Drones have become an integral part of the digital transformation currently sweeping the mining industry; particularly in surface operations, where they allow operators to model the terrain quickly and effortlessly with GPS localization and advanced mission planning software. Recently, the usage of drones has expanded to underground mines, with advancements in drone autonomy in GPS-denied environments. Developments in lidar technology and Simultaneous Localization and Mapping (SLAM) algorithms are enabling UAVs to function safely underground where they can be used to map workings and digitally reconstruct them into 3D point clouds for a wide variety of applications. Underground mines can be expansive with inaccessible and dangerous areas preventing safe access for traditional inspections, mapping and monitoring. In addition, abandoned mines and historic mines being reopened may lack reliable maps of sufficient detail. The underground mine environment presents a multitude of unique challenges that must be addressed for reliable drone flights. This work covers the development of drones for GPS-denied underground mines, in addition to several case studies where drone-based lidar and photogrammetry were used to capture 3D point clouds of underground mines, and the associated applications of mine digitization, such as geotechnical analysis and pillar strength analysis. This research also features an applied use case of custom drones built to detect methane leaks at natural gas production and distribution sites. / Doctor of Philosophy / Drones have become an integral part of the digital transformation currently sweeping the mining industry; particularly in surface operations, where they allow operators to model the terrain quickly and effortlessly. Recently, the usage of drones has expanded to underground mines, with advancements in drone autonomy. New developments are enabling UAVs to function safely underground where they can be used to digitally reconstruct workings for a wide variety of applications. Underground mines can be expansive with inaccessible and dangerous areas preventing safe access for traditional inspections, mapping and monitoring. In addition, abandoned mines and historic mines being reopened may lack reliable maps of sufficient detail. The underground mine environment presents a multitude of unique challenges that must be addressed for reliable drone flights. This work covers the development of drones for GPS-denied underground mines, in addition to several case studies where drones were used to create 3D models of mines, and the associated applications of mine digitization. This research also features an applied use case of custom drones built to detect methane leaks at natural gas production and distribution sites.

This thesis aimed to assess the feasibility of using an off-the-shelf virtual reality tracking system as a low cost precision pose estimation solution for robotic operations in both indoor and outdoor environments. Such a tracking solution has the potential of assisting critical operations related to planetary exploration missions, parcel handling/delivery, and wildfire detection/early warning systems. The boom of virtual reality experiences has accelerated the development of various low-cost, precision indoor tracking technologies. For the purpose of this thesis we choose to adapt the SteamVR Lighthouse system developed by Valve, which uses photo-diodes on the trackers to detect the rotating IR laser sheets emitted from the anchored base stations, also known as lighthouses. Some previous researches had been completed using the first generation of lighthouses, which has a few limitations on communication from lighthouses to the tracker. A NASA research has cited poor tracking performance under sunlight. We choose to use the second generation lighthouses which has improved the method of communication from lighthouses to the tracker, and we performed various experiments to assess their performance outdoors, including under sunlight. The studies of this thesis have two stages, the first stage focused on a controlled, indoor environment, having an Unmanned Aerial Vehicle (UAS) perform repeatable flight patterns and simultaneously tracked by the Lighthouse and a reference indoor tracking system, which showed that the tracking precision of the lighthouse is comparable to the industrial standard indoor tracking solution. The second stage of the study focused on outdoor experiments with the tracking system, comparing UAS flights between day and night conditions as well as positioning accuracy assessments with a CNC machine under indoor and outdoor conditions. The results showed matching performance between day and night while still comparable to industrial standard indoor tracking solution down to centimeter precision, and matching simulated CNC trajectory down to millimeter precision. There is also some room for improvement in regards to the experimental method and equipment used, as well as improvements on the tracking system itself needed prior to adaptation in real-world applications. / Master of Science / This thesis aimed to assess the feasibility of using an off-the-shelf virtual reality tracking system as a low cost precision pose estimation solution for robotic operations in both indoor and outdoor environments. Such a tracking solution has the potential of assisting critical operations related to planetary exploration missions, parcel handling/delivery, and wildfire detection/early warning systems. The boom of virtual reality experiences has accelerated the development of various low-cost, precision indoor tracking technologies. For the purpose of this thesis we choose to adapt the SteamVR Lighthouse system developed by Valve, which uses photo-diodes on the trackers to detect the rotating IR laser sheets emitted from the anchored base stations, also known as lighthouses. Some previous researches had been completed using the first generation of lighthouses, which has a few limitations on communication from lighthouses to the tracker. A NASA research has cited poor tracking performance under sunlight. We choose to use the second generation lighthouses which has improved the method of communication from lighthouses to the tracker, and we performed various experiments to assess their performance outdoors, including under sunlight. The studies of this thesis have two stages, the first stage focused on a controlled, indoor environment, having an Unmanned Aerial Vehicle (UAS) perform repeatable flight patterns and simultaneously tracked by the Lighthouse and a reference indoor tracking system, which showed that the tracking precision of the lighthouse is comparable to the industrial standard indoor tracking solution. The second stage of the study focused on outdoor experiments with the tracking system, comparing UAS flights between day and night conditions as well as positioning accuracy assessments with a CNC machine under indoor and outdoor conditions. The results showed matching performance between day and night while still comparable to industrial standard indoor tracking solution down to centimeter precision, and matching simulated CNC trajectory down to millimeter precision. There is also some room for improvement in regards to the experimental method and equipment used, as well as improvements on the tracking system itself needed prior to adaptation in real-world applications.

<p>Integrating unoccupied aerial systems (UAS) into agricultural remote sensing has revolutionized several domains, including crop yield estimation. This research arises from the need to combat citrus greening disease, a major threat to citrus production. Accurately estimating crop yields is crucial for evaluating the effectiveness of treatments and controls for this disease. In response, our study examined the efficacy of phenotypic data extracted from multi-temporal RGB and multispectral UAS images in estimating individual citrus tree yields before harvest and then using this as an indicator to analyze the effectiveness of the treatments and control choice.</p> <p>This study presents machine learning-based regression models for estimating individual citrus tree yields, utilizing the diverse features extracted to provide comprehensive insights into the citrus trees under investigation. Four machine learning algorithms, random forest regression, extreme gradient boosting regression, adaptive boosting, and support vector regression, were employed to build the yield estimation models. The experiment was designed in two phases: single-temporal and multi-temporal modeling.</p>

With unmanned aerial vehicle(s) (UAV), swift responses to urgent needs (such as search and rescue missions or medical deliveries) can be realized. Simultaneously, legislators are establishing so-called geographical zones, which restrict UAV operations to mitigate air and ground risks to third parties. These geographical zones serve particular safety interests but they may also hinder the efficient usage of UAVs in time-critical missions with range-limiting battery capacities. In this study, we address a facility location problem for up to two UAV hangars and combine it with a routing problem of a standard UAV mission to consider geographical zones as restricted areas, battery constraints, and the impact of wind to increase the robustness of the solution. To this end, water rescue missions are used exemplary, for which positive and negative location factors for UAV hangars and areas of increased drowning risk as demand points are derived from open-source georeferenced data. Optimum UAV mission trajectories are computed with an A* algorithm, considering five different restriction scenarios. As this pathfinding is very time-consuming, binary occupancy grids and image-processing algorithms accelerate the computation by identifying either entirely inaccessible or restriction-free connections beforehand. For the optimum UAV hangar locations, we maximize accessibility while minimizing the service times to the hotspots, resulting in a decrease from the average service time of 570.4 s for all facility candidates to 351.1 s for one and 287.2 s for two optimum UAV hangar locations.

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Die Sensordatenfusion ist eine allgegenwärtige Aufgabe im Bereich der mobilen Robotik und darüber hinaus. In der vorliegenden Arbeit wird das typischerweise verwendete Verfahren zur Sensordatenfusion in der Robotik in Frage gestellt und anhand von neuartigen Algorithmen, basierend auf einem Faktorgraphen, gelöst sowie mit einer korrespondierenden Extended-Kalman-Filter-Implementierung verglichen. Im Mittelpunkt steht dabei das technische sowie algorithmische Sensorkonzept für die Navigation eines Flugroboters im Innenbereich. Ausführliche Experimente zeigen die Qualitätssteigerung unter Verwendung der neuen Variante der Sensordatenfusion, aber auch Einschränkungen und Beispiele mit nahezu identischen Ergebnissen beider Varianten der Sensordatenfusion. Neben Experimenten anhand einer hardwarenahen Simulation wird die Funktionsweise auch anhand von realen Hardwaredaten evaluiert.

Die Sensordatenfusion ist eine allgegenwärtige Aufgabe im Bereich der mobilen Robotik und darüber hinaus. In der vorliegenden Arbeit wird das typischerweise verwendete Verfahren zur Sensordatenfusion in der Robotik in Frage gestellt und anhand von neuartigen Algorithmen, basierend auf einem Faktorgraphen, gelöst sowie mit einer korrespondierenden Extended-Kalman-Filter-Implementierung verglichen. Im Mittelpunkt steht dabei das technische sowie algorithmische Sensorkonzept für die Navigation eines Flugroboters im Innenbereich. Ausführliche Experimente zeigen die Qualitätssteigerung unter Verwendung der neuen Variante der Sensordatenfusion, aber auch Einschränkungen und Beispiele mit nahezu identischen Ergebnissen beider Varianten der Sensordatenfusion. Neben Experimenten anhand einer hardwarenahen Simulation wird die Funktionsweise auch anhand von realen Hardwaredaten evaluiert.

For unmanned aircraft systems to gain full access to the National Airspace System (NAS), they must have the capability to detect and avoid other aircraft. This research focuses on the development of a detect-and-avoid (DAA) system for small unmanned aircraft systems. To safely avoid another aircraft, an unmanned aircraft must detect the intruder aircraft with ample time and distance. Two analytical methods for finding the minimum detection range needed are described. The first method, time-based geometric velocity vectors (TGVV), includes the bank-angle dynamics of the ownship while the second, geometric velocity vectors (GVV), assumes an instantaneous bank-angle maneuver. The solution using the first method must be found numerically, while the second has a closed-form analytical solution. These methods are compared to two existing methods. Results show the time-based geometric velocity vectors approach is precise, and the geometric velocity vectors approach is a good approximation under many conditions. The DAA problem requires the use of a robust target detection and tracking algorithm for tracking multiple maneuvering aircraft in the presence of noisy, cluttered, and missed measurements. Additionally these algorithms needs to be able to detect overtaking intruders, which has been resolved by using multiple radar sensors around the aircraft. To achieve these goals the formulation of a nonlinear extension to R-RANSAC has been performed, known as extended recursive-RANSAC (ER-RANSAC). The primary modifications needed for this ER-RANSAC implementation include the use of an EKF, nonlinear inlier functions, and the Gauss-Newton method for model hypothesis and generation. A fully functional DAA system includes target detection and tracking, collision detection, and collision avoidance. In this research we demonstrate the integration of each of the DAA-system subcomponents into fully functional simulation and hardware implementations using a ground-based radar setup. This integration resulted in various modifications of the radar DSP, collision detection, and collision avoidance algorithms, to improve the performance of the fully integrated DAA system. Using these subcomponents we present flight results of a complete ground-based radar DAA system, using actual radar hardware.

The increasing demand to integrate unmanned aircraft systems (UAS) into the national airspace is motivated by the rapid growth of the UAS industry, especially small UAS weighing less than 55 pounds. Their use however has been limited by the Federal Aviation Administration regulations due to collision risk they pose, safety and regulatory concerns. Therefore, before civil aviation authorities can approve routine UAS flight operations, UAS must be equipped with sense-and-avoid technology comparable to the see-and-avoid requirements for manned aircraft. The sense-and-avoid problem includes several important aspects including regulatory and system-level requirements, design specifications and performance standards, intruder detecting and tracking, collision risk assessment, and finally path planning and collision avoidance. In this dissertation, our primary focus is on developing an collision detection, risk assessment and avoidance framework that is computationally affordable and suitable to run on-board small UAS. To begin with, we address the minimum sensing range for the sense-and-avoid (SAA) system. We present an approximate close form analytical solution to compute the minimum sensing range to safely avoid an imminent collision. The approach is then demonstrated using a radar sensor prototype that achieves the required minimum sensing range. In the area of collision risk assessment and collision prediction, we present two approaches to estimate the collision risk of an encounter scenario. The first is a deterministic approach similar to those been developed for Traffic Alert and Collision Avoidance (TCAS) in manned aviation. We extend the approach to account for uncertainties of state estimates by deriving an analytic expression to propagate the error variance using Taylor series approximation. To address unanticipated intruders maneuvers, we propose an innovative probabilistic approach to quantify likely intruder trajectories and estimate the probability of collision risk using the uncorrelated encounter model (UEM) developed by MIT Lincoln Laboratory. We evaluate the proposed approach using Monte Carlo simulations and compare the performance with linearly extrapolated collision detection logic. For the path planning and collision avoidance part, we present multiple reactive path planning algorithms. We first propose a collision avoidance algorithm based on a simulated chain that responds to a virtual force field produced by encountering intruders. The key feature of the proposed approach is to model the future motion of both the intruder and the ownship using a chain of waypoints that are equally spaced in time. This timing information is used to continuously re-plan paths that minimize the probability of collision. Second, we present an innovative collision avoidance logic using an ownship centered coordinate system. The technique builds a graph in the local-level frame and uses the Dijkstra's algorithm to find the least cost path. An advantage of this approach is that collision avoidance is inherently a local phenomenon and can be more naturally represented in the local coordinates than the global coordinates. Finally, we propose a two step path planner for ground-based SAA systems. In the first step, an initial suboptimal path is generated using A* search. In the second step, using the A* solution as an initial condition, a chain of unit masses connected by springs and dampers evolves in a simulated force field. The chain is described by a set of ordinary differential equations that is driven by virtual forces to find the steady-state equilibrium. The simulation results show that the proposed approach produces collision-free plans while minimizing the path length. To move towards a deployable system, we apply collision detection and avoidance techniques to a variety of simulation and sensor modalities including camera, radar and ADS-B along with suitable tracking schemes.