Parameterized Algorithms and Data Reduction for Safe Convoy Routing

We study a problem that models safely routing a convoy through a transportation network, where any vertex adjacent to the travel path of the convoy requires additional precaution: Given a graph G=(V,E), two vertices s,t in V, and two integers k,l, we search for a simple s-t-path with at most k vertices and at most l neighbors. We study the problem in two types of transportation networks: graphs with small crossing number, as formed by road networks, and tree-like graphs, as formed by waterways. For graphs with constant crossing number, we provide a subexponential 2^O(sqrt n)-time algorithm and prove a matching lower bound. We also show a polynomial-time data reduction algorithm that reduces any problem instance to an equivalent instance (a so-called problem kernel) of size polynomial in the vertex cover number of the input graph. In contrast, we show that the problem in general graphs is hard to preprocess. Regarding tree-like graphs, we obtain a 2^O(tw) * l^2 * n-time algorithm for graphs of treewidth tw, show that there is no problem kernel with size polynomial in tw, yet show a problem kernel with size polynomial in the feedback edge number of the input graph

Semi-Autonomous Small Unmanned Aircraft Systems for Sampling Tornadic Supercell Thunderstorms

This work describes the development of a network-centric unmanned aircraft system (UAS) for in situ sampling of supercell thunderstorms. UAS have been identified as a well-suited platform for meteorological observations given their portability, endurance, and ability to mitigate atmospheric disturbances. They represent a unique tool for performing targeted sampling in regions of a supercell thunderstorm previously unreachable through other methods. Doppler radar can provide unique measurements of the wind field in and around supercell thunderstorms. In order to exploit this capability, a planner was developed that can optimize ingress trajectories for severe storm penetration. The resulting trajectories were examined to determine the feasibility of such a mission, and to optimize ingress in terms of flight time and exposure to precipitation. A network-centric architecture was developed to handle the large amount of distributed data produced during a storm sampling mission. Creation of this architecture was performed through a bottom-up design approach which reflects and enhances the interplay between networked communication and autonomous aircraft operation. The advantages of the approach are demonstrated through several field and hardware-in-the-loop experiments containing different hardware, networking protocols, and objectives. Results are provided from field experiments involving the resulting network-centric architecture. An airmass boundary was sampled in the Collaborative Colorado Nebraska Unmanned Aircraft Experiment (CoCoNUE). Utilizing lessons learned from CoCoNUE, a new concept of operations (CONOPS) and UAS were developed to perform in situ sampling of supercell thunderstorms. Deployment during the Verification of the Origins of Rotation in Tornadoes Experiment 2 (VOR- TEX2) resulted in the first ever sampling of the airmass associated with the rear flank downdraft of a tornadic supercell thunderstorm by a UAS. Hardware-in-the-loop simulation capability was added to the UAS to enable further assessment of the system and CONOPS. The simulation combines a full six degree-of-freedom aircraft dynamic model with wind and precipitation data from simulations of severe convective storms. Interfaces were written to involve as much of the system\u27s field hardware as possible, including the creation of a simulated radar product server. A variety of simulations were conducted to evaluate different aspects of the CONOPS used for the 2010 VORTEX2 field campaign

Layoutautomatisierung im analogen IC-Entwurf mit formalisiertem und nicht-formalisiertem Expertenwissen

After more than three decades of electronic design automation, most layouts for analog integrated circuits are still handcrafted in a laborious manual fashion today. Obverse to the highly automated synthesis tools in the digital domain (coping with the quantitative difficulty of packing more and more components onto a single chip – a desire well known as More Moore), analog layout automation struggles with the many diverse and heavily correlated functional requirements that turn the analog design problem into a More than Moore challenge. Facing this qualitative complexity, seasoned layout engineers rely on their comprehensive expert knowledge to consider all design constraints that uncompromisingly need to be satisfied. This usually involves both formally specified and nonformally communicated pieces of expert knowledge, which entails an explicit and implicit consideration of design constraints, respectively. Existing automation approaches can be basically divided into optimization algorithms (where constraint consideration occurs explicitly) and procedural generators (where constraints can only be taken into account implicitly). As investigated in this thesis, these two automation strategies follow two fundamentally different paradigms denoted as top-down automation and bottom-up automation. The major trait of top-down automation is that it requires a thorough formalization of the problem to enable a self-intelligent solution finding, whereas a bottom-up automatism –controlled by parameters– merely reproduces solutions that have been preconceived by a layout expert in advance. Since the strengths of one paradigm may compensate the weaknesses of the other, it is assumed that a combination of both paradigms –called bottom-up meets top-down– has much more potential to tackle the analog design problem in its entirety than either optimization-based or generator-based approaches alone. Against this background, the thesis at hand presents Self-organized Wiring and Arrangement of Responsive Modules (SWARM), an interdisciplinary methodology addressing the design problem with a decentralized multi-agent system. Its basic principle, similar to the roundup of a sheep herd, is to let responsive mobile layout modules (implemented as context-aware procedural generators) interact with each other inside a user-defined layout zone. Each module is allowed to autonomously move, rotate and deform itself, while a supervising control organ successively tightens the layout zone to steer the interaction towards increasingly compact (and constraint compliant) layout arrangements. Considering various principles of self-organization and incorporating ideas from existing decentralized systems, SWARM is able to evoke the phenomenon of emergence: although each module only has a limited viewpoint and selfishly pursues its personal objectives, remarkable overall solutions can emerge on the global scale. Several examples exhibit this emergent behavior in SWARM, and it is particularly interesting that even optimal solutions can arise from the module interaction. Further examples demonstrate SWARM’s suitability for floorplanning purposes and its application to practical place-and-route problems. The latter illustrates how the interacting modules take care of their respective design requirements implicitly (i.e., bottom-up) while simultaneously paying respect to high level constraints (such as the layout outline imposed top-down by the supervising control organ). Experimental results show that SWARM can outperform optimization algorithms and procedural generators both in terms of layout quality and design productivity. From an academic point of view, SWARM’s grand achievement is to tap fertile virgin soil for future works on novel bottom-up meets top-down automatisms. These may one day be the key to close the automation gap in analog layout design.Nach mehr als drei Jahrzehnten Entwurfsautomatisierung werden die meisten Layouts für analoge integrierte Schaltkreise heute immer noch in aufwändiger Handarbeit entworfen. Gegenüber den hochautomatisierten Synthesewerkzeugen im Digitalbereich (die sich mit dem quantitativen Problem auseinandersetzen, mehr und mehr Komponenten auf einem einzelnen Chip unterzubringen – bestens bekannt als More Moore) kämpft die analoge Layoutautomatisierung mit den vielen verschiedenen und stark korrelierten funktionalen Anforderungen, die das analoge Entwurfsproblem zu einer More than Moore Herausforderung machen. Angesichts dieser qualitativen Komplexität bedarf es des umfassenden Expertenwissens erfahrener Layouter um sämtliche Entwurfsconstraints, die zwingend eingehalten werden müssen, zu berücksichtigen. Meist beinhaltet dies formal spezifiziertes als auch nicht-formal übermitteltes Expertenwissen, was eine explizite bzw. implizite Constraint Berücksichtigung nach sich zieht. Existierende Automatisierungsansätze können grundsätzlich unterteilt werden in Optimierungsalgorithmen (wo die Constraint Berücksichtigung explizit erfolgt) und prozedurale Generatoren (die Constraints nur implizit berücksichtigen können). Wie in dieser Arbeit eruiert wird, folgen diese beiden Automatisierungsstrategien zwei grundlegend unterschiedlichen Paradigmen, bezeichnet als top-down Automatisierung und bottom-up Automatisierung. Wesentliches Merkmal der top-down Automatisierung ist die Notwendigkeit einer umfassenden Problemformalisierung um eine eigenintelligente Lösungsfindung zu ermöglichen, während ein bottom-up Automatismus –parametergesteuert– lediglich Lösungen reproduziert, die vorab von einem Layoutexperten vorgedacht wurden. Da die Stärken des einen Paradigmas die Schwächen des anderen ausgleichen können, ist anzunehmen, dass eine Kombination beider Paradigmen –genannt bottom-up meets top down– weitaus mehr Potenzial hat, das analoge Entwurfsproblem in seiner Gesamtheit zu lösen als optimierungsbasierte oder generatorbasierte Ansätze für sich allein. Vor diesem Hintergrund stellt die vorliegende Arbeit Self-organized Wiring and Arrangement of Responsive Modules (SWARM) vor, eine interdisziplinäre Methodik, die das Entwurfsproblem mit einem dezentralisierten Multi-Agenten-System angeht. Das Grundprinzip besteht darin, ähnlich dem Zusammentreiben einer Schafherde, reaktionsfähige mobile Layoutmodule (realisiert als kontextbewusste prozedurale Generatoren) in einer benutzerdefinierten Layoutzone interagieren zu lassen. Jedes Modul darf sich selbständig bewegen, drehen und verformen, wobei ein übergeordnetes Kontrollorgan die Zone schrittweise verkleinert, um die Interaktion auf zunehmend kompakte (und constraintkonforme) Layoutanordnungen hinzulenken. Durch die Berücksichtigung diverser Selbstorganisationsgrundsätze und die Einarbeitung von Ideen bestehender dezentralisierter Systeme ist SWARM in der Lage, das Phänomen der Emergenz hervorzurufen: obwohl jedes Modul nur eine begrenzte Sichtweise hat und egoistisch seine eigenen Ziele verfolgt, können sich auf globaler Ebene bemerkenswerte Gesamtlösungen herausbilden. Mehrere Beispiele veranschaulichen dieses emergente Verhalten in SWARM, wobei besonders interessant ist, dass sogar optimale Lösungen aus der Modulinteraktion entstehen können. Weitere Beispiele demonstrieren SWARMs Eignung zwecks Floorplanning sowie die Anwendung auf praktische Place-and-Route Probleme. Letzteres verdeutlicht, wie die interagierenden Module ihre jeweiligen Entwurfsanforderungen implizit (also: bottom-up) beachten, während sie gleichzeitig High-Level-Constraints berücksichtigen (z.B. die Layoutkontur, die top-down vom übergeordneten Kontrollorgan auferlegt wird). Experimentelle Ergebnisse zeigen, dass Optimierungsalgorithmen und prozedurale Generatoren von SWARM sowohl bezüglich Layoutqualität als auch Entwurfsproduktivität übertroffen werden können. Aus akademischer Sicht besteht SWARMs große Errungenschaft in der Erschließung fruchtbaren Neulands für zukünftige Arbeiten an neuartigen bottom-up meets top-down Automatismen. Diese könnten eines Tages der Schlüssel sein, um die Automatisierungslücke im analogen Layoutentwurf zu schließen

Department of Computer Science Activity 1998-2004

This report summarizes much of the research and teaching activity of the Department of Computer Science at Dartmouth College between late 1998 and late 2004. The material for this report was collected as part of the final report for NSF Institutional Infrastructure award EIA-9802068, which funded equipment and technical staff during that six-year period. This equipment and staff supported essentially all of the department\u27s research activity during that period

Decision uncertainty minimization and autonomous information gathering

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 272-283).Over the past several decades, technologies for remote sensing and exploration have become increasingly powerful but continue to face limitations in the areas of information gathering and analysis. These limitations affect technologies that use autonomous agents, which are devices that can make routine decisions independent of operator instructions. Bandwidth and other communications limitation require that autonomous differentiate between relevant and irrelevant information in a computationally efficient manner. This thesis presents a novel approach to this problem by framing it as an adaptive sensing problem. Adaptive sensing allows agents to modify their information collection strategies in response to the information gathered in real time. We developed and tested optimization algorithms that apply information guides to Monte Carlo planners. Information guides provide a mechanism by which the algorithms may blend online (realtime) and offline (previously simulated) planning in order to incorporate uncertainty into the decisionmaking process. This greatly reduces computational operations as well as decisional and communications overhead. We begin by introducing a 3-level hierarchy that visualizes adaptive sensing at synoptic (global), mesocale (intermediate) and microscale (close-up) levels (a spatial hierarchy). We then introduce new algorithms for decision uncertainty minimization (DUM) and representational uncertainty minimization (RUM). Finally, we demonstrate the utility of this approach to real-world sensing problems, including bathymetric mapping and disaster relief. We also examine its potential in space exploration tasks by describing its use in a hypothetical aerial exploration of Mars. Our ultimate goal is to facilitate future large-scale missions to extraterrestrial objects for the purposes of scientific advancement and human exploration.by Lawrence A. M. Bush.Ph. D

Intelligent Transportation Systems, Hybrid Electric Vehicles, Powertrain Control, Cooperative Adaptive Cruise Control, Model Predictive Control

Information obtainable from Intelligent Transportation Systems (ITS) provides the possibility of improving the safety and efficiency of vehicles at different levels. In particular, such information has the potential to be utilized for prediction of driving conditions and traffic flow, which allows us to improve the performance of the control systems in different vehicular applications, such as Hybrid Electric Vehicles (HEVs) powertrain control and Cooperative Adaptive Cruise Control (CACC). In the first part of this work, we study the design of an MPC controller for a Cooperative Adaptive Cruise Control (CACC) system, which is an automated application that provides the drivers with extra benefits, such as traffic throughput maximization and collision avoidance. CACC systems must be designed in a way that are sufficiently robust against all special maneuvers such as interfering vehicles cutting-into the CACC platoons or hard braking by leading cars. To address this problem, we first propose a Neural- Network (NN)-based cut-in detection and trajectory prediction scheme. Then, the predicted trajectory of each vehicle in the adjacent lanes is used to estimate the probability of that vehicle cutting-into the CACC platoon. To consider the calculated probability in control system decisions, a Stochastic Model Predictive Controller (SMPC) needs to be designed which incorporates this cut-in probability, and enhances the reaction against the detected dangerous cut-in maneuver. However, in this work, we propose an alternative way of solving this problem. We convert the SMPC problem into modeling the CACC as a Stochastic Hybrid System (SHS) while we still use a deterministic MPC controller running in the only state of the SHS model. Finally, we find the conditions under which the designed deterministic controller is stable and feasible for the proposed SHS model of the CACC platoon. In the second part of this work, we propose to improve the performance of one of the most promising realtime powertrain control strategies, called Adaptive Equivalent Consumption Minimization Strategy (AECMS), using predicted driving conditions. In this part, two different real-time powertrain control strategies are proposed for HEVs. The first proposed method, including three different variations, introduces an adjustment factor for the cost of using electrical energy (equivalent factor) in AECMS. The factor is proportional to the predicted energy requirements of the vehicle, regenerative braking energy, and the cost of battery charging and discharging in a finite time window. Simulation results using detailed vehicle powertrain models illustrate that the proposed control strategies improve the performance of AECMS in terms of fuel economy by 4\%. Finally, we integrate the recent development in reinforcement learning to design a novel multi-level power distribution control. The proposed controller reacts in two levels, namely high-level and low-level control. The high-level control decision estimates the most probable driving profile matched to the current (and near future) state of the vehicle. Then, the corresponding low-level controller of the selected profile is utilized to distribute the requested power between Electric Motor (EM) and Internal Combustion Engine (ICE). This is important because there is no other prior work addressing this problem using a controller which can adjust its decision to the driving pattern. We proposed to use two reinforcement learning agents in two levels of abstraction. The first agent, selects the most optimal low-level controller (second agent) based on the overall pattern of the drive cycle in the near past and future, i.e., urban, highway and harsh. Then, the selected agent by the high-level controller (first agent) decides how to distribute the demanded power between the EM and ICE. We found that by carefully designing a training scheme, it is possible to effectively improve the performance of this data-driven controller. Simulation results show up to 6\% improvement in fuel economy compared to the AECMS

Optimal Control of an Uninhabited Loyal Wingman

As researchers strive to achieve autonomy in systems, many believe the goal is not that machines should attain full autonomy, but rather to obtain the right level of autonomy for an appropriate man-machine interaction. A common phrase for this interaction is manned-unmanned teaming (MUM-T), a subset of which, for unmanned aerial vehicles, is the concept of the loyal wingman. This work demonstrates the use of optimal control and stochastic estimation techniques as an autonomous near real-time dynamic route planner for the DoD concept of the loyal wingman. First, the optimal control problem is formulated for a static threat environment and a hybrid numerical method is demonstrated. The optimal control problem is transcribed to a nonlinear program using direct orthogonal collocation, and a heuristic particle swarm optimization algorithm is used to supply an initial guess to the gradient-based nonlinear programming solver. Next, a dynamic and measurement update model and Kalman filter estimating tool is used to solve the loyal wingman optimal control problem in the presence of moving, stochastic threats. Finally, an algorithm is written to determine if and when the loyal wingman should dynamically re-plan the trajectory based on a critical distance metric which uses speed and stochastics of the moving threat as well as relative distance and angle of approach of the loyal wingman to the threat. These techniques are demonstrated through simulation for computing the global outer-loop optimal path for a minimum time rendezvous with a manned lead while avoiding static as well as moving, non-deterministic threats, then updating the global outer-loop optimal path based on changes in the threat mission environment. Results demonstrate a methodology for rapidly computing an optimal solution to the loyal wingman optimal control problem

Feasible, Robust and Reliable Automation and Control for Autonomous Systems

The Special Issue book focuses on highlighting current research and developments in the automation and control field for autonomous systems as well as showcasing state-of-the-art control strategy approaches for autonomous platforms. The book is co-edited by distinguished international control system experts currently based in Sweden, the United States of America, and the United Kingdom, with contributions from reputable researchers from China, Austria, France, the United States of America, Poland, and Hungary, among many others. The editors believe the ten articles published within this Special Issue will be highly appealing to control-systems-related researchers in applications typified in the fields of ground, aerial, maritime vehicles, and robotics as well as industrial audiences