Matching Items (41)
Description
Robotic swarms can potentially perform complicated tasks such as exploration and mapping at large space and time scales in a parallel and robust fashion. This thesis presents strategies for mapping environmental features of interest – specifically obstacles, collision-free paths, generating a metric map and estimating scalar density fields– in an unknown domain using data obtained by a swarm of resource-constrained robots. First, an approach was developed for mapping a single obstacle using a swarm of point-mass robots with both directed and random motion. The swarm population dynamics are modeled by a set of advection-diffusion-reaction partial differential equations (PDEs) in which a spatially-dependent indicator function marks the presence or absence of the obstacle in the domain. The indicator function is estimated by solving an optimization problem with PDEs as constraints. Second, a methodology for constructing a topological map of an unknown environment was proposed, which indicates collision-free paths for navigation, from data collected by a swarm of finite-sized robots. As an initial step, the number of topological features in the domain was quantified by applying tools from algebraic topology, to a probability function over the explored region that indicates the presence of obstacles. A topological map of the domain is then generated using a graph-based wave propagation algorithm. This approach is further extended, enabling the technique to construct a metric map of an unknown domain with obstacles using uncertain position data collected by a swarm of resource-constrained robots, filtered using intensity measurements of an external signal. Next, a distributed method was developed to construct the occupancy grid map of an unknown environment using a swarm of inexpensive robots or mobile sensors with limited communication. In addition to this, an exploration strategy which combines information theoretic ideas with Levy walks was also proposed. Finally, the problem of reconstructing a two-dimensional scalar field using observations from a subset of a sensor network in which each node communicates its local measurements to its neighboring nodes was addressed. This problem reduces to estimating the initial condition of a large interconnected system with first-order linear dynamics, which can be solved as an optimization problem.
ContributorsRamachandran, Ragesh Kumar (Author) / Berman, Spring M (Thesis advisor) / Mignolet, Marc (Committee member) / Artemiadis, Panagiotis (Committee member) / Marvi, Hamid (Committee member) / Robinson, Michael (Committee member) / Arizona State University (Publisher)
Created2018
Description
This thesis details our experience assisting BASE Equity Partners, a private equity firm based in New York City, on three prospective agricultural dealership deals over the course of this past academic year. The firm is currently structured as a Fundless Sponsor. This distinct structural trait is common for a type of private equity firm known among practitioners as pledge funds. This creates an interesting element for our experience as there is very limited academic research on these types of firms, which, since the Great Recession, have become popular players in middle-market private equity deals. We, first, provide some historical context on pledge funds and identify their primary differences with traditional private equity. The remainder of the paper documents our experience working on the agricultural dealership deals. We have organized this portion after the manner in which we received assignments. We go into detail on the specific projects with which we were tasked, our interactions with the partners and the major takeaways we had from this learning experience. This thesis paper will enrich the academic knowledge regarding pledge funds—and private equity generally—by documenting a real experience of what it is like performing analyst-level tasks at a real firm. Additionally, we were privy to information that is highly confidential, and though we have protected the confidentiality of the companies through pseudonyms and redaction of confidential material, all of the financial data shown, models provided and qualitative discussion is real.
ContributorsTang, Ivan (Co-author) / Johnson, Bradley (Co-author) / Panosian, Tro (Co-author) / Simonson, Mark (Thesis director) / Bonadurer, Werner (Committee member) / Barrett, The Honors College (Contributor) / Department of Finance (Contributor) / Department of English (Contributor) / School of Accountancy (Contributor) / School of International Letters and Cultures (Contributor)
Created2015-05
ContributorsYu, Wan-Ting (Performer) / Baik, Elijah (Performer) / Robinson, Michael (Performer) / ASU Library. Music Library (Publisher)
Created2021-03-27
ContributorsGuo, Hongzuo (Performer) / Jin, Leon (Performer) / Robinson, Michael (Performer) / Liu, Miao (Performer) / Liu, Sicong (Performer) / ASU Library. Music Library (Publisher)
Created2021-03-03
ContributorsRobinson, Michael (Performer) / Yu, Wan-Ting (Performer) / Baik, Elijah (Performer) / ASU Library. Music Library (Publisher)
Created2021-04-14
ContributorsJin, Leon (Performer) / Robinson, Michael (Performer) / Guo, Hongzuo (Performer) / ASU Library. Music Library (Publisher)
Created2021-02-26
ContributorsArch, Nathan (Performer) / Zelenak, Kristen (Performer) / Weiss, Stephanie (Performer) / Robinson, Michael (Performer) / ASU Library. Music Library (Publisher)
Created2021-10-16
ContributorsMacdonald, Ben (Performer) / Creviston, Hannah (Performer) / Maes, Kelsey (Performer) / Robinson, Michael (Performer) / Johnson, Bradley (Performer) / Harden, John (Performer) / Solaire Reed Quintet (Performer) / ASU Library. Music Library (Publisher)
Created2022-04-23
ContributorsJin, Leon (Performer) / Guo, Hongzuo (Performer) / Robinson, Michael (Editor) / Liu, Miao (Performer) / Hernandez, Heidi (Performer) / Espinoza, Diego (Performer) / Coffey, Brennan (Performer) / Ulm, Jeremy (Performer) / Solari, John (Performer) / ASU Library. Music Library (Publisher)
Created2022-04-02
ContributorsSanders, Thomas (Performer) / Harden, John (Performer) / Novak, Gail (Pianist) (Performer) / Fox, Ethan (Performer) / Miller, Dahre (Performer) / Robinson, Michael (Performer) / *AudibleNoise* quartet (Performer)
Created2022-04-16