Finding Water on Poleless Using Melomaniac Myopic Chameleon Robots

In 2042, the exoplanet exploration program, launched in 2014 by NASA, finally discovers a new exoplanet so-called Poleless, due to the fact that it is not subject to any magnetism. A new generation of autonomous mobile robots, called M2C (for Melomaniac Myopic Chameleon), have been designed to find water on Poleless. To address this problem, we investigate optimal (w.r.t., visibility range and number of used colors) solutions to the infinite grid exploration problem (IGE) by a small team of M2C robots. Our first result shows that minimizing the visibility range and the number of used colors are two orthogonal issues: it is impossible to design a solution to the IGE problem that is optimal w.r.t. both parameters simultaneously. Consequently, we address optimality of these two criteria separately by proposing two algorithms; the former being optimal in terms of visibility range, the latter being optimal in terms of number of used colors. It is worth noticing that these two algorithms use a very small number of robots, respectively six and eight

Extempore: The design, implementation and application of a cyber-physical programming language

There is a long history of experimental and exploratory programming supported by systems that expose interaction through a programming language interface. These live programming systems enable software developers to create, extend, and modify the behaviour of executing software by changing source code without perceptual breaks for recompilation. These live programming systems have taken many forms, but have generally been limited in their ability to express low-level programming concepts and the generation of efficient native machine code. These shortcomings have limited the effectiveness of live programming in domains that require highly efficient numerical processing and explicit memory management. The most general questions addressed by this thesis are what a systems language designed for live programming might look like and how such a language might influence the development of live programming in performance sensitive domains requiring real-time support, direct hardware control, or high performance computing. This thesis answers these questions by exploring the design, implementation and application of Extempore, a new systems programming language, designed specifically for live interactive programming

Coordination schemes for distributed boundary coverage with a swarm of miniature robots:synthesis, analysis and experimental validation

We provide a comparison of a series of original coordination mechanisms for the distributed boundary coverage problem with a swarm of miniature robots. Our analysis is based on real robot experimentation and models at different levels of abstraction. Distributed boundary coverage is an instance of the distributed coverage problem and has applications such as inspection of structures, de-mining, cleaning, and painting. Coverage is a particularly good example for the benefits of a multi-robot approach due to the potential for parallel task execution and additional robustness out of redundancy. The constraints imposed by a potential application, the autonomous inspection of a jet turbine engine, were our motivation for the algorithms considered in this thesis. Thus, there is particular emphasis on how algorithms perform under the influence of sensor and actuator noise, limited computational and communication capabilities, as well as on the policies about how to cope with such problems. The algorithms developed in this dissertation can be classified into reactive and deliberative algorithms, as well as non-collaborative and collaborative algorithms. The performance of these algorithms ranges from very low to very high, corresponding to highly redundant coverage to near-optimal partitioning of the environments, respectively. At the same time, requirements and assumptions on the robotic platform and the environment (from no communication to global communication, and from no localization to global localization) are incrementally raised. All the algorithms are robust to sensor and actuator noise and gracefully decay to the performance of a randomized algorithm as a function of an increased noise level and/or additional hardware constraints. Although the deliberative algorithms are fully deterministic, the actual performance is probabilistic due to inevitable sensor and actuator noise. For this reason, probabilistic models are used for predicting time to complete coverage and take into account sensor and actuator noise calibrated by using real hardware. For reactive systems with limited memory, the performance is captured using a compact representation based on rate equations that track the expected number of robots in a certain state. As the number of states explode for the deliberative algorithms that require a substantial use of memory, this approach becomes less tractable with the amount of deliberation performed, and we use Discrete Event System (DES) simulation in these cases. Our contribution to the domain of multi-robot systems is three-fold. First, we provide a methodology for system identification and optimal control of a robot swarm using probabilistic models. Second, we develop a series of algorithms for distributed coverage by a team of miniature robots that gracefully decay from a near-optimal performance to the performance of a randomized approach under the influence of sensor and actuator noise. Third, we design an implement a miniature inspection platform based on the miniature robot Alice with ZigBee ready communication capabilities and color vision on a foot-print smaller than 2 × 2 × 3 cm3

Adaptiveness, Asynchrony, and Resource Efficiency in Parallel Stochastic Gradient Descent

Accelerated digitalization and sensor deployment in society in recent years poses critical challenges for associated data processing and analysis infrastructure to scale, and the field of big data, targeting methods for storing, processing, and revealing patterns in huge data sets, has surged. Artificial Intelligence (AI) models are used diligently in standard Big Data pipelines due to their tremendous success across various data analysis tasks, however exponential growth in Volume, Variety and Velocity of Big Data (known as its three V’s) in recent years require associated complexity in the AI models that analyze it, as well as the Machine Learning (ML) processes required to train them. In order to cope, parallelism in ML is standard nowadays, with the aim to better utilize contemporary computing infrastructure, whether it being shared-memory multi-core CPUs, or vast connected networks of IoT devices engaging in Federated Learning (FL).Stochastic Gradient Descent (SGD) serves as the backbone of many of the most popular ML methods, including in particular Deep Learning. However, SGD has inherently sequential semantics, and is not trivially parallelizable without imposing strict synchronization, with associated bottlenecks. Asynchronous SGD (AsyncSGD), which relaxes the original semantics, has gained significant interest in recent years due to promising results that show speedup in certain contexts. However, the relaxed semantics that asynchrony entails give rise to fundamental questions regarding AsyncSGD, relating particularly to its stability and convergence rate in practical applications.This thesis explores vital knowledge gaps of AsyncSGD, and contributes in particular to: Theoretical frameworks – Formalization of several key notions related to the impact of asynchrony on the convergence, guiding future development of AsyncSGD implementations; Analytical results – Asymptotic convergence bounds under realistic assumptions. Moreover, several technical solutions are proposed, targeting in particular: Stability – Reducing the number of non-converging executions and the associated wasted energy; Speedup – Improving convergence time and reliability with instance-based adaptiveness; Elasticity – Resource-efficiency by avoiding over-parallelism, and thereby improving stability and saving computing resources. The proposed methods are evaluated on several standard DL benchmarking applications and compared to relevant baselines from previous literature. Key results include: (i) persistent speedup compared to baselines, (ii) increased stability and reduced risk for non-converging executions, (iii) reduction in the overall memory footprint (up to 17%), as well as the consumed computing resources (up to 67%).In addition, along with this thesis, an open-source implementation is published, that connects high-level ML operations with asynchronous implementations with fine-grained memory operations, leveraging future research for efficient adaptation of AsyncSGD for practical applications