A área de robótica móvel se encontra numa fase de grande expansão, mas um dos obstáculos a ser vencido é o desenvolvimento de sistemas computacionais embarcados que combinem baixo consumo de energia com alta capacidade de processamento. A computação reconfigurável tem o potencial para atender esta demanda. Este trabalho visa avaliar as dificuldades no aproveitamento desta tecnologia através da implementação em hardware de um sistema de desvio de obstáculos para robôs móveis usando uma única câmera de baixo custo como sensor. Normalmente os algorítmos de fluxo óptico usados neste projeto são implementados inteiramente em software e sofrem várias restrições para poderem operar nos computadores embarcados nos robôs. O projeto descrito neste trabalho não tem estas restrições mas exige um esforço maior de desenvolvimento / The area of mobile robotics is undergoing a tremendous expansion, but one of the obstacles to be dealt with is the development of embedded computational systems that combine low power consumption and high performance. Reconfigurable computing has the potential to meet these requirements. This project is an evaluation of the complexities of fully exploiting this technology through the hardware implementation of an obstacle avoidance system for mobile robots using a single, low cost camera as its sensor. Normally, the optic flow algorithms used in this project are implemented entirely in software and so suffer several limitations in order to run on computers embedded in the robots. The hardware described here does not have the same limitations but requires more development effort

Soft robotic systems are becoming increasingly popular as they are generally safer, lighter, and easier to manufacture than their more rigid, traditional counterparts. These advantages allow an increased sense of freedom in both the design and operation of these platforms. In this work, we seek methods of leveraging this freedom to both design and plan motions for two different serial-chain, pneumatically actuated manipulators developed by Pneubotics, a small startup company based in San Francisco. In doing so, we focus primarily on two related endeavors: (1) the optimal kinematic design of these and other similar robots (i.e., choosing link lengths, base positioning, etc.), and (2) the planning of smooth paths in joint space that enable these robots to perform useful tasks. Our method of design optimization employs a genetic algorithm in combination with maximin multi-objective optimization techniques to efficiently generate a diverse set of Pareto optimal designs. This set represents the optimal region of the design space and highlights inherent tradeoffs that designers must make when choosing a particular set of design parameters for manufacture. In our work, we have chosen to optimize inflatable robots to be both dexterous, and to be able to support loads near the ground with limited deflection. We have also applied our framework to optimize a plastic manipulator to perform painting motions. In our approach to motion planning we simultaneously optimize the base position and joint motions of a robot in order to enable its end effector to follow a smooth desired trajectory. While this method of path planning generalizes to any kind of robot, we envision it to be especially applicable to soft robots and other mobile robots that can be quickly and easily repositioned to perform tasks in varying environments. Our method of path planning works by moving a set of virtual robot arms (each representing a single configuration in a sequence) branching from a common base, to a number of assigned target poses associated with a task. Additional goals and hard constraints (including joint limits) are naturally incorporated. The optimization problem at the core of this method is a quadratic program, allowing constrained high-dimensional problems to be solved in very little time. We demonstrate our method by planning and performing painting motion on two different systems. We also demonstrate in simulation how our planner could be used to perform several common tasks including those involving, pick-and-place, wiping and wrapping motions.

Miniature Unmanned Aerial Vehicles (UAVs) are currently being researched for a wide range of tasks, including search and rescue, surveillance, reconnaissance, traffic monitoring, fire detection, pipe and electrical line inspection, and border patrol to name only a few of the application domains. Although small / miniature UAVs, including both Vertical Takeoff and Landing (VTOL) vehicles and small helicopters, have shown great potential in both civilian and military domains, including research and development, integration, prototyping, and field testing, these unmanned systems / vehicles are limited to only a handful of university labs. For VTOL type aircraft the number is less than fifteen worldwide! This lack of development is due to both the extensive time and cost required to design, integrate and test a fully operational prototype as well as the shortcomings of published materials to fully describe how to design and build a "complete" and "operational" prototype system. This dissertation overcomes existing barriers and limitations by describing and presenting in great detail every technical aspect of designing and integrating a small UAV helicopter including the on-board navigation controller, capable of fully autonomous takeoff, waypoint navigation, and landing. The presented research goes beyond previous works by designing the system as a testbed vehicle. This design aims to provide a general framework that will not only allow researchers the ability to supplement the system with new technologies but will also allow researchers to add innovation to the vehicle itself. Examples include modification or replacement of controllers, updated filtering and fusion techniques, addition or replacement of sensors, vision algorithms, Operating Systems (OS) changes or replacements, and platform modification or replacement. This is supported by the testbed's design to not only adhere to the technology it currently utilizes but to be general enough to adhere to a multitude of technology that have yet to be tested. This research will allow labs without the proper expertise to build a safe and reliable vehicle that can provide them access to real world data thus increasing the effectiveness and validity of their research. It will also allow researchers working in simulation to quickly enter into UAV development without utilizing thousands of man hours to create an unmanned vehicle. The presented research is designed to benefit the entire UAV researching community by allowing in depth access to an area of research that has been typically classified as too expensive and too time consuming to enter.

Robot localization is one of the most important subjects in the Robotics science. It is an interesting and complicated topic. There are many algorithms to solve the problem of localization. Each localization system has its own set of features, and based on them, a solution will be chosen. In my thesis, I want to present a solution to find the best estimate for a robot position in certain space for which a map is available. The thesis started with an elementary introduction to the probability and the Gaussian theories. Simple and advanced practical examples are presented to illustrate each concept related to localization. Extended Kalman Filter is chosen to be the main algorithm to find the best estimate of the robot position. It was presented through two chapters with many examples. All these examples were simulated in Matlab in this thesis in order to give the readers and future students a clear and complete introduction to Kalman Filter. Fortunately, I applied this algorithm on a robot that I have built its base from scratch. MCECS-Bot was a project started in Winter 2012 and it was assigned to me from my adviser, Dr. Marek Perkowski. This robot consists of the base with four Mecanum wheels, the waist based on four linear actuators, an arm, neck and head. The base is equipped with many sensors, which are bumper switches, encoders, sonars, LRF and Kinect. Additional devices can provide extra information as backup sensors, which are a tablet and a camera. The ultimate goal of this thesis is to have the MCECS-Bot as an open source system accessed by many future classes, capstone projects and graduate thesis students for education purposes. A well-known MRPT software system was used to present the results of the Extended Kalman Filter (EKF). These results are simply the robot positions estimated by EKF. They are demonstrated on the base floor of the FAB building of PSU. In parallel, simulated results to all different solutions derived in this thesis are presented using Matlab. A future students will have a ready platform and a good start to continue developing this system.

In the design of autonomous mobile robots, databases have been used mainly to store information on the environment in which the device is to operate. For most of the models and ready systems, the database when used, is not a stand alone component in the system, rather it is only intended to keep static information on the disposition and properties of objects on the map.

A novel methodology is presented for organizing swarms of robots into a formation utilizing artificial potential fields generated from normal and sigmoid functions. These functions construct the surface which swarm members travel on, controlling the overall swarm geometry and the individual member spacing. Nonlinear limiting functions are defined to provide tighter swarm control by modifying and adjusting a set of control variables forcing the swarm to behave according to set constraints, formation and member spacing. The swarm function and limiting functions are combined to control swarm formation, orientation, and swarm movement as a whole. Parameters are chosen based on desired formation as well as user defined constraints. This approach compared to others, is simple, computationally efficient, scales well to different swarm sizes, to heterogeneous systems, and to both centralized and decentralized swarm models. Simulation results are presented for a swarm of four and ten particles following circle, ellipse and wedge formations. Experimental results are also included with a swarm of four unmanned ground vehicles (UGV) as well as UGV swarm and unmanned aerial vehicle (UAV) coordination.

This thesis is concerned with the Distributed Information Gathering (DIG) problem in which a Sensor Network is tasked with building a common representation of environment. The problem is motivated by the advantages offered by distributed autonomous sensing systems and the challenges they present. The focus of this study is on Macro Sensor Networks, characterized by platform mobility, heterogeneous teams, and long mission duration. The system under consideration may consist of an arbitrary number of mobile autonomous robots, stationary sensor platforms, and human operators, all linked in a network. This work describes a comprehensive framework called Active Sensor Network (ASN) which addresses the tasks of information fusion, decistion making, system configuration, and user interaction. The main design objectives are scalability with the number of robotic platforms, maximum flexibility in implementation and deployment, and robustness to component and communication failure. The framework is described from three complementary points of view: architecture, algorithms, and implementation. The main contribution of this thesis is the development of the ASN architecture. Its design follows three guiding principles: decentralization, modularity, and locality of interactions. These principles are applied to all aspects of the architecture and the framework in general. To achieve flexibility, the design approach emphasizes interactions between components rather than the definition of the components themselves. The architecture specifies a small set of interfaces sufficient to implement a wide range of information gathering systems. In the area of algorithms, this thesis builds on the earlier work on Decentralized Data Fusion (DDF) and its extension to information-theoretic decistion making. It presents the Bayesian Decentralized Data Fusion (BDDF) algorithm formulated for environment features represented by a general probability density function. Several specific representations are also considered: Gaussian, discrete, and the Certainty Grid map. Well known algorithms for these representations are shown to implement various aspects of the Bayesian framework. As part of the ASN implementation, a practical indoor sensor network has been developed and tested. Two series of experiments were conducted, utilizing two types of environment representation: 1) point features with Gaussian position uncertainty and 2) Certainty Grid maps. The network was operational for several days at a time, with individual platforms coming on and off-line. On several occasions, the network consisted of 39 software components. The lessons learned during the system's development may be applicable to other heterogeneous distributed systems with data-intensive algorithms.

Thesis (M.S.)--Rochester Institute of Technology, 2007. / Typescript. Includes bibliographical references (leaves 61-64).

Thesis (Ph. D.)--University of Washington, 2006. / Vita. Includes bibliographical references (leaves 134-141).

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