Electrical & Computer Engineering
Permanent URI for this communityhttp://hdl.handle.net/1903/2234
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Item Novel Integrated System Architecture for an Autonomous Jumping Micro-Robot(2010) Churaman, Wayne Anthony; Goldsman, Neil; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)As the capability and complexity of robotic platforms continue to evolve from the macro to micro-scale, innovation of such systems is driven by the notion that a robot must be able to sense, think, and act [1]. The traditional architecture of a robotic platform consists of a structural layer upon which, actuators, controls, power, and communication modules are integrated for optimal system performance. The structural layer, for many micro-scale platforms, has commonly been implemented using a silicon die, thus leading to robotic platforms referred to as "walking chips" [2]. In this thesis, the first-ever jumping microrobotic platform is demonstrated using a hybrid integration approach to assemble on-board sensing and power directly onto a polymer chassis. The microrobot detects a change in light intensity and ignites 0.21mg of integrated nanoporous energetic silicon, resulting in 246µJ of kinetic energy and a vertical jump height of 8cm.Item Implementation, Evaluation, and Applications of Mobile Mesh Networks for Platforms in Motion(2009) Napora, Jared Stanislaus; Davis, Christopher C; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This thesis explores the selection, implementation, and evaluation of two mobile mesh networks, each involving a different distributed computing problem. In the forthcoming discussion, it will become apparent how system constraints affect the optimal choice of mesh networking design and implementation in these cases. The first problem explores the design and implementation of a distributed computing mesh network that will allow a collection of autonomous land vehicles to gather, process, and exchange information in an unknown environment. This network was established by adapting standard commercial 802.11 routers and by providing a software framework that handles all communication between wireless nodes. The second problem involves the design of a network for tracking and monitoring personnel. This network was implemented utilizing ZigBee modules due to power and custom implementation constraints. Both networks were tested with respect to their specific design constraints and they lay the foundation for additional application development and research.Item Bio-inspired VLSI Systems: from Synapse to Behavior(2008-08-04) Xu, Peng; Abshire, Pamela; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)We investigate VLSI systems using biological computational principles. The elegance of biological systems throughout the structure levels provides possible solutions to many engineering challenges. Specifically, we investigate neural systems at the synaptic level and at the sensorimotor integration level, which inspire our similar implementations in silicon. For both VLSI systems, we use floating gate MOSFETs in standard CMOS processes as nonvolatile storage elements, which enable adaptation and programmability. We propose a compact silicon stochastic synapse and methods to incorporate activity-dependent dynamics, which emulate a biological stochastic synapse. We implement and demonstrate the first silicon stochastic synapse with short-term depression by modulating the influence of noise on the circuit. The circuit exhibits true randomness and similar behavior of rate normalization and information redundancy reduction as its biological counterparts. The circuit behavior also agrees well with the theory and simulation of a circuit model based on a subtractive single release model. To understand the stochastic behavior of the silicon stochastic synapse and the stochastic operation of conventional circuits due to semiconductor technology scaling, we develop the stochastic modeling of circuits and transient analysis from the numerical solution of the stochastic model. The analytical solution of steady state distribution could be obtained from first principles. Small signal stochastic models show the interaction between noise and circuit dynamics, elucidating the effect of device parameters and biases on the stochastic behavior. We investigate optic flow wide field integration based navigation inspired from the fly in simulation, theory, and VLSI design. We generalize the framework to limited view angles. We design and test an integrated motion image sensor with on-chip optic flow estimation, adaptation, and programmable spatial filtering to directly interface with actuators for autonomous navigation. This is the first reported image sensor that uses the spatial motion pattern to extract motion parameters enabled by the mismatch compensation and programmable filters. The sensor is integrated with a ground vehicle and navigation through simple tunnel environments is demonstrated. It provides light weight and low power integrated approach to autonomous navigation of micro air vehicles.Item Steering Laws for Pursuit(2007-08-13) Reddy, Puduru Viswanadha; Krishnaprasad, Perinkulam S; Justh, Eric W; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Pursuit problems have attracted considerable attention from biologists, mathematicians and engineers. Guidance/steering laws are essential in robotic systems. In this thesis, we first review the results on steering laws for a specific pursuit process, motion camouflage with respect to infinity. This type of guidance law renders the baseline (line connecting the pursuer and evader) parallel to a fixed line. In observations of prey pursuit trajectories of echolocating bats, it has been noted that the same geometrical condition of eventual parallelism holds. We hypothesize that a steering law of the same form as discussed here for motion camouflage with respect to infinity, also applies to the trajectories of prey capture behavior by bats. In this thesis, we develop a method to extract curvatures for trajectories and a detailed investigation to validate this hypothesis. In the latter part of the thesis, we discuss the effect of delays on the performance of motion camouflage laws. We derive limits on the feedback gain and an upper bound on the delay that can be allowed in a pursuit evader system, to ensure successful motion camouflage.Item Collaborative Control of Autonomous Swarms with Resource Constraints(2006-11-24) Xi, Wei; Baras, John; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation focuses on the collaborative control of homogeneous UAV swarms. A two-level scheme is proposed by combining the high-level path planning and the lowlevel vehicle motion control. A decentralized artificial potential function (APF) based approach, which mimics the bacteria foraging process, is studied for the high-level path planning. The deterministic potential based approach, however, suffers from the local minima entrapment dilemma, which motivate us to fix the "flaw" that is naturally embedded. An innovative decentralized stochastic approach based on the Markov Random Filed (MRF) theory is proposed; this approach traditionally used in statistical mechanics and in image processing. By modeling the local interactions as Gibbs potentials, the movements of vehicles are then decided by using Gibbs sampler based simulated annealing (SA) algorithm. A two-step sampling scheme is proposed to coordinate vehicle networks: in the first sampling step, a vehicle is picked through a properly designed, configuration-dependent proposal distribution, and in the second sampling step, the vehicle makes a move by using the local characteristics of the Gibbs distribution. Convergence properties are established theoretically and confirmed with simulations. In order to reduce the communication cost and the delay, a fully parallel sampling algorithm is studied and analyzed accordingly. In practice, the stochastic nature of the proposed algorithm might lead to a high traveling cost. To mitigate this problem, a hybrid algorithm is eveloped by combining the Gibbs sampler based method with the deterministic gradient-flow method to gain the advantages of both approaches. The robustness of the Gibbs sampler based algorithm is also studied. The convergence properties are investigated for different types sensor errors including range-error and random-error. Error bounds are derived to guarantee the convergence of the stochastic algorithm. In the low-level motion control module, a model predictive control (MPC) approach is investigated for car-like UAV model. Multiple control objectives, for example, minimizing tracking error, avoiding actuator/state saturation, and minimizing control effort, are easily encoded in the objective function. Two numerical optimization approaches, gradient descendent approach and dynamic programming approach, are studied to strike the balance between computation time and complexity.