Path planning algorithms for atmospheric science applications of autonomous aircraft systems

Among current techniques, used to assist the modelling of atmospheric processes, is an approach involving the balloon or aircraft launching of radiosondes, which travel along uncontrolled trajectories dependent on wind speed. Radiosondes are launched daily from numerous worldwide locations and the data collected is integral to numerical weather prediction.This thesis proposes an unmanned air system for atmospheric research, consisting of multiple, balloon-launched, autonomous gliders. The trajectories of the gliders are optimised for the uniform sampling of a volume of airspace and the efficient mapping of a particular physical or chemical measure. To accomplish this we have developed a series of algorithms for path planning, driven by the dual objectives of uncertainty andinformation gain.Algorithms for centralised, discrete path planning, a centralised, continuous planner and finally a decentralised, real-time, asynchronous planner are presented. The continuous heuristics search a look-up table of plausible manoeuvres generated by way of an offline flight dynamics model, ensuring that the optimised trajectories are flyable. Further to this, a greedy heuristic for path growth is introduced alongside a control for search coarseness, establishing a sliding control for the level of allowed global exploration, local exploitation and computational complexity. The algorithm is also integrated with a flight dynamics model, and communications and flight systems hardware, enabling software and hardware-in-the-loop simulations. The algorithm outperforms random search in two and three dimensions. We also assess the applicability of the unmanned air system in ‘real’ environments, accounting for the presence of complicated flow fields and boundaries. A case study based on the island South Georgia is presented and indicates good algorithm performance in strong, variable winds. We also examine the impact of co-operation within this multi-agent system of decentralised, unmanned gliders, investigating the threshold for communication range, which allows for optimal search whilst reducing both the cost of individual communication devices and the computational resources associated with the processing of data received by each aircraft. Reductions in communication radius are found to have a significant, negative impact upon the resulting efficiency of the system. To somewhat recover these losses, we utilise a sorting algorithm, determining information priority between any two aircraft in range. Furthermore, negotiation between aircraft is introduced, allowing aircraft to resolve any possible conflicts between selected paths, which helps to counteractany latency in the search heuristic

Development of a hybrid genetic programming technique for computationally expensive optimisation problems

The increasing computational power of modern computers has contributed to the advance of nature-inspired algorithms in the fields of optimisation and metamodelling. Genetic programming (GP) is a genetically-inspired technique that can be used for metamodelling purposes. GP main strength is in the ability to infer the mathematical structure of the best model fitting a given data set, relying exclusively on input data and on a set of mathematical functions given by the user. Model inference is based on an iterative or evolutionary process, which returns the model as a symbolic expression (text expression). As a result, model evaluation is inexpensive and the generated expressions can be easily deployed to other users. Despite genetic programming has been used in many different branches of engineering, its diffusion on industrial scale is still limited. The aims of this thesis are to investigate the intrinsic limitations of genetic programming, to provide a comprehensive review of how researchers have tackled genetic programming main weaknesses and to improve genetic programming ability to extract accurate models from data. In particular, research has followed three main directions. The first has been the development of regularisation techniques to improve the generalisation ability of a model of a given mathematical structure, based on the use of a specific tuning algorithm in case sinusoidal functions are among the functions the model is composed of. The second has been the analysis of the influence that prior knowledge regarding the function to approximate may have on genetic programming inference process. The study has led to the introduction of a strategy that allows to use prior knowledge to improve model accuracy. Thirdly, the mathematical structure of the models returned by genetic programming has been systematically analysed and has led to the conclusion that the linear combination is the structure that is mostly returned by genetic programming runs. A strategy has been formulated to reduce the evolutionary advantage of linear combinations and to protect more complex classes of individuals throughout the evolution. The possibility to use genetic programming in industrial optimisation problems has also been assessed with the help of a new genetic programming implementation developed during the research activity. Such implementation is an open source project and is freely downloadable from http://www.personal.leeds.ac.uk/~cnua/mypage.html

Probabilistically Interpolated Rational Hypercube Landscape Evolutionary Algorithm

Evolutionary Algorithms are powerful function optimizers, but suffer from premature convergence. Quantum-Inspired Evolutionary Algorithm (QEA) has been shown to be less prone to this on an important class of binary encoded problems. QEA uses Q-bits in place of ordinary bits, introducing a rational parameter into an otherwise binary search space. The essential feature of QEA is that the fitness of individuals in the population is defined stochastically by sampling from discrete points in the landscape. The probability of a particular point being sampled is based on the proximity of an individual to that point, where the individual represents a point in the solid hypercube spanned by the possible discrete solutions. This paper presents Probabilistically Interpolated Rational Hypercube Landscape Evolutionary Algorithm (PIRHLEA), which generalizes QEA by relaxing its two vestigial quantum mechanical attributes: quadratic and angular parameterization of probabilities and using single samples to determine fitness estimates of individuals. This is accomplished by replacing each Q-bit with a rational parameter between zero and one. Compared to QEA, PIRHLEA is simpler to code, more computationally efficient, and easier to visualize. PIRHLEA also permits multiple samples from points in the landscape to determine individuals\u27 fitness

Agent-Based Modeling of Resilience in Smallholder Agriculture: Toward Robust Models and Equitable Outcomes

Smallholder farmers constitute one of the world's most vulnerable populations. Moreover, rising socioeconomic inequalities and biophysical degradation threaten to increase this vulnerability. There is therefore a pressing need to build resilience in smallholder agriculture. Socio-environmental systems (SES) modeling can support this goal, yet confronts two challenges that may limit its usefulness for informing agricultural development. First, as agricultural systems are highly heterogeneous and our ability to model them is imperfect, there is a risk that model-based recommendations inadvertently increase vulnerability. Second, there exist a range of approaches to agricultural development that prioritize distinct objectives (e.g., market integration versus social equity), and conflicts between these approaches could undermine progress toward more resilient futures. To build smallholder resilience therefore requires an integrated perspective on development as well as robust methodologies for comparing and integrating alternative development strategies. This dissertation uses agent-based modeling (ABM) to help address these challenges. The first contribution of this dissertation is a set of methodological advances that improve the robustness of model-based policy analysis. These advances question two analytical norms within SES modeling. The first is a lack of attention to equity. For instance, by disregarding heterogeneity in outcomes, model-based recommendations may benefit the well-off at the expense of the vulnerable and thereby perpetuate inequity. Chapters two and three address this issue, first by establishing a conceptual framework for the equity-ABM interface and then by applying an agent-based model to examine equity in the effects of resilience-enhancing strategies. The second analytical norm that this dissertation questions is the use of a single, “best-fit” model to assess policy effects; due to our incomplete understanding of complex SES, multiple plausible models may exist. This common condition is known as equifinality, but it is not often considered in SES modeling or policy analysis. To attend to this challenge, chapter four develops an approach for identifying a set of diverse model calibrations and using these to achieve a more robust policy analysis. Together, these methodological advances facilitate more robust and equitable policy assessments, in agricultural systems and beyond. The second principal contribution of this dissertation is substantive. Emerging from the modeling of smallholder resilience, I find complementarity between disparate agricultural development approaches. For instance, chapter five compares the effects of legume cover cropping (a form of ecological farm management) and microinsurance (a financial institutional support) on smallholder climate resilience. Although these approaches are traditionally promoted by distinct academic communities and development organizations, the results show that, when implemented together, they are highly complementary. Next, chapter six investigates the potential for contract farming to overcome the negative effects of large-scale land acquisitions on smallholder food security. Results suggest that preserving smallholder autonomy through contract farming can simultaneously improve smallholder food security and agricultural production, thereby better aligning the preferences of developers and smallholders. Thus, these chapters together suggest the benefits of reconciling perspectives on and approaches to agricultural development. As a whole, this dissertation advances the application of agent-based modeling and resilience thinking in smallholder agriculture. Beyond agricultural applications, it lays the groundwork for identifying robust and equitable development strategies in SES.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169916/1/tgw_1.pd

An introduction to the mechanics of performance assessment using examples of calculations done for the Waste Isolation Pilot Plant between 1990 and 1992. Revision

This document provides an overview of the processes used to access the performance of the Waste Isolation Pilot Plant (WIPP). The quantitative metrics used in the performance-assessment (PA) process are those put forward in the Environmental Protection Agency`s Environmental Standards for the Management and Disposal of Spent Nuclear Fuel, HIgh-LEvel and transuranic radioactive Wastes (40 CFR 191)

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Chemical programming to eploit chemical Reaction systems for computation

This thesis is on programming approaches to exploit the computational capabilities of chemical systems, consisting of two parts. In the first part, constructive design, research activities on theoretical development of chemical programming are reported. As results of the investigations, general programming principles, named organization-oriented programming, are derived. The idea is to design reaction networks such that the desired computational outputs correspond to the organizational structures within the networks. The second part, autonomous design, discusses on programming strategies without human interactions, namely evolution and exploration. Motivations for this programming approach include possibilities to discover novelty without rationalization. Regarding first the evolutionary strategies, we rather focused on how to track the evolutionary processes. Our approach is to analyze these dynamical processes on a higher level of abstraction, and usefulness of distinguishing organizational evolution in space of organizations from actual evolution in state space is emphasized. As second strategy of autonomous chemical programming, we suggest an explorative approach, in which an automated system is utilized to explore the behavior of the chemical reaction system as a preliminary step. A specific aspect of the system's behavior becomes ready for a programmer to be chosen for a particular computational purpose. In this thesis, developments of autonomous exploration techniques are reported. Finally, we discuss combining those two approaches, constructive design and autonomous design, titled as a hybrid approach. From our perspective, hybrid approaches are ideal, and cooperation of constructive design and autonomous design is fruitful