz rocks. These lichens are highly susceptible to disturbance and so field surveys have been limited due to concerns about disturbing this unusual desert feature. Here we present findings that illustrate how non-destructive surveys based upon advanced photogrammetry techniques can yield meaningful and novel scientific data on these lichens. We combined ‘structure from motion analysis,’ computer vision and GIS to create 3-dimensional point clouds from two-dimensional imagery. The data were robust in its application to estimating absolute lichen cover. An orange Stellarangia spp. assemblage had coverage of 22.8% of available substrate, whilst for a black Xanthoparmelia spp. assemblage coverage was markedly lower at 0.6% of available substrate. Hyperspectral signatures for both lichens were distinct in the near-infra red range indicating that Xanthoparmelia spp. was likely under relatively more moisture stress than Stellarangia spp. at the time of sampling, and we postulate that albedo effects may have contributed to this in the black lichen. Further transformation of the data revealed a colonization preference for west-facing quartz surfaces and this coincides with prevailing winds for marine fog that is the major source of moisture in this system. Furthermore, a three-dimensional ‘fly through’ of the lichen habitat was created to illustrate how the application of computer vision in microbiology has further potential as a research and education tool. We discuss how advanced photogrammetry could be applied in astrobiology using autonomous rovers to add quantitative ecological data for visible surface colonization on the surface of Mars.AdLR thanks the support of the grant CTM2015-64728-C2-2-R from the Spanish Ministry of Economy, Industry and Competitiveness.http://www.frontiersin.org/Microbiologyam2017Genetic

Towards reduced-order models for online motion planning and control of UAVs in the presence of wind

This paper describes a model reduction strategy for obtaining a computationally efficient prediction of a fixed-wing UAV performing waypoint navigation under steady wind conditions. The strategy relies on the off-line generation of time parametrized trajectory libraries for a set of flight conditions and reduced order basis functions functions for determining intermediate locations. It is assumed that the UAV has independent bounded control over the airspeed and altitude, and consider a 2D slice of the operating environment. We found that the reduced-order model finds intermediate positions within 10% and at speeds of 10x faster than clock-time (even in wind conditions in excess of 50% of the UAV’s forward airspeed) when compared against simulation results using a medium-fidelity flight dynamics model. The potential of this strategy for online planning operations is highlighted

Development of micro-UAV with integrated motion planning for open-cut mining surveillance

Small unmanned aerial vehicles called micro-UAVs are excellent examples of cyber-physical systems which interact with complex and dynamic environments. The success of this technology depends on smart avionics systems compensating for the physical limitations of small airframes, which have very limited on-board power. This paper presents development of micro-UAV for surveillance of open-cut mining sites that represent significant challenges due to difficult terrain and changing wind conditions. The real time aircraft control is integrated with motion planning based on Rapid-exploring Random Tree (RRT) methods which allow efficient handling of the wind factor. The main computational difficulty with RRT in real time motion planning is overcome by employing reduced forward model (RFM) of the aircraft. We also outline some strategies on integrating motion planning, control, and payload processors in reconfigurable hardware to optimise performance and power consumption. The micro-UAV development process is incremental and in large part based on simulations with hardware in the loop but gathering data from experimental flights is essential for accurate reduced forward models. We developed the avionics and experimental vehicle and used it in surveillance missions over mining sites to validate our approach

A Green Fingerprint of Antarctica: Drones, Hyperspectral Imaging, and Machine Learning for Moss and Lichen Classification

Mapping Antarctic Specially Protected Areas (ASPAs) remains a critical yet challenging task, especially in extreme environments like Antarctica. Traditional methods are often cumbersome, expensive, and risky, with limited satellite data further hindering accuracy. This study addresses these challenges by developing a workflow that enables precise mapping and monitoring of vegetation in ASPAs. The processing pipeline of this workflow integrates small unmanned aerial vehicles (UAVs)—or drones—to collect hyperspectral and multispectral imagery (HSI and MSI), global navigation satellite system (GNSS) enhanced with real-time kinematics (RTK) to collect ground control points (GCPs), and supervised machine learning classifiers. This workflow was validated in the field by acquiring ground and aerial data at ASPA 135, Windmill Islands, East Antarctica. The data preparation phase involves a data fusion technique to integrate HSI and MSI data, achieving the collection of georeferenced HSI scans with a resolution of up to 0.3 cm/pixel. From these high-resolution HSI scans, a series of novel spectral indices were proposed to enhance the classification accuracy of the model. Model training was achieved using extreme gradient boosting (XGBoost), with four different combinations tested to identify the best fit for the data. The research results indicate the successful detection and mapping of moss and lichens, with an average accuracy of 95%. Optimised XGBoost models, particularly Model 3 and Model 4, demonstrate the applicability of the custom spectral indices to achieve high accuracy with reduced computing power requirements. The integration of these technologies results in significantly more accurate mapping compared to conventional methods. This workflow serves as a foundational step towards more extensive remote sensing applications in Antarctic and ASPA vegetation mapping, as well as in monitoring the impact of climate change on the Antarctic ecosystem

Monitoring of Antarctica’s Fragile Vegetation Using Drone-Based Remote Sensing, Multispectral Imagery and AI

Vegetation in East Antarctica, such as moss and lichen, vulnerable to the effects of climate change and ozone depletion, requires robust non-invasive methods to monitor its health condition. Despite the increasing use of unmanned aerial vehicles (UAVs) to acquire high-resolution data for vegetation analysis in Antarctic regions through artificial intelligence (AI) techniques, the use of multispectral imagery and deep learning (DL) is quite limited. This study addresses this gap with two pivotal contributions: (1) it underscores the potential of deep learning (DL) in a field with notably limited implementations for these datasets; and (2) it introduces an innovative workflow that compares the performance between two supervised machine learning (ML) classifiers: Extreme Gradient Boosting (XGBoost) and U-Net. The proposed workflow is validated by detecting and mapping moss and lichen using data collected in the highly biodiverse Antarctic Specially Protected Area (ASPA) 135, situated near Casey Station, between January and February 2023. The implemented ML models were trained against five classes: Healthy Moss, Stressed Moss, Moribund Moss, Lichen, and Non-vegetated. In the development of the U-Net model, two methods were applied: Method (1) which utilised the original labelled data as those used for XGBoost; and Method (2) which incorporated XGBoost predictions as additional input to that version of U-Net. Results indicate that XGBoost demonstrated robust performance, exceeding 85% in key metrics such as precision, recall, and F1-score. The workflow suggested enhanced accuracy in the classification outputs for U-Net, as Method 2 demonstrated a substantial increase in precision, recall and F1-score compared to Method 1, with notable improvements such as precision for Healthy Moss (Method 2: 94% vs. Method 1: 74%) and recall for Stressed Moss (Method 2: 86% vs. Method 1: 69%). These findings contribute to advancing non-invasive monitoring techniques for the delicate Antarctic ecosystems, showcasing the potential of UAVs, high-resolution multispectral imagery, and ML models in remote sensing applications