1,720,988 research outputs found
Identifiability Analysis of Planar Rigid-Body Frictional Contact
No full textThis paper addresses the identifiability of the inertial parameters and the contact forces associated with an object making and breaking frictional contact with the environment. Our goal is to explore under what conditions, and to what degree, the observation of physical interaction, in the form of motions and/or forces, is indicative of the underlying dynamics that governs it. In this initial study we consider the cases of passive interaction, where an object free-falls under gravity, and active interaction, where known external perturbations act on the object at contact. We assume that both object and environment are planar and rigid, and exploit the well-known complementarity formulation for contact resolution to establish a closed-form relationship between inertial parameters, contact forces, and observed motions. Consistent with intuition, the analysis indicates that without the application of known external forces, the identifiable set of parameters remains coupled, i.e., the ratio of mass moment of inertia to mass and the ratio of contact forces to the mass. Interestingly, the analysis also shows that known external forces can lead to decoupling and identifiability of mass, mass moment of inertia, and normal and tangential contact forces. We evaluate the identifiability formulation both in simulation and with real experiments
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Planning, Control, and Estimation for Diverse Multi-UAS Missions
No full textUnmanned Aircraft Systems (UAS) are being used for a variety of single vehicle missions such as surveillance, inspection, and payload delivery. Teams of UAS can perform these same missions more efficiently and can pursue novel cooperative missions not possible with a single vehicle. However, this comes at the cost of increased system complexity and introduces the challenge of safe team coordination. This motivates our research to pursue four diverse multi-UAS mission configurations. We propose novel methods to command and control teams of UAS with the majority supported with full-scale experimental validation.
To support all experiments conducted in this thesis, an experimental test bed consisting of a custom, open-source quadrotor, flight controller, and supporting infrastructure is developed with designs and code shared publicly.
This thesis offers four specific contributions to enable the deployment of UAS teams in missions with potential to benefit society. First, an existing formation control method, continuum deformation, is experimentally validated with observed tracking errors and delays informing the theory. Required inter-vehicle separation constraints are defined and applied to the real system. A global minimum separation bound based on a local controller error bound is derived to guarantee safety during real-world flights.
Second, a novel heads-up haptic pushing interface is developed that enables a user to move a heavy payload carried by multiple small UAS through a crowded cluttered environment. Real-time estimation of user applied force via an instrumented payload updates virtual dynamics of an admittance controller to guide the system. This capability will support resilient and safe package delivery to untrained consumers and can assist in delivering medical and survival supplies in disaster relief scenarios.
Third, computationally efficient planning methods are developed to support wildfire mapping over a large area by a team of UAS. A state machine is used to handle multi-vehicle task allocation between exploration (coverage) and exploitation (line following). Efficiency gains are achieved by separating the 3D problem into 2D lateral and 1D terrain avoidance sub-problems. This work offers a first step towards mapping increasingly severe wildfire threats that cause significant damage and claim the lives of hundreds of people every year.
Fourth, a generalized path planner is developed to manage a deformable small UAS formation capable of rotating, expanding, contracting, and shearing. Provably sufficient inter-agent collision avoidance constraints are leveraged to efficiently plan safe trajectories in large-scale complex but static environments. An integrated guidance and control module onboard each small UAS tracks the designed trajectory while avoiding pop-up obstacles and vehicle failures by following an ideal fluid flow field airspace template.
We are only beginning to imagine and prototype the missions made possible with teams of UAS. This thesis provides problem formulations, solution methods, and experimental realizations for four multi-UAS mission configurations. Countless other missions can be pursued that will operate safely and can improve quality of life.
Continued research is needed to achieve this including full system integration with onboard sensing and collaboration with key stakeholders for each mission that deeply understand the problem spaces. Nonetheless, this thesis provides a solid foundation for future researchers to build upon.PhDRoboticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/174372/1/mmroma_1.pd
Learning and Inference for Adaptable Manipulation Planning
No full textA central challenge for developing general-purpose robot assistants is the development of algorithms for robot manipulation that can perform a wide range of tasks across a diverse set of environments. In this thesis, I develop planning and trajectory optimization methods that can adapt to new and unforeseen systems. The key to these methods is the ability of robots to learn from experience and reason about related uncertainty. Using modern machine learning and approximate probabilistic inference techniques, the work I present in this thesis improves the ability of planning methods to do so.
Probabilistic inference is useful in two ways. First, by using a probabilistic framing, probabilities can be used as a way of expressing confidence in our current models. I develop a method that learns to predict the uncertainty of a given dynamics model with a small amount of data collected online and avoids areas where the model is uncertain. I also propose an approach that learns a generative model of control sequences to complete a given task. I demonstrate that we can detect and adapt this generative model to situations where the environment differs from the training environments.
Second, I incorporate probabilistic inference into the proposed methods by viewing planning itself as an inference problem. By framing planning as inference, we construct probability distributions over trajectories. This framework allows me to develop a method that views constrained trajectory optimization as inference, generating diverse sets of constraint-satisfying trajectories for completing manipulation tasks. This allows improved adaptation to online disturbances, since at any given time, there is a set of trajectories to select from. I demonstrate the effectiveness of this method on several different tasks, including a 7DoF manipulator turning a wrench and a 16DoF multi-fingered hand turning a precision screwdriver.
The methods I present in this thesis contribute to the development of adaptable algorithms for robotic manipulation for the next generation of general-purpose robot assistants.PhDRoboticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/194744/1/tpower_1.pd
Toward Scalable Spatial Intelligence for Embodied AI: Language Grounding and 3D Reconstruction
No full textThe long-term promise of embodied AI is a robot that can see, talk, and act in human environments. Realizing this promise hinges on two capabilities that connect perception and language at scale: 3D reconstruction, which builds a coherent spatial model of the world from images, and 3D–text grounding, which links natural language to specific objects, attributes, and relations in that model. This dissertation argues that the key missing ingredient for robust, everyday performance is scaling: scaling the semantics of how language is used, scaling the data that supervises grounding, and scaling the algorithms that reconstruct and update 3D state.
First, we introduce LLM-Grounder, which treats a large language model as an agentic planner and evaluator for open–vocabulary 3D–text grounding. By decomposing complex, relational instructions and arbitrating among 3D candidates with spatial and commonsense reasoning, LLM-Grounder improves zero-shot grounding on long, compositional queries without additional task-specific labels.
Second, to address the scarcity of dense supervision, we contribute 3D-GRAND, a million-scale resource with 6.2M noun-level, densely grounded 3D–text pairs across approximately 40K household scenes. We further release 3D-POPE, a benchmark for probing object hallucination in grounded 3D language, facilitating systematic evaluation of whether models respect 3D evidence rather than language priors. Together, these resources enable instruction tuning that more closely matches the richness of natural household dialogue and reveal clear positive scaling trends with data density and breadth.
Third, we develop fast and scalable 3D reconstruction. Fast3R performs single-pass multi-view reconstruction, yielding over 300× speedups compared to pairwise + global–alignment pipelines while maintaining strong pose and geometry accuracy. We then extend reconstruction to thousand–view regimes with Fast3R-v2, which introduces (i) register fusion—summarizing per–view patch tokens into a compact set of learned registers to make global fusion far more efficient—and (ii) sequence parallelism, which partitions long multi-view sequences across multiple GPUs so that model capacity and throughput scale with available hardware.
Across grounding and reconstruction, this thesis substantiates a scaling–centric view: when semantics (agentic reasoning), data (dense 3D–language supervision), and algorithms (architectures that exploit parallel hardware) are scaled together, embodied systems become markedly better at understanding and communicating about the 3D world. We discuss how these ingredients also position future extensions in dynamic (4D) reconstruction and world–model pretraining, moving toward unified and scalable spatial intelligence beyond the lab.PhDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studie
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