Video guidance, landing, and imaging systems

The adaptive potential of video guidance technology for earth orbital and interplanetary missions was explored. The application of video acquisition, pointing, tracking, and navigation technology was considered to three primary missions: planetary landing, earth resources satellite, and spacecraft rendezvous and docking. It was found that an imaging system can be mechanized to provide a spacecraft or satellite with a considerable amount of adaptability with respect to its environment. It also provides a level of autonomy essential to many future missions and enhances their data gathering ability. The feasibility of an autonomous video guidance system capable of observing a planetary surface during terminal descent and selecting the most acceptable landing site was successfully demonstrated in the laboratory. The techniques developed for acquisition, pointing, and tracking show promise for recognizing and tracking coastlines, rivers, and other constituents of interest. Routines were written and checked for rendezvous, docking, and station-keeping functions

Provably-Correct Task Planning for Autonomous Outdoor Robots

Autonomous outdoor robots should be able to accomplish complex tasks safely and reliably while considering constraints that arise from both the environment and the physical platform. Such tasks extend basic navigation capabilities to specify a sequence of events over time. For example, an autonomous aerial vehicle can be given a surveillance task with contingency plans while complying with rules in regulated airspace, or an autonomous ground robot may need to guarantee a given probability of success while searching for the quickest way to complete the mission. A promising approach for the automatic synthesis of trusted controllers for complex tasks is to employ techniques from formal methods. In formal methods, tasks are formally specified symbolically with temporal logic. The robot then synthesises a controller automatically to execute trusted behaviour that guarantees the satisfaction of specified tasks and regulations. However, a difficulty arises from the lack of expressivity, which means the constraints affecting outdoor robots cannot be specified naturally with temporal logic. The goal of this thesis is to extend the capabilities of formal methods to express the constraints that arise from outdoor applications and synthesise provably-correct controllers with trusted behaviours over time. This thesis focuses on two important types of constraints, resource and safety constraints, and presents three novel algorithms that express tasks with these constraints and synthesise controllers that satisfy the specification. Firstly, this thesis proposes an extension to probabilistic computation tree logic (PCTL) called resource threshold PCTL (RT-PCTL) that naturally defines the mission specification with continuous resource threshold constraints; furthermore, it synthesises an optimal control policy with respect to the probability of success. With RT-PCTL, a state with accumulated resource out of the specified bound is considered to be failed or saturated depending on the specification. The requirements on resource bounds are naturally encoded in the symbolic specification, followed by the automatic synthesis of an optimal controller with respect to the probability of success. Secondly, the thesis proposes an online algorithm called greedy Buchi algorithm (GBA) that reduces the synthesis problem size to avoid the scalability problem. A framework is then presented with realistic control dynamics and physical assumptions in the environment such as wind estimation and fuel constraints. The time and space complexity for the framework is polynomial in the size of the system state, which is efficient for online synthesis. Lastly, the thesis proposes a synthesis algorithm for an optimal controller with respect to completion time given the minimum safety constraints. The algorithm naturally balances between completion time and safety. This work proves an analytical relationship between the probability of success and the conditional completion time given the mission specification. The theoretical contributions in this thesis are validated through realistic simulation examples. This thesis identifies and solves two core problems that contribute to the overall vision of developing a theoretical basis for trusted behaviour in outdoor robots. These contributions serve as a foundation for further research in multi-constrained task planning where a number of different constraints are considered simultaneously within a single framework

LiDAR in extreme environment: surveying in Antarctica

This study was performed under the patronage of the Italian National Research Programme in Antarctica (PNRA) with the aim to realize a high resolution Digital Elevation Model (DEM) of the moraine named "Boulder Clay" which insists approximately 7 km far from the Italian Research Base "Mario Zucchelli Station" in the Terra Nova Bay area. The DEM will be included in the project for the construction of two runways to be used as support facilities for the scientific research campaigns which take place on regular basis each year. Although the research efforts to realize a detailed cartography of the area is on-going, for the specific aim and urgency of this project it was decided to perform a laser scanning survey in this extreme environment in order to obtain contour lines describing the terrain elevation each 50 cm and volume analysis. The final result will be super imposed on a photogrammetric DEM with contour lines each 2.5 m and satellite images. This paper focus both on the final scientific data and on all the challenges have to be faced in such extreme and particular environment during the laser scanning survey

Adaptive Airborne Separation to Enable UAM Autonomy in Mixed Airspace

The excitement and promise generated by Urban Air Mobility (UAM) concepts have inspired both new entrants and large aerospace companies throughout the world to invest hundreds of millions in research and development of air vehicles, both piloted and unpiloted, to fulfill these dreams. The management and separation of all these new aircraft have received much less attention, however, and even though NASAs lead is advancing some promising concepts for Unmanned Aircraft Systems (UAS) Traffic Management (UTM), most operations today are limited to line of sight with the vehicle, airspace reservation and geofencing of individual flights. Various schemes have been proposed to control this new traffic, some modeled after conventional air traffic control and some proposing fully automatic management, either from a ground-based entity or carried out on board among the vehicles themselves. Previous work has examined vehicle-based traffic management in the very low altitude airspace within a metroplex called UTM airspace in which piloted traffic is rare. A management scheme was proposed in that work that takes advantage of the homogeneous nature of the traffic operating in UTM airspace. This paper expands that concept to include a traffic management plan usable at all altitudes desired for electric Vertical Takeoff and Landing urban and short-distance, inter-city transportation. The interactions with piloted aircraft operating under both visual and instrument flight rules are analyzed, and the role of Air Traffic Control services in the postulated mixed traffic environment is covered. Separation values that adapt to each type of traffic encounter are proposed, and the relationship between required airborne surveillance range and closure speed is given. Finally, realistic scenarios are presented illustrating how this concept can reliably handle the density and traffic mix that fully implemented and successful UAM operations would entail

Natural Infrastructure Alternatives Mitigate Hurricane-Driven Flood Vulnerability: Application to Tyndall Air Force Base

Hurricane frequency and magnitude intensification are expected over the remainder of the twenty-first century. Uncertainty in future projections requires that coastal communities approach adaptation decisions with caution. Traditional approaches are costly and inflexible. Soft policy adaptations are largely unenforceable. Hard, natural adaptations have emerged as an opportunity to partially mitigate the growing risk of extreme flooding, without the large investments required for traditional approaches, where natural infrastructure already exists. Existing literature for natural adaptations has not leveraged intensification expectations for hurricane events. This research uses multihazard damage evaluation software and spatial analysis to investigate placement of dredged sediment as a barrier island maintenance technique to determine economic viability, as compared to no-action alternatives. The efficacy of this strategy is tested against 18 threat and time calibrated scenarios, applied to existing barrier islands at Tyndall AFB. The results illustrate that protection of 7 square miles of existing barrier islands could help avert facility and infrastructure damage from high-intensity hurricane surge events predicted at 2100 by up to 3 orders of magnitude compared to a status quo scenario. The broader implications suggest that planners should look to preserve natural infrastructure that provides surge protection based on the ability to mitigate damages from intensified climate factors

Interactive Planning and Sensing for Aircraft in Uncertain Environments with Spatiotemporally Evolving Threats

Autonomous aerial, terrestrial, and marine vehicles provide a platform for several applications including cargo transport, information gathering, surveillance, reconnaissance, and search-and-rescue. To enable such applications, two main technical problems are commonly addressed.On the one hand, the motion-planning problem addresses optimal motion to a destination: an application example is the delivery of a package in the shortest time with least fuel. Solutions to this problem often assume that all relevant information about the environment is available, possibly with some uncertainty. On the other hand, the information gathering problem addresses the maximization of some metric of information about the environment: application examples include such as surveillance and environmental monitoring. Solutions to the motion-planning problem in vehicular autonomy assume that information about the environment is available from three sources: (1) the vehicle’s own onboard sensors, (2) stationary sensor installations (e.g. ground radar stations), and (3) other information gathering vehicles, i.e., mobile sensors, especially with the recent emphasis on collaborative teams of autonomous vehicles with heterogeneous capabilities. Each source typically processes the raw sensor data via estimation algorithms. These estimates are then available to a decision making system such as a motion- planning algorithm. The motion-planner may use some or all of the estimates provided. There is an underlying assumption of “separation� between the motion-planning algorithm and the information about environment. This separation is common in linear feedback control systems, where estimation algorithms are designed independent of control laws, and control laws are designed with the assumption that the estimated state is the true state. In the case of motion-planning, there is no reason to believe that such a separation between the motion-planning algorithm and the sources of estimated environment information will lead to optimal motion plans, even if the motion planner and the estimators are themselves optimal. The goal of this dissertation is to investigate whether the removal of this separation, via interactive motion-planning and sensing, can significantly improve the optimality of motion- planning. The major contribution of this work is interactive planning and sensing. We consider the problem of planning the path of a vehicle, which we refer to as the actor, to traverse a threat field with minimum threat exposure. The threat field is an unknown, time- variant, and strictly positive scalar field defined on a compact 2D spatial domain – the actor’s workspace. The threat field is estimated by a network of mobile sensors that can measure the threat field pointwise. All measurements are noisy. The objective is to determine a path for the actor to reach a desired goal with minimum risk, which is a measure sensitive not only to the threat exposure itself, but also to the uncertainty therein. A novelty of this problem setup is that the actor can communicate with the sensor network and request that the sensors position themselves in a procedure we call sensor reconfiguration such that the actor’s risk is minimized. This work continues with a foundation in motion planning in time-varying fields where waiting is a control input. Waiting is examined in the context of finding an optimal path with considerations for the cost of exposure to a threat field, the cost of movement, and the cost of waiting. For example, an application where waiting may be beneficial in motion-planning is the delivery of a package where adverse weather may pose a risk to the safety of a UAV and its cargo. In such scenarios, an optimal plan may include “waiting until the storm passes.� Results on computational efficiency and optimality of considering waiting in path- planning algorithms are presented. In addition, the relationship of waiting in a time- varying field represented with varying levels of resolution, or multiresolution is studied. Interactive planning and sensing is further developed for the case of time-varying environments. This proposed extension allows for the evaluation of different mission windows, finite sensor network reconfiguration durations, finite planning durations, and varying number of available sensors. Finally, the proposed method considers the effect of waiting in the path planner under the interactive planning and sensing for time-varying fields framework. Future work considers various extensions of the proposed interactive planning and sensing framework including: generalizing the environment using Gaussian processes, sensor reconfiguration costs, multiresolution implementations, nonlinear parameters, decentralized sensor networks and an application to aerial payload delivery by parafoil

Long-term shoreline changes at large spatial scales at the Baltic Sea: remote-sensing based assessment and potential drivers

In this study, we demonstrate how freely available satellite images can be used to understand large-scale coastline developments along the coast of Mecklenburg-Western Pomerania (MWP). By validating the resulting dataset with an independent Light Detection and Ranging (LIDAR) dataset, we achieved a high level of accuracy for the calculation of rates of change (ROC) with a root mean square error (RMSE) of up to 0.91 m, highlighting the reliability of Earth observation data for this purpose. The study assessed the coastal system’s natural evolution from 1984 to 1990, prior to significant human interventions, and examined the period from 1996 to 2022, which was characterized by regular sand nourishments amounting to approximately 16 million m³. The results reveal notable changes in the study area, with a significant decline in erosive trends and an increase in the number of stable and accreting transects. However, it is important to note that the regular sand nourishments may be masking the true ROC along the coastline. Furthermore, the future supply of sand raises concerns about the sustainability of coastal developments, particularly in the context of future sea level rise (SLR). The study provides valuable insights for coastal authorities and policymakers, informing decisions on sand resource allocation and highlighting the need for appropriate adaptation strategies to address future SLR and ensure long-term coastal resilience