Dept. of Industrial and Enterprise Systems Engineering
http://hdl.handle.net/2142/16358
Fri, 28 Jul 2017 01:05:18 GMT2017-07-28T01:05:18ZDesign and analysis of fiber reinforced elastomeric enclosures with application in upper arm orthosis
http://hdl.handle.net/2142/95617
Design and analysis of fiber reinforced elastomeric enclosures with application in upper arm orthosis
Singh, Gaurav
Fiber Reinforced Elastomeric Enclosures (FREEs) are soft pneumatic actuators that deform in a predetermined fashion upon inflation. They are constructed using a hollow elastomeric cylinder reinforced by two families of helical fibers. This thesis analyzes the deformation behavior of FREEs by formulating a simple calculus of variations problem that involves constrained maximization of the enclosed volume. The model accurately captures the deformed shape (kinematics) for FREEs with any general fiber angle orientation, and its relation with actuation pressure, material properties and applied load (kinetostatics). The accuracy of the model is verified by benchmarking with existing models for a popular McKibben Pneumatic Artificial muscle (PAM) actuator with two helically wrapped families of fibers having equal and opposite orientations. For FREEs with any general fiber orientations and other novel designs with no prior literature, the model is validated experimentally. This model is deemed to be useful in the design synthesis of fiber reinforced elastomeric actuators for any desired motion and force requirement. FREEs are soft, compliant, and have a high power to weight ratio, which makes them suitable for orthotic devices for upper extremities.
The second part of the thesis considers the design and fabrication of a soft pneumatic sleeve for arm orthosis that uses a contracting FREE is shown. The sleeve is designed to reduce wrist loads in patients that use crutches for ambulation, thereby reducing the risk of joint injury. It forms an alternate load path between the crutch and the forearm, circumventing the wrist. The constricting force generated by the sleeve on the arm is analyzed by a string model and validated with experiments.
Soft Robotics; Soft Actuator; Bio-inspired; Artificial Muscle; Calculus of Variation; Actuator; Fiber reinforced; Orthosis; Lofstrand Crutch
Wed, 07 Dec 2016 00:00:00 GMThttp://hdl.handle.net/2142/956172016-12-07T00:00:00ZSingh, GauravDesign and analysis of a soft spiral gripper
http://hdl.handle.net/2142/95613
Design and analysis of a soft spiral gripper
Uppalapati, Naveen Kumar
Continuum robots have been gaining popularity in recent years for their umpteen advantages. Soft robots are a class of continuum robots which are made of squishy materials which have the added benefit of being innocuous to humans. Soft robotic grippers are one of the major application of soft robots as they have the ability to conform and adapt their structure to the object to be grasped. This work presents a bio-inspired technique to increase contact area while grasping and handling long slender objects by helically twisting around them. An embodiment of such a spiral gripper utilizes unique configurations of pneumatically actuated Fiber Reinforced Elastomeric Enclosures which has a range of motions like extension, rotation, contraction. This work presents a detailed analysis technique using Cosserat beam theory to estimate the normal contact force exerted by the spiral gripper on cylindrical objects.
Soft robots; Soft Grippers; Cosserat beams; Bio-inspired Robots
Tue, 06 Dec 2016 00:00:00 GMThttp://hdl.handle.net/2142/956132016-12-06T00:00:00ZUppalapati, Naveen KumarA study in digital prototyping and multidisciplinary design team collaboration
http://hdl.handle.net/2142/95427
A study in digital prototyping and multidisciplinary design team collaboration
Batmunkh, Baigalmaa
This thesis presents results of work that: (1) examined collaboration between engineering and industrial design students in solving biomedical design tasks, as well as gathering information on student perceptions of the relative usefulness of digital prototyping tools employed in the development of the design projects, and (2) reviewed the design literature while focusing on areas related to a computer-aided product realization course curriculum, namely: reflective practice in design, design thinking, computer-aided design (CAD) and creativity, prototyping, 3D printing, and next generation CAD.
In the fall of 2015 twenty-three students were enrolled in a testbed computer-aided product design course. In the first half of the semester these students were familiarized with a common set of 3D CAD and digital prototyping (i.e., 3D printing, 3D scanning) tools. The second half of the semester was devoted to biomedical device design project tasks, where course participants were assigned to six teams in order to solve a specific design problem. Each team included members from bioengineering, industrial design, and either systems or mechanical engineering, or both. There were two main course instructors, one from engineering and one from industrial design. The course also included a teaching professor from bioengineering, a graduate research assistant, as well as a graduate teaching assistant.
We hypothesized that ready access to 3D printing aids in successful design outcomes and addressed the following research questions: (1) what activities do multidisciplinary student teams pursue in the early stages of design?, (2) what benefits and challenges with regard to multidisciplinary design collaboration do students frequently note?, (3) what are the students’ perceived understandings of the effectiveness of cloud-based 3D CAD tools on both team collaboration and design concept development?, and (4) what are the students’ perceptions of the use of 3D printing for developing design solutions? A combination of methods, including audio recordings, reflection journals and surveys were employed to answer these research questions. The results of this work showed that course participants had positive views of the multidisciplinary composition of the design teams. Another finding was that the use of the digital prototyping tools, in particular the use of the cloud-based 3D CAD tool and 3D printing was helpful in supporting collaboration as well as in improving the likelihood of successful design outcomes. Another key finding from reviewing design literature was the role and importance of reflection in design education and practice. The results of this study have implications for promoting design team collaboration across disciplines, in particular among engineering and design students, and for contributing to effective teaching, learning and exploitation of new 3D CAD digital prototyping tools in engineering design education. Finally, based on the overall results of this thesis, recommendations aimed at improving the course curriculum are discussed.
Multidisciplinary design collaboration; Engineering design; Design thinking; Reflective practice; 3D Computer-aided design (CAD); 3D printing; 3D scanning; Physical prototyping
Fri, 09 Dec 2016 00:00:00 GMThttp://hdl.handle.net/2142/954272016-12-09T00:00:00ZBatmunkh, BaigalmaaGenerative design algorithms in topology optimization of passive heat spreaders
http://hdl.handle.net/2142/95423
Generative design algorithms in topology optimization of passive heat spreaders
Lohan, Danny John
This thesis explores the use of generative algorithms in engineering design. A framework for using generative algorithms in design is presented and a case study for passive heat spreaders is devised to demonstrate the execution of this framework. Topology optimization methods are now the state of the art for heat spreader design. These methods are introduced herein and are used to benchmark solutions obtained through generative design methods. The generative design methodology augments the existing topology optimization methods using evolutionary algorithms in hybrid optimization. The results presented in this thesis are the first steps in creating a rich and generalizable design optimization methodology.
Topology Optimization; Heat Spreader Design; Generative Algorithms
Thu, 08 Dec 2016 00:00:00 GMThttp://hdl.handle.net/2142/954232016-12-08T00:00:00ZLohan, Danny JohnMachine learning and task disambiguation in hand-picked agriculture
http://hdl.handle.net/2142/95298
Machine learning and task disambiguation in hand-picked agriculture
Srivastava, Nitin
Although GPS-based travel data has been studied by many mainly for automated travel mode detection, the area of activity mode detection during harvest still remains an open technical challenge. This thesis proposes and tests a pattern recognition approach to harvest mode recognition from GPS travel data collected from 4 volunteers for 2 days in Oxnard, California. Three profiles were created to characterize activities performed during harvest. Piecewise quadratic interpolation was used on smoothened data to detect segments in trips taken by workers. Trip segments are then evaluated with the different profiles to find the best fitting profiles and the associated optimal parameters. Results indicated that the proposed framework performs well under data discrepancies. Identification of different modes during harvest is of relevance for assessing productivity of different workers and addressing any mismatch in vehicle scheduling. In our assessment, this proof-of-principle study demonstrates a use case for using GPS data in disambiguating different activities conducted during harvest; scalability of the methodology remains a challenge - programming GPUs to take advantage of independence in the different processes has been proposed to reduce the code runtime.
Machine learning; Pattern recognition; GPS data; Agriculture
Wed, 26 Oct 2016 00:00:00 GMThttp://hdl.handle.net/2142/952982016-10-26T00:00:00ZSrivastava, NitinShoulder pain in manual wheelchair users: towards a multi-disciplinary solution for a multi-faceted problem
http://hdl.handle.net/2142/89198
Shoulder pain in manual wheelchair users: towards a multi-disciplinary solution for a multi-faceted problem
Jayaraman, Chandrasekaran
It is estimated that there are over 2 million manual wheelchair users in the United States. Up to 70% of manual wheelchair users report upper limb pain, which is mainly manifested in the shoulder and wrist. Shoulder pain in wheelchair users is linked to difficulty performing activities of daily living, decreased physical activity and decreased quality of life.
The main focus of this dissertation is to identify biomarkers from wheelchair propulsion data that are potentially related to shoulder pain in manual wheelchair users. Three biomarkers that distinguish between manual wheelchair users with and without shoulder pain are identified. The acceptability of the identified biomarkers are subjected to hypothesis testing using data collected from a sample of 30 experienced adult manual wheelchair users with and without shoulder pain. The results and their implications will be discussed. In this dissertation we will also discuss the interpretation and the physical significance of each of the results, a summary of limitations for the approaches adopted, and suggestions on the future course of research to address these limitations.
While the past two decades of research on shoulder pain and wheelchair propulsion has led to the development of important clinical guidelines, it has failed to identify specific biomarkers that may be related to shoulder pain in manual wheelchair users. This could be in part due to employing a binary approach by focusing on just (1) the pure bio-mechanical aspects, and (2) wheelchair design aspects (ergonomics). The originality of this dissertation is in the adoption of a multidisciplinary approach. Methodologies integrating theories and analyses from fields related to human movement science such as human motor control theory, non-linear dynamics and human factors (occupational ergonomics) are adopted to identify potential biomarkers that relate to shoulder pain in manual wheelchair users.
This dissertation concludes with preliminary results from a prototype wearable device, custom developed for manual wheelchair users. Wheelchair propulsion data obtained from the device will be benchmarked with data from the currently available technologies for tracking manual wheelchair propulsion (SMARTWheel and motion capture). This dissertation also proposes a framework for incorporating the research findings into the custom developed wearable technology for home-based rehabilitation training purposes.
Shoulder pain; Human motor control; jerk minimization; jerk cost; repetitive injury; kinematics, compensatory strategy; pain; non-linear dynamics; entropy; complexity; wheelchair propulsion recovery kinematics; Shoulder pain and wheelchair propulsion; wheelchair propulsion patterns; trunk kinematics
Tue, 17 Nov 2015 00:00:00 GMThttp://hdl.handle.net/2142/891982015-11-17T00:00:00ZJayaraman, ChandrasekaranOn the resolution of misspecification in stochastic optimization, variational inequality, and game-theoretic problems
http://hdl.handle.net/2142/89033
On the resolution of misspecification in stochastic optimization, variational inequality, and game-theoretic problems
Jiang, Hao
Traditionally, much of the research in the field of optimization algorithms has assumed that problem parameters are correctly specified. Recent efforts under the robust optimization framework have relaxed this assumption by allowing unknown parameters to vary in a prescribed uncertainty set and by subsequently solving for a worst-case solution. This dissertation considers a rather different approach in which the unknown or misspecified parameter is a solution to a suitably defined (stochastic) learning problem based on
having access to a set of samples. Practical approaches in resolving such a set of coupled problems have been either sequential or direct variational approaches. In the case of the former, this entails the following steps: (i) a solution to the learning problem for parameters is first obtained; and (ii) a solution is obtained to the associated parametrized computational problem by using (i). Such avenues prove difficult to adopt particularly since the learning process has to be terminated finitely and consequently, in large-scale or stochastic instances, sequential approaches may often be corrupted by error. On the other hand, a variational approach requires that the problem may be recast as a possibly non-monotone stochastic variational inequality problem; but there are no known first-order (stochastic) schemes currently available for the solution of such
problems. Motivated by these challenges, this thesis focuses on studying joint schemes of optimization and learning in three settings: (i) misspecified stochastic optimization and variational inequality problems, (ii)
misspecified stochastic Nash games, (iii) misspecified Markov decision processes.
In the first part of this thesis, we present a coupled stochastic approximation scheme which simultaneously solves both the optimization and the learning problems. The obtained schemes are shown to be equipped with almost sure convergence properties in regimes when the function $f$ is either strongly convex as well as merely convex. Importantly, the scheme displays the optimal rate for strongly convex problems while in merely convex regimes, through an averaging approach, we quantify the degradation associated with learning
by noting that the error in function value after $K$ steps is $O(\sqrt{\ln(K)/K})$, rather than $O(\sqrt{1/K})$ when $\theta^*$ is available. Notably, when the averaging window is modified suitably, it can be see that the original rate
of $O(\sqrt{1/K})$ is recovered. Additionally, we consider an online counterpart of the misspecified optimization problem and provide a non-asymptotic bound on the average regret with respect to an offline counterpart. We also extend these statements to a class of stochastic variational inequality problems, an object that unifies stochastic convex optimization problems and a range of stochastic equilibrium problems. Analogous almost-sure convergence statements are provided in strongly monotone and merely monotone regimes, the latter facilitated by using an iterative Tikhonov regularization. In the merely monotone regime, under a
weak-sharpness requirement, we quantify the degradation associated with learning and show that expected
error associated with $dist(x_k,X^*)$ is $O(\sqrt{\ln(K)/K})$.
In the second part of this thesis, we present schemes for computing equilibria to two classes of convex
stochastic Nash games complicated by a parametric misspecification, a natural concern in the control of large-
scale networked engineered system. In both schemes, players learn the equilibrium strategy while resolving
the misspecification: (1) Stochastic Nash games: We present a set of coupled stochastic approximation
distributed schemes distributed across agents in which the first scheme updates each agent’s strategy via a projected (stochastic) gradient step while the second scheme updates every agent’s belief regarding its misspecified parameter using an independently specified learning problem. We proceed to show that the produced sequences converge to the true equilibrium strategy and the true parameter in an almost sure sense. Surprisingly, convergence in the equilibrium strategy achieves the optimal rate of convergence in a mean-squared sense with a quantifiable degradation in the rate constant; (2) Stochastic Nash-Cournot games with unobservable aggregate output: We refine (1) to a Cournot setting where we assume that the tuple of strategies is unobservable while payoff functions and strategy sets are public knowledge through a common knowledge assumption. By utilizing observations of noise-corrupted prices, iterative fixed-point schemes are developed, allowing for simultaneously learning the equilibrium strategies and the misspecified parameter in an almost-sure sense.
In the third part of this thesis, we consider the solution of a finite-state infinite horizon Markov Decision Process (MDP) in which both the transition matrix and the cost function are misspecified, the latter in a parametric sense. We consider a data-driven regime in which the learning problem is a stochastic
convex optimization problem that resolves misspecification. Via such a framework, we make the following
contributions: (1) We first show that a misspecified value iteration scheme converges almost surely to its
true counterpart and the mean-squared error after $K$ iterations is $O(\sqrt{1/K})$; (2) An analogous asymptotic almost-sure convergence statement is provided for misspecified policy iteration; and (3) Finally, we present a constant steplength misspecified Q-learning scheme and show that a suitable error metric is $O(\sqrt{1/K})$ + $O(\sqrt{δ})$ after K iterations where δ is a bound on the steplength.
Misspecified Stochastic Optimization; Misspecified Variational Inequality; Misspecified Stochastic Nash Games; Misspecified Markov Decision Processes
Wed, 02 Dec 2015 00:00:00 GMThttp://hdl.handle.net/2142/890332015-12-02T00:00:00ZJiang, HaoOn computing a liveness enforcing supervisory policy for a class of general petri nets
http://hdl.handle.net/2142/89010
On computing a liveness enforcing supervisory policy for a class of general petri nets
Somnath, Nisha
Discrete-Event/Discrete-State (DEDS) Systems are prone to livelocks. Once a system enters a livelocked-state, there is at least one activity of the modeled system that cannot be executed from all subsequent states of the system. This phenomenon is common to
many operating systems where some process enters into a state of suspended animation for perpetuity, and the user is left with no other option than to terminate the process, or reboot the machine. This thesis is about computing Liveness Enforcing Supervisory Policies (LESPs) for Petri net (PN) models of DEDS systems. The existence of an LESP for general PNs is not even semi-decidable.
This thesis identifies two classes of PNs F and H for which the existence of a LESP is decidable. It also describes an
object-oriented implementation of a procedure for the synthesis of the minimally-restrictive LESP for any instance from these classes.
The minimally-restrictive LESP prevents the occurrence of events in a DEDS system only when it is absolutely necessary.
A suite of methods, based on refinement/abstraction concepts, is developed to reduce the complexity of LESP-synthesis. This involves
the synthesis of a LESP for a simplified-version of a complex PN structure, which is subsequently refined to serve as a LESP for the original
complex PN.
Two PNs are in a simulation relationship if their behaviors are "similar" in a formal sense. The thesis concludes with a result that shows that the above mentioned procedure can be generalized to PNs in simulation relationships. That is, a LESP for a PN can be modified to
serve as a LESP for another PN that is "similar". The implementation of this theoretical observation is suggested as a topic for future work.
Petri Nets; Supervisory control; Discrete event systems
Tue, 01 Dec 2015 00:00:00 GMThttp://hdl.handle.net/2142/890102015-12-01T00:00:00ZSomnath, NishaA large-scale neighborhood search approach to vehicle routing pick-up and delivery problem with time windows under uncertainty
http://hdl.handle.net/2142/88233
A large-scale neighborhood search approach to vehicle routing pick-up and delivery problem with time windows under uncertainty
Tumuluri, Praveen
The vehicle routing problem with shipment pick-up and delivery with time windows (VRPPDTW) is one of the core problems that is addressed by a package delivery company in its operations. Most often, this problem has been addressed from the point of view of cost-cutting, to achieve the lowest cost possible under a given/predicted demand and service time scenario. This thesis aims to study a real-world VRPPDTW problem with side-constraints and build solutions that are cost-effective as well as robust to stochasticity in demands and service times. Even without the additional side constraints, the VRPPDTW is NP-hard. In particular, we consider the solution of VRPPDTW with side-constraints adopted by a carrier. Because of the nature as well as the size of the problem and the network, we demonstrate that the problem is combinatorially explosive. We therefore develop a large-scale neighbourhood search heuristic combined with a break-and-join heuristic and a clustering heuristic. We use this heuristic to build a set of schedules with far lower operating costs than the existing solution and effectively decrease the costs by 15% by reducing the number of routes needed to serve the shipments. We then build a framework to evaluate the performance of the solutions under stochasticity, and present results related to under stochasticity in service times.
vehicle routing; large-scale neighbourhood search
Fri, 24 Jul 2015 00:00:00 GMThttp://hdl.handle.net/2142/882332015-07-24T00:00:00ZTumuluri, PraveenOn the convexity of right-closed sets and its application to liveness enforcement in Petri Nets
http://hdl.handle.net/2142/88056
On the convexity of right-closed sets and its application to liveness enforcement in Petri Nets
Salimi, Ehsan
A set of n-dimensional integral vectors,
Nn, is said to be right-closed if for any x 2
, any
vector y x also belongs to it. An integral-set
Nn is convex if and only if there is a convex set
C Rn such that
= Int(C), where Int( ) denotes the integral points in the set argument. In this
dissertation, we show that the problem of verifying convexity of a right-closed set is decidable. Following
this, we present a polynomial time, LP-based algorithm, for verifying the convexity of a right-closed
set of integral vectors, when the dimension n is xed. This result is to be viewed against the backdrop
of the fact that checking the convexity of a real-valued, geometric set can only be accomplished in an
approximate sense; and, the fact that most algorithms involving sets of real-valued vectors do not apply
directly to their integral counterparts. Also, we discuss a grid-search based algorithm for verifying the
convexity of such a set, although not a polynomial time procedure, it is a method that veri es the
convexity of right-closed sets in a reasonable time complexity.
On the application side, right-closed sets feature in the synthesis of Liveness Enforcing Supervisory
Policies (LESPs) for a large family of Petri Nets (PNs). For any PN structure N from this family,
the set of initial markings, (N), for which there is a LESP, is right-closed. A LESP determines the
transitions of a PN that are to be permitted to re at any marking in such a manner that, irrespective
of the past, every transition can be red at some marking in the future. A system that is modeled by a
live PN does not experience livelocks, which serves as the motivation for investigating implementation
paradigms for LESPs in practice.
If a transition is prevented from ring at a marking by a LESP, and all LESPs, irrespective of
the implementation-paradigm that is chosen, prescribe the same control for the marking, then it is a
minimally restrictive LESP. It is possible to synthesize the minimally restrictive LESP for any instance N of the aforementioned family that uses the right-closed set of markings (N). The literature also
contains an implementation paradigm called invariant-based monitors for liveness enforcement in PNs.
This paradigm is popular due to the fact that the resulting supervisor can be directly incorporated
into the semantics of the PN model of the controlled system. In this work, we show that there is an
invariant-based monitor that is equivalent to the minimally restrictive LESP that uses the right-closed
set (N) if and only if (N) is convex. This result serves as the motivation behind exploring the
convexity of right-closed sets.
Right-closed set; convexity; integer convexity; polyhedral theory; Petri Nets; Liveness
Thu, 16 Jul 2015 00:00:00 GMThttp://hdl.handle.net/2142/880562015-07-16T00:00:00ZSalimi, Ehsan