Ultra high frequency (UHF) radio-frequency identification (RFID) for robot perception and mobile manipulation

Personal robots with autonomy, mobility, and manipulation capabilities have the potential to dramatically improve quality of life for various user populations, such as older adults and individuals with motor impairments. Unfortunately, unstructured environments present many challenges that hinder robot deployment in ordinary homes. This thesis seeks to address some of these challenges through a new robotic sensing modality that leverages a small amount of environmental augmentation in the form of Ultra High Frequency (UHF) Radio-Frequency Identification (RFID) tags. Previous research has demonstrated the utility of infrastructure tags (affixed to walls) for robot localization; in this thesis, we specifically focus on tagging objects. Owing to their low-cost and passive (battery-free) operation, users can apply UHF RFID tags to hundreds of objects throughout their homes. The tags provide two valuable properties for robots: a unique identifier and receive signal strength indicator (RSSI, the strength of a tag's response). This thesis explores robot behaviors and radio frequency perception techniques using robot-mounted UHF RFID readers that enable a robot to efficiently discover, locate, and interact with UHF RFID tags applied to objects and people of interest. The behaviors and algorithms explicitly rely on the robot's mobility and manipulation capabilities to provide multiple opportunistic views of the complex electromagnetic landscape inside a home environment. The electromagnetic properties of RFID tags change when applied to common household objects. Objects can have varied material properties, can be placed in diverse orientations, and be relocated to completely new environments. We present a new class of optimization-based techniques for RFID sensing that are robust to the variation in tag performance caused by these complexities. We discuss a hybrid global-local search algorithm where a robot employing long-range directional antennas searches for tagged objects by maximizing expected RSSI measurements; that is, the robot attempts to position itself (1) near a desired tagged object and (2) oriented towards it. The robot first performs a sparse, global RFID search to locate a pose in the neighborhood of the tagged object, followed by a series of local search behaviors (bearing estimation and RFID servoing) to refine the robot's state within the local basin of attraction. We report on RFID search experiments performed in Georgia Tech's Aware Home (a real home). Our optimization-based approach yields superior performance compared to state of the art tag localization algorithms, does not require RF sensor models, is easy to implement, and generalizes to other short-range RFID sensor systems embedded in a robot's end effector. We demonstrate proof of concept applications, such as medication delivery and multi-sensor fusion, using these techniques. Through our experimental results, we show that UHF RFID is a complementary sensing modality that can assist robots in unstructured human environments.PhDCommittee Chair: Kemp, Charles C.; Committee Member: Abowd, Gregory; Committee Member: Howard, Ayanna; Committee Member: Ingram, Mary Ann; Committee Member: Reynolds, Matt; Committee Member: Tentzeris, Emmanoui

Biochemical properties of Paracoccus denitrificans FnrP:Reactions with molecular oxygen and nitric oxide

In Paracoccus denitrificans, three CRP/FNR family regulatory proteins, NarR, NnrR and FnrP, control the switch between aerobic and anaerobic (denitrification) respiration. FnrP is a [4Fe-4S] cluster containing homologue of the archetypal O2 sensor FNR from E. coli and accordingly regulates genes encoding aerobic and anaerobic respiratory enzymes in response to O2, and also NO, availability. Here we show that FnrP undergoes O2-driven [4Fe-4S] to [2Fe-2S] cluster conversion that involves up to 2 O2 per cluster, with significant oxidation of released cluster sulfide to sulfane observed at higher O2 concentrations. The rate of the cluster reaction was found to be ~6-fold lower than that of E. coli FNR, suggesting that FnrP can remain transcriptionally active under microaerobic conditions. This is consistent with a role for FnrP in activating expression of the high O2 affinity cytochrome c oxidase under microaerobic conditions. Cluster conversion resulted in dissociation of the transcriptionally active FnrP dimer into monomers. Therefore, along with E. coli FNR, FnrP belongs to the subset of FNR proteins in which cluster type is correlated with association state. Interestingly, two key charged residues, Arg140 and Asp154, that have been shown to play key roles in the monomer-dimer equilibrium in E. coli FNR are not conserved in FnrP, indicating that different protomer interactions are important for this equilibrium. Finally, the FnrP [4Fe-4S] cluster is shown to undergo reaction with multiple NO molecules, resulting in iron nitrosyl species and dissociation into monomers

TEDDI: Tamper Event Detection on Distributed Cyber-Physical Systems

Edge devices, or embedded devices installed along the periphery of a power grid SCADA network, pose a significant threat to the grid, as they give attackers a convenient entry point to access and cause damage to other essential equipment in substations and control centers. Grid defenders would like to protect these edge devices from being accessed and tampered with, but they are hindered by the grid defender\u27s dilemma; more specifically, the range and nature of tamper events faced by the grid (particularly distributed events), the prioritization of grid availability, the high costs of improper responses, and the resource constraints of both grid networks and the defenders that run them makes prior work in the tamper and intrusion protection fields infeasible to apply. In this thesis, we give a detailed description of the grid defender\u27s dilemma, and introduce TEDDI (Tamper Event Detection on Distributed Infrastructure), a distributed, sensor-based tamper protection system built to solve this dilemma. TEDDI\u27s distributed architecture and use of a factor graph fusion algorithm gives grid defenders the power to detect and differentiate between tamper events, and also gives defenders the flexibility to tailor specific responses for each event. We also propose the TEDDI Generation Tool, which allows us to capture the defender\u27s intuition about tamper events, and assists defenders in constructing a custom TEDDI system for their network. To evaluate TEDDI, we collected and constructed twelve different tamper scenarios, and show how TEDDI can detect all of these events and solve the grid defender\u27s dilemma. In our experiments, TEDDI demonstrated an event detection accuracy level of over 99% at both the information and decision point levels, and could process a 99-node factor graph in under 233 microseconds. We also analyzed the time and resources needed to use TEDDI, and show how it requires less up-front configuration effort than current tamper protection solutions

Accurate vehicle classification including motorcycles using piezoelectric sensors

Thesis (M.S. ECE)--University of Oklahoma, 2012.Includes bibliographical references (leaves 88-90).State and federal departments of transportation are charged with classifying vehicles and monitoring mileage traveled. Accurate data reporting enables suitable roadway design for safety and capacity. Vehicle classifier devices currently employ inductive loops, piezoelectric sensors, or some combination of both, to aid in the identification of 13 Federal Highway Administration (FHWA) classifications. However, systems using inductive loops have proven unable to accurately classify motorcycles and record pertinent data. Previous investigations undertaken to overcome this problem have focused on classification techniques utilizing inductive loops signal output, magnetic sensor output with neural networks, or the fusion of several sensor outputs. Most were off-line classification studies with results not directly intended for product development. Vision, infrared, and acoustic classification systems among others have also been explored as possible solutions. This thesis presents a novel vehicle classification setup that uses a single piezoelectric sensor placed diagonally on the roadway to accurately identify motorcycles from among other vehicles, as well as identify vehicles in the remaining 12 FHWA classifications. An algorithm was formulated and deployed in an embedded system for field testing. Both single element and multi-element piezoelectric sensors were investigated for use as part of the vehicle classification system. The piezoelectric sensors and vehicle classification system reported in this thesis were subsequently tested at the University of Oklahoma-Tulsa campus. Various vehicle types traveling at limited vehicle speeds were investigated. The newly developed vehicle classification system demonstrated results that met expectation for accurately identifying motorcycles

Taming and Leveraging Directionality and Blockage in Millimeter Wave Communications

To cope with the challenge for high-rate data transmission, Millimeter Wave(mmWave) is one potential solution. The short wavelength unlatched the era of directional mobile communication. The semi-optical communication requires revolutionary thinking. To assist the research and evaluate various algorithms, we build a motion-sensitive mmWave testbed with two degrees of freedom for environmental sensing and general wireless communication.The first part of this thesis contains two approaches to maintain the connection in mmWave mobile communication. The first one seeks to solve the beam tracking problem using motion sensor within the mobile device. A tracking algorithm is given and integrated into the tracking protocol. Detailed experiments and numerical simulations compared several compensation schemes with optical benchmark and demonstrated the efficiency of overhead reduction. The second strategy attempts to mitigate intermittent connections during roaming is multi-connectivity. Taking advantage of properties of rateless erasure code, a fountain code type multi-connectivity mechanism is proposed to increase the link reliability with simplified backhaul mechanism. The simulation demonstrates the efficiency and robustness of our system design with a multi-link channel record.The second topic in this thesis explores various techniques in blockage mitigation. A fast hear-beat like channel with heavy blockage loss is identified in the mmWave Unmanned Aerial Vehicle (UAV) communication experiment due to the propeller blockage. These blockage patterns are detected through Holm\u27s procedure as a problem of multi-time series edge detection. To reduce the blockage effect, an adaptive modulation and coding scheme is designed. The simulation results show that it could greatly improve the throughput given appropriately predicted patterns. The last but not the least, the blockage of directional communication also appears as a blessing because the geometrical information and blockage event of ancillary signal paths can be utilized to predict the blockage timing for the current transmission path. A geometrical model and prediction algorithm are derived to resolve the blockage time and initiate active handovers. An experiment provides solid proof of multi-paths properties and the numeral simulation demonstrates the efficiency of the proposed algorithm

Development of the UMAC-based control system with application to 5-axis ultraprecision micromilling machines

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Increasing demands from end users in the fields of optics, defence, automotive, medical, aerospace, etc. for high precision 3D miniaturized components and microstructures from a range of materials have driven the development in micro and nano machining and changed the manufacturing realm. Conventional manufacturing processes such as chemical etching and LIGA are found unfavourable or limited due to production time required and have led mechanical micro machining to grow further. Mechanical micro machining is an ideal method to produce high accuracy micro components and micro milling is the most flexible enabling process and is thus able to generate a wider variety of complex micro components and microstructures. Ultraprecision micromilling machine tools are required so as to meet the accuracy, surface finish and geometrical complexity of components and parts. Typical manufacturing requirements are high dimensional accuracy being better than 1 micron, flatness and roundness better than 50 nm and surface finish ranging between 10 and 50 nm. Manufacture of high precision components and parts require very intricate material removal procedure. There are five key components that include machine tools, cutting tools, material properties, operation variables and environmental conditions, which constitute in manufacturing high quality components and parts. End users assess the performance of a machine tool based on the dimensional accuracy and surface quality of machined parts including the machining time. In this thesis, the emphasis is on the design and development of a control system for a 5-axis bench-type ultraprecision micromilling machine- Ultra-Mill. On the one hand, the developed control system is able to offer high motion and positioning accuracy, dynamic stiffness and thermal stability for motion control, which are essential for achieving the machining accuracy and surface finish desired. On the other hand, the control system is able to undertake in-process inspection and condition monitoring of the machine tool and process. The control of multi-axis precision machines with high-speed and high-accuracy motions and positioning are desirable to manufacture components with high accuracy and complex features to increase productivity and maintain machine stability, etc. The development of the control system has focused on fast, accurate and robust positioning requirements at the machine system design stage. Apart from the mechanical design, the performance of the entire precision systems is greatly dependent on diverse electrical and electronics subsystems, controllers, drive instruments, feedback devices, inspection and monitoring system and software. There are some variables that dynamically alter the system behaviour and sensitivity to disturbance that are not ignorable in the micro and nano machining realm. In this research, a structured framework has been developed and integrated to aid the design and development of the control system. The framework includes critically reviewing the state of the art of ultraprecision machining tools, understanding the control system technologies involved, highlighting the advantages and disadvantages of various control system methods for ultraprecision machines, understanding what is required by end-users and formulating what actually makes a machine tool be an ultraprecision machine particularly from the control system perspective. In the design and development stage, the possession of mechatronic know-how is essential as the design and development of the Ultra-Mill is a multidisciplinary field. Simulation and modelling tool such as Matlab/Simulink is used to model the most suitable control system design. The developed control system was validated through machining trials to observe the achievable accuracy, experiments and testing of subsystems individually (slide system, tooling system, monitoring system, etc.). This thesis has successfully demonstrated the design and development of the control system for a 5-axis ultraprecision machine tool- Ultra-Mill, with high performance characteristics, fast, accurate, precise, etc. for motion and positioning, high dynamic stiffness, robustness and thermal stability, whereby was provided and maintained by the control system

CALVIN: A Benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Tasks

General-purpose robots coexisting with humans in their environment must learn to relate human language to their perceptions and actions to be useful in a range of daily tasks. Moreover, they need to acquire a diverse repertoire of general-purpose skills that allow composing long-horizon tasks by following unconstrained language instructions. In this paper, we present CALVIN (Composing Actions from Language and Vision), an open-source simulated benchmark to learn long-horizon language-conditioned tasks. Our aim is to make it possible to develop agents that can solve many robotic manipulation tasks over a long horizon, from onboard sensors, and specified only via human language. CALVIN tasks are more complex in terms of sequence length, action space, and language than existing vision-and-language task datasets and supports flexible specification of sensor suites. We evaluate the agents in zero-shot to novel language instructions and to novel environments and objects. We show that a baseline model based on multi-context imitation learning performs poorly on CALVIN, suggesting that there is significant room for developing innovative agents that learn to relate human language to their world models with this benchmark.Comment: Accepted for publication at IEEE Robotics and Automation Letters (RAL). Code, models and dataset available at http://calvin.cs.uni-freiburg.d

Dynamic Scene Reconstruction and Understanding

Traditional approaches to 3D reconstruction have achieved remarkable progress in static scene acquisition. The acquired data serves as priors or benchmarks for many vision and graphics tasks, such as object detection and robotic navigation. Thus, obtaining interpretable and editable representations from a raw monocular RGB-D video sequence is an outstanding goal in scene understanding. However, acquiring an interpretable representation becomes significantly more challenging when a scene contains dynamic activities; for example, a moving camera, rigid object movement, and non-rigid motions. These dynamic scene elements introduce a scene factorization problem, i.e., dividing a scene into elements and jointly estimating elements’ motion and geometry. Moreover, the monocular setting brings in the problems of tracking and fusing partially occluded objects as they are scanned from one viewpoint at a time. This thesis explores several ideas for acquiring an interpretable model in dynamic environments. Firstly, we utilize synthetic assets such as floor plans and object meshes to generate dynamic data for training and evaluation. Then, we explore the idea of learning geometry priors with an instance segmentation module, which predicts the location and grouping of indoor objects. We use the learned geometry priors to infer the occluded object geometry for tracking and reconstruction. While instance segmentation modules usually have a generalization issue, i.e., struggling to handle unknown objects, we observed that the empty space information in the background geometry is more reliable for detecting moving objects. Thus, we proposed a segmentation-by-reconstruction strategy for acquiring rigidly-moving objects and backgrounds. Finally, we present a novel neural representation to learn a factorized scene representation, reconstructing every dynamic element. The proposed model supports both rigid and non-rigid motions without pre-trained templates. We demonstrate that our systems and representation improve the reconstruction quality on synthetic test sets and real-world scans