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1. DART: Diversity-enhanced Autonomy in Robot Teams

   Ayanian, Nora (December 2017, International Symposium on Robotics Research)

   This paper defines the research area of Diversity-enhanced Autonomy in Robot Teams (DART), a novel paradigm for the creation and design of policies for multi-robot coordination. While current approaches to multi-robot coordination have been successful in structured, well understood environments, they have not been successful in unstructured, uncertain environments, such as disaster response. The reason for this is not due to limitations in robot hardware, which has advanced significantly in the past decade, but in how multi-robot problems are solved. Even with significant advances in the field of multi-robot systems, the same problem-solving paradigm has remained: assumptions are made to simplify the problem, and a solution is optimized for those assumptions and deployed on the entire team. This results in brittle solutions that prove incapable if the original assumptions are invalidated. This paper introduces a new multi-robot problem-solving paradigm which relies on diverse control policies to make multi-robot systems more resilient to uncertain environments. 

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2. SMC: Satisfiability Modulo Convex Programming

   https://doi.org/10.1109/JPROC.2018.2849003  

   Shoukry, Yasser; Nuzzo, Pierluigi; Sangiovanni-Vincentelli, Alberto L.; Seshia, Sanjit A.; Pappas, George J.; Tabuada, Paulo (January 2018, Proceedings of the IEEE)

   The design of cyber-physical systems (CPSs) requires methods and tools that can efficiently reason about the interaction between discrete models, e.g., representing the behaviors of ``cyber'' components, and continuous models of physical processes. Boolean methods such as satisfiability (SAT) solving are successful in tackling large combinatorial search problems for the design and verification of hardware and software components. On the other hand, problems in control, communications, signal processing, and machine learning often rely on convex programming as a powerful solution engine. However, despite their strengths, neither approach would work in isolation for CPSs. In this paper, we present a new satisfiability modulo convex programming (SMC) framework that integrates SAT solving and convex optimization to efficiently reason about Boolean and convex constraints at the same time. We exploit the properties of a class of logic formulas over Boolean and nonlinear real predicates, termed monotone satisfiability modulo convex formulas, whose satisfiability can be checked via a finite number of convex programs. Following the lazy satisfiability modulo theory (SMT) paradigm, we develop a new decision procedure for monotone SMC formulas, which coordinates SAT solving and convex programming to provide a satisfying assignment or determine that the formula is unsatisfiable. A key step in our coordination scheme is the efficient generation of succinct infeasibility proofs for inconsistent constraints that can support conflict-driven learning and accelerate the search. We demonstrate our approach on different CPS design problems, including spacecraft docking mission control, robotic motion planning, and secure state estimation. We show that SMC can handle more complex problem instances than state-of-the-art alternative techniques based on SMT solving and mixed integer convex programming. 

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3. DART: Diversity-enhanced Autonomy in Robot Teams

   https://doi.org/10.1177/0278364919839137  

   Ayanian, Nora (April 2019, The International Journal of Robotics Research)

   This paper defines the research area of Diversity-enhanced Autonomy in Robot Teams (DART), a novel paradigm for the creation and design of policies for multi-robot coordination. Although current approaches to multi-robot coordination have been successful in structured, well-understood environments, they have not been successful in unstructured, uncertain environments, such as disaster response. Although robot hardware has advanced significantly in the past decade, the way we solve multi-robot problems has not. Even with significant advances in the field of multi-robot systems, the same problem-solving paradigm has remained: assumptions are made to simplify the problem, and a solution is optimized for those assumptions and deployed to the entire team. This results in brittle solutions that prove incapable if the original assumptions are invalidated. This paper introduces a new multi-robot problem-solving paradigm which uses a diverse set of control policies that work together synergistically within the same team of robots. Such an approach will make multi-robot systems more robust in unstructured and uncertain environments, such as in disaster response, environmental monitoring, and military applications, and allow multi-robot systems to extend beyond the highly structured and highly controlled environments where they are successful today. 

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4. Student problem solving on Textbook and YouTube Problems pertaining to Vapor Liquid Equilibrium

   Asogwa, U.; Duckett, T. R.; Liberatore, M. W. (January 2021, 2021 ASEE North Central Section Conference)

   null (Ed.)

   Faculty often utilize homework problems as a means to help students practice problem solving. Recently, with textbook solutions manuals being freely available online, students are prone to copying/cheating, which can severely limit improvements in problem solving. One hypothesis is that YouTube problems could serve as alternatives to textbook problems to significantly reduce cheating and promote better problem solving. YouTube problems are student-written problems that were inspired by events in a video publicly available online. While our previous studies have showcased positive attitudes related to engineering, high engagement, and rigor of the YouTube problems, the current study examines a subset of problems related to one major course topic, namely vapor-liquid equilibrium. The cohorts include engineering students from a public university who were assigned homework problems as part of a material and energy balance course. Two constructs were explored: problem solving and perception of problem difficulty. The study adopted an established and validated rubric to quantify performance in relevant stages of problem solving, including problem identification, representation, organization, calculation, solution completion, and solution accuracy. While problem solving can be influenced by perception of problem difficulty, the widely used NASA Task Load Index was adopted to measure the problem rigor. This paper will compare textbook and YouTube problem with respect to overall problem-solving ability as well as in each stage of problem solving. Furthermore, we will investigate whether disparities exist in students’ perceptions when solving vapor-liquid equilibrium problems. 

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5. Improving Engineering Mechanics Self-efficacy by Focusing on Abstracting the Physical World as a Precursor to Analysis

   Kaye, N; Benson, L; Headley, M; Sonavane, K (June 2024, nemo.asee.org)

   Sophomore level engineering mechanics classes typically have high rates of failure or withdrawal. Some explanations posited for this phenomenon include lack of student preparation, the difficulty of the material, ineffective instructional methods, and lack of context. Instructors and textbook authors attempt to overcome these issues with a range of pedagogical approaches such as math reviews, worked examples focused on problem solving processes, “real-world” problems, and active learning focused on physical understanding. However, the first step in the problem-solving process, abstracting the problem, is very often missing. At a fundamental level, engineers follow a four-step design process: (1) Describing or abstracting the physical world with diagrams, words, numbers, and equations (2) Analyzing their model (3) Designing something based on that analysis, and (4) Constructing the designed system. Sophomore mechanics classes traditionally focus on step (2) largely bypassing step (1), instead presenting students with drawings, numbers, and text and teaching them to apply appropriate equations. The goals of this research are (1) to develop a sophomore-level mechanics class that flips the traditional approach by starting with the physical world application and focusing on developing students’ ability to abstract as a precursor to analysis; and (2) to assess if this new approach improves student self-efficacy in basic mechanics. The hypothesis of the proposed research is that, by starting with abstraction, students will build a stronger connection between the physical world and the mechanics modeling. In turn, this will improve student’s perceptions about their ability to solve engineering mechanics problems and their motivation to pursue careers as engineers in the future. The specific research questions we seek to answer are: (1) In what ways does teaching students how to abstract the physical world affect their self-efficacy to solve problems in a basic mechanics class? and (2) In what ways does showing students how to abstract the physical world into tractable engineering science problems affect their future-oriented motivation? We are employing a mixed methods approach that combines quantitative survey data with observations, interviews, and course artifacts to address our research questions. The first phase of our research will establish baseline survey data from statics classes taught in a traditional lecture style that will be compared in future iterations of the course in which students engage in problem abstraction as the first step in the problem-solving process. Results will be presented on the baseline survey data assessing students’ problem-solving self-efficacy and future oriented motivation. In addition to the baseline survey results, we will present example lesson plans, worksheets, class assessments, and an example physical model to illustrate how abstraction will be used in the classroom. Future directions for this project will also be discussed. 

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