Browsing by Author "Zhou, Yu"

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    Art and Engineering Inspired by Swarm Robotics
    (2017-04-13) Zhou, Yu; Goldman, Ronald
    Swarm robotics has the potential to combine the power of the hive with the sensibility of the individual to solve non-traditional problems in mechanical, industrial, and architectural engineering and to develop exquisite art beyond the ken of most contemporary painters, sculptors, and architects. The goal of this thesis is to apply swarm robotics to the sublime and the quotidian to achieve this synergy between art and engineering. The potential applications of collective behaviors, manipulation, and self-assembly are quite extensive. We will concentrate our research on three topics: fractals, stability analysis, and building an enhanced multi-robot simulator. Self-assembly of swarm robots into fractal shapes can be used both for artistic purposes (fractal sculptures) and in engineering applications (fractal antennas). Stability analysis studies whether distributed swarm algorithms are stable and robust either to sensing or to numerical errors, and tries to provide solutions to avoid unstable robot configurations. Our enhanced multi-robot simulator supports this research by providing real-time simulations with customized parameters, and can become as well a platform for educating a new generation of artists and engineers. The goal of this thesis is to use techniques inspired by swarm robotics to develop a computational framework accessible to and suitable for both artists and engineers. The scope we have in mind for art and engineering is unlimited. Modern museums, stadium roofs, dams, solar power plants, radio telescopes, star networks, fractal sculptures, fractal antennas, fractal floral arrangements, smooth metallic railroad tracks, temporary utilitarian enclosures, permanent modern architectural designs, guard structures, op art, and communication networks can all be built from the bodies of the swarm.
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    Infrastructure to factorially manipulate the mean and variance of precipitation in the field
    (Wiley, 2023) Rudgers, Jennifer A.; Luketich, Anthony; Bacigalupa, Melissa; Baur, Lauren E.; Collins, Scott L.; Hall, Kristofer M.; Hou, Enqing; Litvak, Marcy E.; Luo, Yiqi; Miller, Tom E. X.; Newsome, Seth D.; Pockman, William T.; Richardson, Andrew D.; Rinehart, Alex; Villatoro-Castañeda, Melissa; Wainwright, Brooke E.; Watson, Samantha J.; Yogi, Purbendra; Zhou, Yu
    Extensive ecological research has investigated extreme climate events or long-term changes in average climate variables, but changes in year-to-year (interannual) variability may also cause important biological responses, even if the mean climate is stable. The environmental stochasticity that is a hallmark of climate variability can trigger unexpected biological responses that include tipping points and state transitions, and large differences in weather between consecutive years can also propagate antecedent effects, in which current biological responses depend on responsiveness to past perturbations. However, most studies to date cannot predict ecological responses to rising variance because the study of interannual variance requires empirical platforms that generate long time series. Furthermore, the ecological consequences of increases in climate variance could depend on the mean climate in complex ways; therefore, effective ecological predictions will require determining responses to both nonstationary components of climate distributions: the mean and the variance. We introduce a new design to resolve the relative importance of, and interactions between, a drier mean climate and greater climate variance, which are dual components of ongoing climate change in the southwestern United States. The Mean × Variance Experiment (MVE) adds two novel elements to prior field infrastructure methods: (1) factorial manipulation of variance together with the climate mean and (2) the creation of realistic, stochastic precipitation regimes. Here, we demonstrate the efficacy of the experimental design, including sensor networks and PhenoCams to automate monitoring. We replicated MVE across ecosystem types at the northern edge of the Chihuahuan Desert biome as a central component of the Sevilleta Long-Term Ecological Research Program. Soil sensors detected significant treatment effects on both the mean and interannual variability in soil moisture, and PhenoCam imagery captured change in vegetation cover. Our design advances field methods to newly compare the sensitivities of populations, communities, and ecosystem processes to climate mean × variance interactions.
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    Swarm Robotics: Measurement and Sorting
    (2015-04-24) Zhou, Yu; McLurkin, James D.; Kavraki, Lydia E; Chaudhuri, Swarat
    To measure is an important ability for robots to sense the environment and nearby robots. Although camera, laser, and ultrasonic provide very accurate measurements, they are expensive and not scalable for large swarm of low-cost robots. The r-one robot designed at Rice University is equipped with infrared transmitters and receivers, which are designed for remote control and are very inexpensive in mass production. They are a good solution for short-range communication, since the signal attenuates at about 1 to 2 meters with appropriate voltage. This work describes my results in using them to measure bearing, orientation, and distance between nearby robots. However, infrared receivers are not designed for this kind of use, so I present a variable transmit power approach to allow useful and efficient local geometry measurements. With the ability to measure bearing and distance, I am able to solve the problem of sorting a group of n robots in a two-dimensional space. I want to organize robots into a sorted and equally-spaced path between the robots with lowest and highest label, while maintaining a connected communication network throughout the process. I begin with a straightforward geometry-based version of sorting algorithm, and point out there are many difficulties when communication range becomes limited. Then I describe a topology-based distributed algorithm for this task. I introduce operations to break the symmetry between minimum and maximum, in order to keep time, travel distance, and communication costs low without using central control. I run a set of algorithms (leader election, tree formation, path formation, path modification, and geometric straightening) in parallel. I show that my overall approach is safe, correct, and efficient. It is robust to population changes, network connectivity changes, and sensor errors. I validate my theoretical results with simulation results. My algorithm implementation uses communication messages of fixed size and constant memory on each robot, and is a practical solution for large populations of low-cost robots.