Browsing by Author "Kavraki, Lydia"
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Item Calculating Variant Allele Fraction of Structural Variation in Next Generation Sequencing by Maximum Likelihood(2015-04-23) Fan, Xian; Nakhleh, Luay K.; Kavraki, Lydia; Jermaine, Chris; Chen, KenCancer cells are intrinsically heterogeneous. Multiple clones with their unique variants co-exist in tumor tissues. The variants include point mutations and structural variations. Point mutations, or single nucleotide variants are those variants on one base; structural variations are variations involving sequence with length not smaller than 50 bases. Approaches to estimate the number of clones and their respective percentages from point mutations have been recently proposed. However, structural variations, although involving more reads than point mutations, have not been quantitatively studied in characterizing cancer heterogeneity. I describe in this thesis a maximum likelihood approach to estimate variant allele fraction of a putative structural variation, as a step towards the characterization of tumor heterogeneity. A software tool, BreakDown, implemented in Perl realizing this statistical model is publicly available. I studied the performance of BreakDown through both simulated and real data, and found BreakDown outperformed other methods such as THetA in estimating variant allele fractions.Item Collective Transport of an Unknown Object by Multi Robots with Limited Sensing(2015-04-29) Habibi, Golnaz; McLurkin, James; Kavraki, Lydia; O'Malley, Marcia; Schwager, MacThis thesis presents a fully distributed approach to retrieve a large object from an unknown environment. The object is assumed to be located in an environment without GPS or Internet infrastructure. The object is too heavy to transport by one robot. The collective transport problem is broken into five major steps: 1) Exploring the unknown environment and finding the object. 2) Grasping the object. 3) Characterizing the object. 4) Planning a path to the desired location. 5) Transporting the object to the desired location. This thesis presents efficient distributed algorithms for robots with limited sensing to accomplish steps three to five. Object characterization includes centroid estimation and object dimension estimation. Two algorithms are developed for centroid estimation. In the first algorithm, each robot uses a communication tree to compute the sum of its children's positions. The second algorithm is based on pipelined consensus, which is an extension of pairwise gossip-based consensus. Two algorithms are presented to estimate object's dimensions. The first one is a distributed principal component analysis algorithm, and the second one is the distributed version of rotating calipers algorithm. A distributed path planning algorithm is presented. Robots have already been scattered across the terrain and collectively sample the obstacles in the environment. Robots use this sampling along with the estimated dimensions of the object, from above, to construct a configuration space of robots and the object. A variant of the distributed Bellman-Ford algorithm is then used to construct a shortest-path tree. A path navigation algorithm is presented to map each path segments to a distributed motion controller that can command the robots to transport the object. Four distributed motion controllers are designed including: rotation around a pivot robot, rotation in-place around an estimated centroid of the object, translation, and a combined motion of rotation and translation. Finally, a distributed recovery algorithm is presented to recover the robots efficiently and safely after collective transport. This recovery method uses k-redundant maximum-leaf spanning trees that guarantee connectivity during the recovery. All algorithms are verified through simulation as well as hardware experiments. The results are promising, and the algorithms successfully transport convex or concave objects in simulation and hardware experiments. After robots transport the object, robots are successfully recovered at home location by using the recovery algorithm. All algorithms discussed in this thesis are fully distributed, efficient, and robust to object shape and network population changes.Item Distributed Space Coverage for Exploration, Localization, and Navigation in Unknown Environments(2015-04-22) Lee, Seoung Kyou; McLurkin, James D.; Kavraki, Lydia; Hicks, Illya; Fekete, SándorMany tasks, such as search and rescue, exploration and mapping, security and surveil- lance are suited for mobile robots. These tasks require the population to spread out over a large environment, where the sensing and communicating capacity of an individual robot might be too small to cover the entire environment. This thesis presents approaches for cooperative coverage tasks by large populations of robots to overcome the limitation of a single robot. Not only can a multi-robot system accomplish the coverage task far more efficiently than an individual robot, but also it is more reliable against individual hardware failure. This thesis investigates two different scenarios with respect to distributed multi-robot space coverage in an unknown environment: First, I consider the space coverage in un- bounded environments. This scenario takes care of robots searching or monitoring in an unlimited region, such as a forest or submarine sea floor. For efficient scanning, robots need to spread out sufficiently to cover a large area. At the same time, they have to be close to their neighbors to prevent network disconnection. In order to fulfill both requirements simultaneously, I present a cohesive configuration controller by combining existing flock- ing strategies with a boundary force algorithm and network sensing and mode switching method. By doing so, robots form a convex and cohesive configuration without any dis- connection. Moreover, they maintain this final configuration even while moving. Secondly, I study space coverage problem in bounded environments that pursues to build a robotic sensor network. In this scenario, I present an algorithm to construct a triangulation using multiple low-cost robots. The resulting triangulation approximately maps the geometric characteristics of its surrounding environment and produces a physical data structure that stores triangle information distributively. This physical data structure provides auxiliary data to robots and lets them accomplish the application’s goal using only local communications. In this thesis, I address one simple application for patrolling and one complex application for topological Voronoi tessellation and center computation. This work provides theoretical guarantees about the algorithm performance. I also provide numerous simulation and hardware experiment results using the model of low- cost platforms to validate the feasibility of presented methods. In both scenarios, robots successfully form desired configurations without the aid of centralized infrastructures. In addition, the group of robots maintains the connectivity while running the algorithms.Item Impedance Control Approaches for Series Elastic Actuators(2015-09-22) Mehling, Josh S; O'Malley, Marcia K.; Ghorbel, Fathi; Kavraki, LydiaFor applications requiring interaction with humans or unstructured environments, robots are increasingly designed to leverage the intentional drivetrain compliance of series elastic actuators (SEAs). Impedance control, likewise, is of particular value in these applications, having long been considered an effective means of addressing dynamic interaction. Although impedance controlled SEAs appear often in the literature, a number of important questions remain unanswered. If, for example, robust contact stability is required, can an SEA render a virtual stiffness greater than its physical spring rate? Previous studies answer no, but this is largely a question of control architecture. It is proven here, as part of a larger study comparing the stability and passivity of five different control approaches, that this is in fact possible if disturbance observer based impedance control is adopted. The fidelity with which SEAs render desired impedances is important as well. In comparing the impedance rendering accuracy of multiple control approaches, experimental data in both the time and frequency domain point once more to disturbance observer based impedance control. This new SEA control architecture yields demonstrable improvement in actuator transparency, closed loop hysteresis, and the SEA's dynamic response to both reference commands and external torques. Two new performance metrics are formulated based on the H∞ and H2 system norms to further quantify SEA impedance rendering accuracy across the frequency spectrum. A novel model matching framework is then constructed that leverages these metrics for the optimal synthesis of SEA impedance control. Passive and accurate controllers result that, having been deployed on physical hardware, represent the first application of LMI-based, multi-objective, optimal control synthesis to series elastic actuation. These results are all confirmed experimentally on high performance SEAs. Three new actuator designs are presented that provide up to 350 Nm in peak torque and torque sensing resolutions as low as 0.006 Nm. This 58,333:1 dynamic range (an order of magnitude improvement over previous SEAs) is achieved in a torque dense, 94.3 Nm/kg package ideally suited for use in humanoid robots. Demonstrated SEA performance reinforces the practical utility of the recommended control approaches and speaks to the broader applicability of impedance controlled SEAs to human-centric robots.Item Improving Protein Conformational Sampling by Using Guiding Projections(2016-04-08) Novinskaya, Anastasia; Kavraki, LydiaThe ability of a protein to perform its function is mainly dened by the spatial shape it exists in and the way the protein alternates between several stable shapes. To prevent or cure diseases related to protein malfunctioning we study the conformational space of proteins. Sampling-based motion planning algorithms from the eld of robotics have been very successful at this task. However, studying the conformational space of large proteins with hundreds or thousands of Degrees of Freedom remains a big challenge. In this work we investigate how the dimensionality curse can be mitigated by means of low-dimensional projections. Our experiments demonstrate that incorporating the information available on the studied protein into the projection can benefit the conformational exploration process. The techniques we developed to generate efficient low-dimensional projections can enable sampling-based planners to study protein systems, such as viruses, that are currently too large to be investigated by other methods.Item Robonaut 2 and you: Specifying and executing complex operations(IEEE, 2017) Baker, William; Kingston, Zachary; Moll, Mark; Badger, Julia; Kavraki, LydiaCrew time is a precious resource due to the expense of trained human operators in space. Efficient caretaker robots could lessen the manual labor load required by frequent vehicular and life support maintenance tasks, freeing astronaut time for scientific mission objectives. Humanoid robots can fluidly exist alongside human counterparts due to their form, but they are complex and high-dimensional platforms. This paper describes a system that human operators can use to maneuver Robonaut 2 (R2), a dexterous humanoid robot developed by NASA to research co-robotic applications. The system includes a specification of constraints used to describe operations, and the supporting planning framework that solves constrained problems on R2 at interactive speeds. The paper is developed in reference to an illustrative, typical example of an operation R2 performs to highlight the challenges inherent to the problems R2 must face. Finally, the interface and planner is validated through a case-study using the guiding example on the physical robot in a simulated microgravity environment. This work reveals the complexity of employing humanoid caretaker robots and suggest solutions that are broadly applicable.Item Targeting the Src Homology 2 (SH2) Domain of Signal Transducer and Activator of Transcription 6 (STAT6) with Cell-Permeable, Phosphatase-Stable Phosphopeptide Mimics Potently Inhibits Tyr641 Phosphorylation and Transcriptional Activity(American Chemical Society, 2015) Mandal, Pijus K.; Morlacchi, Pietro; Knight, J. Morgan; Link, Todd M.; Lee, Gilbert R. IV; Nurieva, Roza; Singh, Divyendu; Dhanik, Ankur; Kavraki, Lydia; Corry, David B.; Ladbury, John E.; McMurray, John S.Signal transducer and activator of transcription 6 (STAT6) transmits signals from cytokines IL-4 and IL-13 and is activated in allergic airway disease. We are developing phosphopeptide mimetics targeting the SH2 domain of STAT6 to block recruitment to phosphotyrosine residues on IL-4 or IL-13 receptors and subsequent Tyr641 phosphorylation to inhibit the expression of genes contributing to asthma. Structure–affinity relationship studies showed that phosphopeptides based on Tyr631 from IL-4Rα bind with weak affinity to STAT6, whereas replacing the pY+3 residue with simple aryl and alkyl amides resulted in affinities in the mid to low nM range. A set of phosphatase-stable, cell-permeable prodrug analogues inhibited cytokine-stimulated STAT6 phosphorylation in both Beas-2B human airway cells and primary mouse T-lymphocytes at concentrations as low as 100 nM. IL-13-stimulated expression of CCL26 (eotaxin-3) was inhibited in a dose-dependent manner, demonstrating that targeting the SH2 domain blocks both phosphorylation and transcriptional activity of STAT6.