FLAT2D: Fast localization from approximate transformation into 2D

Many autonomous vehicles require precise localization into a prior map in order to support planning and to leverage semantic information within those maps (e.g. that the right lane is a turn-only lane.) A popular approach in automotive systems is to use infrared intensity maps of the ground surface to localize, making them susceptible to failures when the surface is obscured by snow or when the road is repainted. An emerging alternative is to localize based on the 3D structure around the vehicle; these methods are robust to these types of changes, but the maps are costly both in terms of storage and the computational cost of matching. In this paper, we propose a fast method for localizing based on 3D structure around the vehicle using a 2D representation. This representation retains many of the advantages of "full" matching in 3D, but comes with dramatically lower space and computational requirements. We also introduce a variation of Graph-SLAM tailored to support localization, allowing us to make use of graph-based error-recovery techniques in our localization estimate. Finally, we present real-world localization results for both an indoor mobile robotic platform and an autonomous golf cart, demonstrating that autonomous vehicles do not need full 3D matching to accurately localize in the environment

Policy-Based Planning for Robust Robot Navigation

This thesis proposes techniques for constructing and implementing an extensible navigation framework suitable for operating alongside or in place of traditional navigation systems. Robot navigation is only possible when many subsystems work in tandem such as localization and mapping, motion planning, control, and object tracking. Errors in any one of these subsystems can result in the robot failing to accomplish its task, oftentimes requiring human interventions that diminish the benefits theoretically provided by autonomous robotic systems. Our first contribution is Direction Approximation through Random Trials (DART), a method for generating human-followable navigation instructions optimized for followability instead of traditional metrics such as path length. We show how this strategy can be extended to robot navigation planning, allowing the robot to compute the sequence of control policies and switching conditions maximizing the likelihood with which the robot will reach its goal. This technique allows robots to select plans based on reliability in addition to efficiency, avoiding error-prone actions or areas of the environment. We also show how DART can be used to build compact, topological maps of its environments, offering opportunities to scale to larger environments. DART depends on the existence of a set of behaviors and switching conditions describing ways the robot can move through an environment. In the remainder of this thesis, we present methods for learning these behaviors and conditions in indoor environments. To support landmark-based navigation, we show how to train a Convolutional Neural Network (CNN) to distinguish between semantically labeled 2D occupancy grids generated from LIDAR data. By providing the robot the ability to recognize specific classes of places based on human labels, not only do we support transitioning between control laws, but also provide hooks for human-aided instruction and direction. Additionally, we suggest a subset of behaviors that provide DART with a sufficient set of actions to navigate in most indoor environments and introduce a method to learn these behaviors from teleloperated demonstrations. Our method learns a cost function suitable for integration into gradient-based control schemes. This enables the robot to execute behaviors in the absence of global knowledge. We present results demonstrating these behaviors working in several environments with varied structure, indicating that they generalize well to new environments. This work was motivated by the weaknesses and brittleness of many state-of-the-art navigation systems. Reliable navigation is the foundation of any mobile robotic system. It provides access to larger work spaces and enables a wide variety of tasks. Even though navigation systems have continued to improve, catastrophic failures can still occur (e.g. due to an incorrect loop closure) that limit their reliability. Furthermore, as work areas approach the scale of kilometers, constructing and operating on precise localization maps becomes expensive. These limitations prevent large scale deployments of robots outside of controlled settings and laboratory environments. The work presented in this thesis is intended to augment or replace traditional navigation systems to mitigate concerns about scalability and reliability by considering the effects of navigation failures for particular actions. By considering these effects when evaluating the actions to take, our framework can adapt navigation strategies to best take advantage of the capabilities of the robot in a given environment. A natural output of our framework is a topological network of actions and switching conditions, providing compact representations of work areas suitable for fast, scalable planning.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144073/1/rgoeddel_1.pd

A Literacy Photostory: Improving Classroom Literacy Instruction

In rapidly changing times, students need to understand and utilize various types of literacies in order to be productive citizens and communicate with each other in a fast-paced world. Literacy is often simplified to the ability to read and write, only on some occasions including other topics such as speaking and listening skills, or computer literacies. The need for improved literacy instruction is apparent in high schools, and this study seeks to inform educators of different needs and hopes that students may have in the broad field of literacy. This study seeks to expand the definition of literacy while simultaneously starting a conversation about ways to better incorporate literacy instruction into schools

Casper Is a FADD- and Caspase-Related Inducer of Apoptosis

AbstractCaspases are cysteine proteases that play a central role in apoptosis. Caspase-8 may be the first enzyme of the proteolytic cascade activated by the Fas ligand and tumor necrosis factor (TNF). Caspase-8 is recruited to Fas and TNF receptor-1 (TNF-R1) through interaction of its prodomain with the death effector domain (DED) of the receptor-associating FADD. Here we describe a novel 55 kDa protein, Casper, that has sequence similarity to caspase-8 throughout its length. However, Casper is not a caspase since it lacks several conserved amino acids found in all caspases. Casper interacts with FADD, caspase-8, caspase-3, TRAF1, and TRAF2 through distinct domains. When overexpressed in mammalian cells, Casper potently induces apoptosis. A C-terminal deletion mutant of Casper inhibits TNF- and Fas-induced cell death, suggesting that Casper is involved in these apoptotic pathways

An Investigation of the Manganese Indium System

A preliminary phase-equilibria study has been made of the manganese-indium alloy system by means of thermal analysis (cooling curves) and metallographic studies (photomicrographs). Ferromagnetism has been found to exist in alloys containing up to 70 atom percent manganese and has tentatively been attributed to the compound Mn2In. A phase diagram has been proposed, on the basis of the data obtained, in which the indium rich portion of the system consists of indium plus Mn2In, while the manganese rich portion is made up of a manganese solid solution. Additional experiments have been outlined to further identify the nature and extent of the phases present. A new scheme of chemical analysis has been applied to the alloys with good success

Fused Fluoride--Inconel System Under Cyclotron Irradiation--Preliminary Results

The fused fluoride-Inconel reactor system was studied under irradiation with nominal 19 Mev deuterons as supplied by the Berkeley 60-inch cyclotron. Chemical, metallographic, magnetic susceptibility, electron diffraction and X-ray diffraction studies were made on the as-received materials, one control run and two irradiated runs. No changes in the fused fluoride fuel were noted. Accelerated intergranular corrosion and increased grain size were observed in the irradiated Inconel specimens. This report is based upon studies conducted for the Atomic Energy Commission under Contract AT-40-1-GEN-1064

Perioperative Acute Kidney Injury

Acute kidney injury (AKI) is one of the important complications of the perioperative period, and associated with increased risk of chronic kidney disease, renal replacement therapy requirements, increased cost, and risk of mortality. In this overview, we summarized baseline confounders and surgical procedure related risk factors contributing to the perioperative AKI, which may serve as risk scores, improve early diagnosis, contribute to prevention, and early management of AKI. There are immediate needs for context specific clinical prediction scores and novel biomarkers to very early diagnose AKI. Preventive guidance provided by Kidney Disease: Improving Global Outcomes is a helpful practical tool for clinicians. Potential roles of novel biomarkers and their context specific contributions require further exploration and close attention. Perioperative hemodynamics and oxygenation appear to contribute to AKI. Therefore, while their optimization can be recommended, their detailed and context specific roles need further explored. Overall, decreased exposure to nephrotoxic agents is recommended to further decrease the impact of perioperative AKI