Model-based controller design and simulation of a marine chiller

For the past decade, the US Navy has committed to fundamental research and technology development on its next generation of surface ships. The vision is that these warships will be dynamically reconfigurable, energy-efficient, and have state-of-the-art pulsed energy weapons and sensors onboard. These developments represent a significant increase in highly dynamic on-board electrical systems that will produce correspondingly large amounts of dynamic heat generation, which, if not managed properly, will likely produce significant thermal side effects. In previous work, a highly customizable simulation framework has been developed to address thermal management issues across both the mechanical and electrical domains. This software environment is called the Dynamic Thermal Modeling and Simulation (DTMS) framework. The purpose of the current work is to introduce modern control theory into DTMS, thus providing the framework with the ability to control large-scale system simulations. The research reported in this thesis uses control of a marine chiller as a simulation vehicle. Several control strategies were implemented. These included the well-established PID controller as well as a new controller based on optimal control theory. Results for chiller simulations in the case of no-control, PID control, and optimal control are presented here. The comparative effectiveness of these controls in bringing the chiller to startup equilibrium is investigated. Response of the chiller model and the optimal controller to highly dynamic, varying heat loads was tested. The PID controller in DTMS is modeled as a special case of the transfer function control scheme. A PID controller is simple to implement but responses are inherently local and multiple controls in a system or subsystem simulation can easily lead to conflicts. The optimal control problem has been modeled as an Infinite Horizon Linear Quadratic Regulator (LQR) problem. This formulation is not local and does not create undesirable effects in parts of the system that not controlled directly by controller inputs. Using the York 200-ton marine chiller as an example, specific steps required to formulate the LQR problem are documented in this report. Implementation of the LQR controller was demonstrated for the startup to steady-state function of the chiller at full load. Treatment of the optimal controller ends with simulation of the chiller and its LQR controller under the influence of varying dynamic heat loads in a chilled water loop. The heat load variation examined has highly transient characteristics that affect the temperature of the fresh water entering the chiller, as well as the refrigerant pressure and temperature in the evaporator. The LQR formulation is shown to actively adjust to these varying operating points in a smooth and responsive manner.Mechanical Engineerin

Bridging Action Space Mismatch in Learning from Demonstrations

Learning from demonstrations (LfD) methods guide learning agents to a desired solution using demonstrations from a teacher. While some LfD methods can handle small mismatches in the action spaces of the teacher and student, here we address the case where the teacher demonstrates the task in an action space that can be substantially different from that of the student -- thereby inducing a large action space mismatch. We bridge this gap with a framework, Morphological Adaptation in Imitation Learning (MAIL), that allows training an agent from demonstrations by other agents with significantly different morphologies (from the student or each other). MAIL is able to learn from suboptimal demonstrations, so long as they provide some guidance towards a desired solution. We demonstrate MAIL on challenging household cloth manipulation tasks and introduce a new DRY CLOTH task -- cloth manipulation in 3D task with obstacles. In these tasks, we train a visual control policy for a robot with one end-effector using demonstrations from a simulated agent with two end-effectors. MAIL shows up to 27% improvement over LfD and non-LfD baselines. It is deployed to a real Franka Panda robot, and can handle multiple variations in cloth properties (color, thickness, size, material) and pose (rotation and translation). We further show generalizability to transfers from n-to-m end-effectors, in the context of a simple rearrangement task

PLGRIM: Hierarchical Value Learning for Large-scale Exploration in Unknown Environments

In order for an autonomous robot to efficiently explore an unknown environment, it must account for uncertainty in sensor measurements, hazard assessment, localization, and motion execution. Making decisions for maximal reward in a stochastic setting requires value learning and policy construction over a belief space, i.e., probability distribution over all possible robot-world states. However, belief space planning in a large spatial environment over long temporal horizons suffers from severe computational challenges. Moreover, constructed policies must safely adapt to unexpected changes in the belief at runtime. This work proposes a scalable value learning framework, PLGRIM (Probabilistic Local and Global Reasoning on Information roadMaps), that bridges the gap between (i) local, risk-aware resiliency and (ii) global, reward-seeking mission objectives. Leveraging hierarchical belief space planners with information-rich graph structures, PLGRIM addresses large-scale exploration problems while providing locally near-optimal coverage plans. We validate our proposed framework with high-fidelity dynamic simulations in diverse environments and on physical robots in Martian-analog lava tubes

Secondary cytogenetic abnormalities in core-binding factor AML harboring inv(16) vs t(8;21)

Patients with core-binding factor (CBF) acute myeloid leukemia (AML), caused by either t(8; 21)(q22;q22) or inv(16)(p13q22)/t(16;16)(p13;q22), have higher complete remission rates and longer survival than patients with other subtypes of AML. However, similar to 40% of patients relapse, and the literature suggests that patients with inv(16) fare differently from those with t(8;21). We retrospectively analyzed 537 patients with CBF-AML, focusing on additional cytogenetic aberrations to examine their impact on clinical outcomes. Trisomies of chromosomes 8, 21, or 22 were significantly more common in patients with inv(16)/t(16;16): 16% vs 7%, 6% vs 0%, and 17% vs 0%, respectively. In contrast, del(9q) and loss of a sex chromosome were more frequent in patients with t(8;21): 15% vs 0.4% for del(9q), 37% vs 0% for loss of X in females, and 44% vs 5% for loss of Y in males. Hyperdiploidy was more frequent in patients with inv(16) (25% vs 9%, whereas hypodiploidy was more frequent in patients with t(8;21) (37% vs 3%. In multivariable analyses (adjusted for age, white blood counts at diagnosis, and KIT mutation status), trisomy 8 was associated with improved overall survival (OS) in inv(16), whereas the presence of other chromosomal abnormalities (not trisomy 8) was associated with decreased OS. In patients with t(8;21), hypodiploidy was associated with improved disease-free survival; hyperdiploidy and del(9q) were associated with improved OS. KIT mutation (either positive or not tested, compared with negative) conferred poor prognoses in univariate analysis only in patients with t(8;21)

NeBula: Team CoSTAR's robotic autonomy solution that won phase II of DARPA Subterranean Challenge

This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge. Specifically, it presents the techniques utilized within the Tunnel (2019) and Urban (2020) competitions, where CoSTAR achieved second and first place, respectively. We also discuss CoSTAR¿s demonstrations in Martian-analog surface and subsurface (lava tubes) exploration. The paper introduces our autonomy solution, referred to as NeBula (Networked Belief-aware Perceptual Autonomy). NeBula is an uncertainty-aware framework that aims at enabling resilient and modular autonomy solutions by performing reasoning and decision making in the belief space (space of probability distributions over the robot and world states). We discuss various components of the NeBula framework, including (i) geometric and semantic environment mapping, (ii) a multi-modal positioning system, (iii) traversability analysis and local planning, (iv) global motion planning and exploration behavior, (v) risk-aware mission planning, (vi) networking and decentralized reasoning, and (vii) learning-enabled adaptation. We discuss the performance of NeBula on several robot types (e.g., wheeled, legged, flying), in various environments. We discuss the specific results and lessons learned from fielding this solution in the challenging courses of the DARPA Subterranean Challenge competition.The work is partially supported by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004), and Defense Advanced Research Projects Agency (DARPA)

Core-binding factor acute myeloid leukemia with t(8;21) Risk factors and a novel scoring system (I-CBFit)

Background: Although the prognosis of core-binding factor (CBF) acute myeloid leukemia (AML) is better than other subtypes of AML, 30% of patients still relapse and may require allogeneic hematopoietic cell transplantation (alloHCT). However, there is no validated widely accepted scoring system to predict patient subsets with higher risk of relapse. Methods: Eleven centers in the US and Europe evaluated 247 patients with t(8;21) (q22;q22). Results: Complete remission (CR) rate was high (92.7%), yet relapse occurred in 27.1% of patients. A total of 24.7% of patients received alloHCT. The median diseasefree (DFS) and overall (OS) survival were 20.8 and 31.2 months, respectively. Age, KIT D816V mutated (11.3%) or nontested (36.4%) compared with KIT D816V wild type (52.5%), high white blood cell counts (WBC), and pseudodiploidy compared with hyper- or hypodiploidy were included in a scoring system (named I-CBFit). DFS rate at 2 years was 76% for patients with a low-risk I-CBFit score compared with 36% for those with a high-risk I-CBFit score (P <0.0001). Low- vs high-risk OS at 2 years was 89% vs 51% (P <0.0001). Conclusions: I-CBFit composed of readily available risk factors can be useful to tailor the therapy of patients, especially for whom alloHCT is not need in CR1 (ie, patients with a low-risk score)

Core-binding factor acute myeloid leukemia with t(8;21): Risk factors and a novel scoring system (I-CBFit).

BackgroundAlthough the prognosis of core-binding factor (CBF) acute myeloid leukemia (AML) is better than other subtypes of AML, 30% of patients still relapse and may require allogeneic hematopoietic cell transplantation (alloHCT). However, there is no validated widely accepted scoring system to predict patient subsets with higher risk of relapse.MethodsEleven centers in the US and Europe evaluated 247 patients with t(8;21)(q22;q22).ResultsComplete remission (CR) rate was high (92.7%), yet relapse occurred in 27.1% of patients. A total of 24.7% of patients received alloHCT. The median disease-free (DFS) and overall (OS) survival were 20.8 and 31.2 months, respectively. Age, KIT D816V mutated (11.3%) or nontested (36.4%) compared with KIT D816V wild type (52.5%), high white blood cell counts (WBC), and pseudodiploidy compared with hyper- or hypodiploidy were included in a scoring system (named I-CBFit). DFS rate at 2 years was 76% for patients with a low-risk I-CBFit score compared with 36% for those with a high-risk I-CBFit score (P &lt; 0.0001). Low- vs high-risk OS at 2 years was 89% vs 51% (P &lt; 0.0001).ConclusionsI-CBFit composed of readily available risk factors can be useful to tailor the therapy of patients, especially for whom alloHCT is not need in CR1 (ie, patients with a low-risk I-CBFit score)