42 research outputs found
FLAT2D: Fast localization from approximate transformation into 2D
No full textMany 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
No full textThis 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
Miniaturized and Thermal‐Based Measurement System to Measure Moisture in Textile Materials
No full textMoisture in textile materials worn close to the skin greatly influences our daily comfort. The measurement of moisture in textile materials is therefore of great interest, for example, to determine the amount of perspiration in clothing or car seats, the wound fluid in dressings, or even the urine in diapers or bed linen. All these applications require a robust moisture measurement method, which is harmless to humans and measures in thin layers. One method ideally suited to fit these requirements is the transient-heat moisture sensing (THMS) method. Herein, a miniaturized and evolved adaption of the THMS method is shown. The measurement system presented herein is optimized for low energy consumption and portability. The working principle of this measuring system is demonstrated by conducting a simple test to investigate the transplanar wicking of eight fundamentally different but garment-typical textiles. The THMS method and its ability to measure in thin layers that is ideally suited to measure moisture in thin layers are shown. Finally, it lays a foundation to enable a multitude of future applications, wherever moisture (e.g., sweat) is to be measured with high accuracy and with a wearable system close to the human skin.This work was financially supported by the Adidas AG and was performed in terms of cooperative doctorate graduation supported by the project "Meeting Point Functional Layers" (MPFL) at the University of Applied Sciences Kaiserslautern. The MPFL is part of the DAAD supporting program of "Strategic Partnerships and Thematic Networks" (project no. 57172293).Picard, A (reprint author), Univ Appl Sci Kaiserslautern, Dept Informat & Micro Syst Technol, Amerikastr 1, D-66482 Zweibrucken, Germany.
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Cylinder performance of locomotive Schenectaday no. 2 at various percentages of cut-offs
No full textTypescriptThesis (B.S.)--Purdue University,Mechanical EngineeringB.S
Vascular endothelial growth factor restores delayed tumor progression in tumors depleted of macrophages
No full textGenetic depletion of macrophages in Polyoma Middle T oncoprotein (PyMT)-induced mammary tumors in mice delayed the angiogenic switch and the progression to malignancy. To determine whether vascular endothelial growth factor A (VEGF-A) produced by tumor-associated macrophages regulated the onset of the angiogenic switch, a genetic approach was used to restore expression of VEGF-A into tumors at the benign stages. This stimulated formation of a high-density vessel network and in macrophage-depleted mice, was followed by accelerated tumor progression. The expression of VEGF-A led to a massive infiltration into the tumor of leukocytes that were mostly macrophages. This study suggests that macrophage-produced VEGF regulates malignant progression through stimulating tumor angiogenesis, leukocytic infiltration and tumor cell invasion
