Network connectivity tracking for a team of unmanned aerial vehicles

Algebraic connectivity is the second-smallest eigenvalue of the Laplacian matrix and can be used as a metric for the robustness and efficiency of a network. This connectivity concept applies to teams of multiple unmanned aerial vehicles (UAVs) performing cooperative tasks, such as arriving at a consensus. As a UAV team completes its mission, it often needs to control the network connectivity. The algebraic connectivity can be controlled by altering edge weights through movement of individual UAVs in the team, or by adding and deleting edges. The addition and deletion problem for algebraic connectivity, however, is NP-hard. The contributions of this work are 1) a comparison of four heuristic methods for modifying algebraic connectivity through the addition and deletion of edges, 2) a rule-based algorithm for tracking a connectivity profile through edge weight modification and the addition and deletion of edges, 3) a new, hybrid method for selecting the best edge to add or remove, 4) a distributed method for estimating the eigenvectors of the Laplacian matrix and selecting the best edge to add or remove for connectivity modification and tracking, and 5) an implementation of the distributed connectivity tracking using a consensus controller and double-integrator dynamics

Mutual information-based gradient-ascent control for distributed robotics

Thesis (Ph. D.)--Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 167-179).This thesis presents the derivation, analysis, and implementation of a novel class of decentralized mutual information-based gradient-ascent controllers that continuously move robots equipped with sensors to better observe their environment. We begin with the fundamental problem of deploying a single ground robot equipped with a range sensor and tasked to build an occupancy grid map. The desired explorative behaviors of the robot for occupancy grid mapping highlight the correlation between the information content and the spatial realization of the robot's range measurements. We prove that any occupancy grid controller tasked to maximize a mutual information reward function is eventually attracted to unexplored space, i.e., areas of highest uncertainty. We show that mutual information encodes geometric relationships that are fundamental to robot control and yields geometrically relevant reward surfaces on which robots can navigate. Taking inspiration from geometric-based approaches to distributed robot coordination, we show that many multi-robot inference tasks can be cast in terms of an optimization problem. This optimization problem defines the task of minimizing the conditional entropy associated with the robots' inferred beliefs of the environment, which is equivalent to maximizing the mutual information between the environment state and the robots' next joint observation. Given simple robot dynamics and few probabilistic assumptions, none of which involve Gaussianity, we derive a gradientascent solution approach to these optimization problems that is convergent between sensor observations and locally optimal. More formally, we invoke LaSalle's Invariance Principle to prove that, given enough time between consecutive joint observations, robots following the gradient of mutual information will converge to goal positions that locally maximize the expected information gain resulting from the next observation. We show that the algorithmic implementation of the generalized gradient-ascent controller is not readily distributed among multiple robots, and thus sample-based methods are introduced to distributively approximate the likelihoods of the robots' joint observations. Not only are the involved non-parametric representations compatible with any type of Bayesian filter, but the computational complexities of the resulting decentralized controllers are independent with respect to the number of robots. Concerning the distributed approximations, we give two example consensus-based algorithms that run on an undirected network graph. The first consensus-based algorithm approximates discrete measurement probabilities, while the second approximates continuous likelihood distributions. We show that these anytime approximations provably converge to the correct values on a static and connected network graph without knowledge of the number of robots in the network or the corresponding graph's topology. Lastly, we incorporate the resulting consensus-based algorithms into both a hardware system and a simulation environment to allow for decentralized controller evaluation under non-ideal network settings. For the hardware experiments, the task is to infer the state of a bounded, planar environment by deploying five quadrotor flying robots with simulated sensors in both indoor and outdoor settings. For the numerical simulations, Monte Carlo-based analyses are performed for 100 robots, where each robot is simulated on an independent computer node within a computer cluster system. Simulations are also performed for 1000 robots using a single workstation computer equipped with a multicore GPU-enabled graphics card. The results from both the hardware experiments and numerical simulations validate our theoretical and computational claims throughout the thesis.by Brian John Julian.Ph.D

Distributed Control and State Estimation of DC Microgrids Based on Constrained Communication Networks.

PhD ThesesThe intermittent nature of renewable energy sources (RES) such as wind turbines and photovoltaic panels, requires advanced control systems to provide the balance between energy supply and demand in any power system. For better management of power quality and security issues, energy storage systems (ESSs) are deployed to compensate for the temporary mismatch of supply and demand. Furthermore, in rural areas with no connection to the main grid, ESSs such as batteries are deployed in large quantities as a solution for temporary power stabilization during RES unavailability. However, the control complexity of the power system increases as more ESSs are getting installed due to the need for coordination of the power transfer among them. This thesis undertakes a thorough analysis of distributed control and state estimation designs for direct current (DC) microgrids with ESSs based on constrained communication networks. The developed distributed control and estimation strategies are designed for operation over constrained communication networks. They don't require a central coordinator for synchronization of the control tasks between the ESSs. This forms a multi-agent environment where the controllers cooperatively achieve the DC microgrid objectives, i.e. voltage stabilization, proportional power-sharing, and balancing of ESSs' energy level. To overcome the communication network constraints, event-based controllers and estimators are designed, which e ectively reduce the network tra c and as a result, provide higher throughput with reduced delays for the real-time control loops of the DC microgrids. The controllers are designed to be distributed, leading to use cases such as autonomous islanded microgrids, smart villages, and plug-and-play mobile microgrids. The feasibility and performance of the proposed control and estimation strategies are con rmed in several experimental test benches by showing the higher reliability and robustness in the delivered power quality. The results have shown considerable reduction in the network tra c, meanwhile the control system provided high performance in terms of stability, robustness, power quality and endurabilit

A multi-agent based system to promote collaboration among Namibian transport stakeholders in order to reduce empty runs

The main aim of transport stakeholders has always been to transport freight efficiently, as this efficiency contributes to the growth and success of their business. A country like Namibia is no different as the efficiency of transport lies in the effective utilisation of carrier capacity in any direction. Due to the various types of freight, transport operators rarely have the capacity to cover all freight movement requests. This research put the empty runs experienced by most of the Namibian transporters at 33%. Empty runs could however be reduced through collaboration and sharing of capacity among transport stakeholders. Multi-agent systems (MAS) are various individual computer agents that are configured independently to interact with other agents to achieve one goal. These systems have been explored as an approach to achieve collaboration among transporter stakeholders. Taking into consideration the characteristics and requirements of MAS, this research was able to conduct a feasibility of its implementation within Namibia. Concluding with an evaluation of available Multi-agent based systems that could achieve collaboration and reduce empty runs in the Namibian transport environment.Information ScienceM. Tech. (Information Technology

Consensus control in robot networks and cooperative teleoperation : an operational space approach

An interesting approach in cooperative control is to design distributed control strategies which use only local information so that a multi-agent system achieves specified behaviors. A basic behavior in cooperative control is the consensus. Given a multi-agent system, like a multiple robot network, it is said that the agents reach a consensus if the state of each agent converges to a common state. Examples of cooperative tasks in which consensus algorithms are employed include formation control, flocking theory, rendezvous problems and synchronization. These cooperative tasks have several possible applications, like: transportation systems (intelligent highways, air-traffic control); military systems (formation flight, surveillance, reconnaissance, cooperative attack and rendezvous) and mobile sensor networks (space-based interferometers, environmental sampling). The solution to the consensus problems involves the design of control algorithms such that the agents can reach an agreement on their states. There are two main problems that are studied in consensus, the leader-follower consensus and the leaderless consensus. In the leader-follower consensus problem, there exists a leader that specifies the state for the whole group while in a leaderless consensus problem, there is not a priori reference state. The main goal of this thesis is the design of operational space controllers that solve the leader-follower and the leaderless consensus problems in networks composed of multiple heterogeneous robots. Furthermore, this document proposes novel operational space control schemes for bilateral teleoperation systems. In both scenarios, different conditions are studied, such as the absence of robot velocity measurements, constant and variable time-delays in the robot's interconnection, and uncertainty in the robot's physical parameters. Most of the previous consensus control algorithms, only work with the position or orientation but not with both. On the contrary, this dissertation deals with the entire pose of the robots that contains both the position and the orientation. Moreover, in order to render a singularity-free description of the orientation, the unit-quaternions are employed. The dissertation provides a rigorous stability analysis of the control algorithms and presents simulations and experiments that validate the effectiveness of the proposed controllers.Un enfoque interesante en el control cooperativo es el diseño de estrategias de control distribuido que requieran sólo información local para que un sistema multi-agente logre comportamientos específicos. Un comportamiento básico del control cooperativo es el consenso. Dado un sistema multi-agente, como una red de múltiples robots, se dice que los agentes llegan a un consenso si el estado de cada agente converge a un estado común. Algunos ejemplos de tareas cooperativas en las que los algoritmos de consenso son utilizados son los siguientes: el control de la formación, flocking, rendezvous y sincronización. Estas tareas cooperativas tienen varias aplicaciones posibles, como: sistemas de transporte (carreteras inteligentes , control de tráfico aéreo); sistemas militares (vuelo en formación, vigilancia, reconocimiento, ataque cooperativo) y redes de sensores móviles (interferómetros en el espacio, el muestreo del ambiente). La solución a los problemas de consenso implica el diseño de algoritmos de control de tal manera que los agentes pueden llegar a un acuerdo sobre sus estados. Hay dos problemas principales que se estudian en el consenso, el consenso líder-seguidor y el consenso sin líder. En el problema de consenso líder-seguidor, existe un líder que especifica el estado de todo el grupo, mientras que en un problema de consenso sin líder, no hay ningún estado de referencia definido a priori. El objetivo principal de esta tesis es el diseño de controladores en el espacio operacional que resuelvan los problemas de consenso líder-seguidor y sin líder en redes compuestas de múltiples robots heterogéneos. Además, este documento propone novedosos esquemas de control en el espacio operacional para sistemas de teleoperación bilateral. En ambos escenarios, se estudian diferentes condiciones, tales como la ausencia de medidas de velocidad de los robots, retardos constantes y variables en la interconexión de los robots y la incertidumbre en los parámetros físicos de los robots. La mayoría de los anteriores algoritmos de control que resuelven el consenso, sólo trabajan con la posición o la orientación, pero no con ambos. Por el contrario, esta tesis doctoral se ocupa de toda la pose de los robots que contiene tanto la posición y la orientación. Por otra parte, a fin de usar una representación de la orientación libre de singularidades, se emplean los cuaterniones unitarios. Esta tesis doctoral proporciona un análisis riguroso de la estabilidad de los algoritmos de control y presenta simulaciones y experimentos que validan la eficacia de los controladores propuesto

Mass Customization of Cloud Services - Engineering, Negotiation and Optimization

Several challenges hinder the entry of mass customization principles into Cloud computing: Firstly, the service engineering on provider side needs to be automated. Secondly, there has to be a suitable negotiation mechanism helping provider and consumer on finding an agreement on Quality-of-Service and price. Thirdly, finding the optimal configuration requires adequate and efficient optimization techniques. The work at hand addresses these challenges through technical and economic contributions

Mobile Ad-Hoc Networks

Being infrastructure-less and without central administration control, wireless ad-hoc networking is playing a more and more important role in extending the coverage of traditional wireless infrastructure (cellular networks, wireless LAN, etc). This book includes state-of-the-art techniques and solutions for wireless ad-hoc networks. It focuses on the following topics in ad-hoc networks: quality-of-service and video communication, routing protocol and cross-layer design. A few interesting problems about security and delay-tolerant networks are also discussed. This book is targeted to provide network engineers and researchers with design guidelines for large scale wireless ad hoc networks

Sensing and Signal Processing in Smart Healthcare

In the last decade, we have witnessed the rapid development of electronic technologies that are transforming our daily lives. Such technologies are often integrated with various sensors that facilitate the collection of human motion and physiological data and are equipped with wireless communication modules such as Bluetooth, radio frequency identification, and near-field communication. In smart healthcare applications, designing ergonomic and intuitive human–computer interfaces is crucial because a system that is not easy to use will create a huge obstacle to adoption and may significantly reduce the efficacy of the solution. Signal and data processing is another important consideration in smart healthcare applications because it must ensure high accuracy with a high level of confidence in order for the applications to be useful for clinicians in making diagnosis and treatment decisions. This Special Issue is a collection of 10 articles selected from a total of 26 contributions. These contributions span the areas of signal processing and smart healthcare systems mostly contributed by authors from Europe, including Italy, Spain, France, Portugal, Romania, Sweden, and Netherlands. Authors from China, Korea, Taiwan, Indonesia, and Ecuador are also included

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