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- Genre: Doctoral Dissertation
- Member of: Theses and Dissertations
Description
Robotic systems are outmatched by the abilities of the human hand to perceive and manipulate the world. Human hands are able to physically interact with the world to perceive, learn, and act to accomplish tasks. Limitations of robotic systems to interact with and manipulate the world diminish their usefulness. In order to advance robot end effectors, specifically artificial hands, rich multimodal tactile sensing is needed. In this work, a multi-articulating, anthropomorphic robot testbed was developed for investigating tactile sensory stimuli during finger-object interactions. The artificial finger is controlled by a tendon-driven remote actuation system that allows for modular control of any tendon-driven end effector and capabilities for both speed and strength. The artificial proprioception system enables direct measurement of joint angles and tendon tensions while temperature, vibration, and skin deformation are provided by a multimodal tactile sensor. Next, attention was focused on real-time artificial perception for decision-making. A robotic system needs to perceive its environment in order to make decisions. Specific actions such as “exploratory procedures” can be employed to classify and characterize object features. Prior work on offline perception was extended to develop an anytime predictive model that returns the probability of having touched a specific feature of an object based on minimally processed sensor data. Developing models for anytime classification of features facilitates real-time action-perception loops. Finally, by combining real-time action-perception with reinforcement learning, a policy was learned to complete a functional contour-following task: closing a deformable ziplock bag. The approach relies only on proprioceptive and localized tactile data. A Contextual Multi-Armed Bandit (C-MAB) reinforcement learning algorithm was implemented to maximize cumulative rewards within a finite time period by balancing exploration versus exploitation of the action space. Performance of the C-MAB learner was compared to a benchmark Q-learner that eventually returns the optimal policy. To assess robustness and generalizability, the learned policy was tested on variations of the original contour-following task. The work presented contributes to the full range of tools necessary to advance the abilities of artificial hands with respect to dexterity, perception, decision-making, and learning.
ContributorsHellman, Randall Blake (Author) / Santos, Veronica J (Thesis advisor) / Artemiadis, Panagiotis K (Committee member) / Berman, Spring (Committee member) / Helms Tillery, Stephen I (Committee member) / Fainekos, Georgios (Committee member) / Arizona State University (Publisher)
Created2016
Description
Millions of individuals suffer from gait impairments due to stroke or other neurological disorders. A primary goal of patients is to walk independently, but most patients only achieve a poor functional outcome five years after injury. Despite the growing interest in using robotic devices for rehabilitation of sensorimotor function, state-of-the-art robotic interventions in gait therapy have not resulted in improved outcomes when compared to traditional treadmill-based therapy. Because bipedal walking requires neural coupling and dynamic interactions between the legs, a fundamental understanding of the sensorimotor mechanisms of inter-leg coordination during walking is needed to inform robotic interventions in gait therapy. This dissertation presents a systematic exploration of sensorimotor mechanisms of inter-leg coordination by studying the effect of unilateral perturbations of the walking surface stiffness on contralateral muscle activation in healthy populations. An analysis of the contribution of several sensory modalities to the muscle activation of the opposite leg provides new insight into the sensorimotor control mechanisms utilized in human walking, including the role of supra-spinal neural circuits in inter-leg coordination. Based on these insights, a model is created which relates the unilateral deflection of the walking surface to the resulting neuromuscular activation in the opposite leg. Additionally, case studies with hemiplegic walkers indicate the existence of the observed mechanism in neurologically impaired walkers. The results of this dissertation suggest a novel approach to gait therapy for hemiplegic patients in which desired muscle activity is evoked in the impaired leg by only interacting with the healthy leg. One of the most significant advantages of this approach over current rehabilitation protocols is the safety of the patient since there is no direct manipulation of the impaired leg. Therefore, the methods and results presented in this dissertation represent a potential paradigm shift in robot-assisted gait therapy.
ContributorsSkidmore, Jeffrey Alan (Author) / Artemiadis, Panagiotis (Thesis advisor) / Santello, Marco (Committee member) / Berman, Spring (Committee member) / Lee, Hyunglae (Committee member) / Marvi, Hamidreza (Committee member) / Arizona State University (Publisher)
Created2017
Description
Vehicles traverse granular media through complex reactions with large numbers of small particles. Many approaches rely on empirical trends derived from wheeled vehicles in well-characterized media. However, the environments of numerous bodies such as Mars or the moon are primarily composed of fines called regolith which require different design considerations. This dissertation discusses research aimed at understanding the role and function of empirical, computational, and theoretical granular physics approaches as they apply to helical geometries, their envelope of applicability, and the development of new laws. First, a static Archimedes screw submerged in granular material (glass beads) is analyzed using two methods: Granular Resistive Force Theory (RFT), an empirically derived set of equations based on fluid dynamic superposition principles, and Discrete element method (DEM) simulations, a particle modeling software. Dynamic experiments further confirm the computational method with multi-body dynamics (MBD)-DEM co-simulations. Granular Scaling Laws (GSL), a set of physics relationships based on non-dimensional analysis, are utilized for the gravity-modified environments. A testing chamber to contain a lunar analogue, BP-1, is developed and built. An investigation of straight and helical grousered wheels in both silica sand and BP-1 is performed to examine general GSL applicability for lunar purposes. Mechanical power draw and velocity prediction by GSL show non-trivial but predictable deviation. BP-1 properties are characterized and applied to an MBD-DEM environment for the first time. MBD-DEM simulation results between Earth gravity and lunar gravity show good agreement with theoretical predictions for both power and velocity. The experimental deviation is further investigated and found to have a mass-dependant component driven by granular sinkage and engagement. Finally, a robust set of helical granular scaling laws (HGSL) are derived. The granular dynamics scaling of three-dimensional screw-driven mobility is reduced to a similar theory as wheeled scaling laws, provided the screw is radially continuous. The new laws are validated in BP-1 with results showing very close agreement to predictions. A gravity-variant version of these laws is validated with MBD-DEM simulations. The results of the dissertation suggest GSL, HGSL, and MBD-DEM give reasonable approximations for use in lunar environments to predict rover mobility given adequate granular engagement.
ContributorsThoesen, Andrew Lawrence (Author) / Marvi, Hamidreza (Thesis advisor) / Berman, Spring (Committee member) / Emady, Heather (Committee member) / Lee, Hyunglae (Committee member) / Klesh, Andrew (Committee member) / Arizona State University (Publisher)
Created2019
Description
For the last 50 years, oscillator modeling in ranging systems has received considerable
attention. Many components in a navigation system, such as the master oscillator
driving the receiver system, as well the master oscillator in the transmitting system
contribute significantly to timing errors. Algorithms in the navigation processor must
be able to predict and compensate such errors to achieve a specified accuracy. While
much work has been done on the fundamentals of these problems, the thinking on said
problems has not progressed. On the hardware end, the designers of local oscillators
focus on synthesized frequency and loop noise bandwidth. This does nothing to
mitigate, or reduce frequency stability degradation in band. Similarly, there are not
systematic methods to accommodate phase and frequency anomalies such as clock
jumps. Phase locked loops are fundamentally control systems, and while control
theory has had significant advancement over the last 30 years, the design of timekeeping
sources has not advanced beyond classical control. On the software end,
single or two state oscillator models are typically embedded in a Kalman Filter to
alleviate time errors between the transmitter and receiver clock. Such models are
appropriate for short term time accuracy, but insufficient for long term time accuracy.
Additionally, flicker frequency noise may be present in oscillators, and it presents
mathematical modeling complications. This work proposes novel H∞ control methods
to address the shortcomings in the standard design of time-keeping phase locked loops.
Such methods allow the designer to address frequency stability degradation as well
as high phase/frequency dynamics. Additionally, finite-dimensional approximants of
flicker frequency noise that are more representative of the truth system than the
tradition Gauss Markov approach are derived. Last, to maintain timing accuracy in
a wide variety of operating environments, novel Banks of Adaptive Extended Kalman
Filters are used to address both stochastic and dynamic uncertainty.
attention. Many components in a navigation system, such as the master oscillator
driving the receiver system, as well the master oscillator in the transmitting system
contribute significantly to timing errors. Algorithms in the navigation processor must
be able to predict and compensate such errors to achieve a specified accuracy. While
much work has been done on the fundamentals of these problems, the thinking on said
problems has not progressed. On the hardware end, the designers of local oscillators
focus on synthesized frequency and loop noise bandwidth. This does nothing to
mitigate, or reduce frequency stability degradation in band. Similarly, there are not
systematic methods to accommodate phase and frequency anomalies such as clock
jumps. Phase locked loops are fundamentally control systems, and while control
theory has had significant advancement over the last 30 years, the design of timekeeping
sources has not advanced beyond classical control. On the software end,
single or two state oscillator models are typically embedded in a Kalman Filter to
alleviate time errors between the transmitter and receiver clock. Such models are
appropriate for short term time accuracy, but insufficient for long term time accuracy.
Additionally, flicker frequency noise may be present in oscillators, and it presents
mathematical modeling complications. This work proposes novel H∞ control methods
to address the shortcomings in the standard design of time-keeping phase locked loops.
Such methods allow the designer to address frequency stability degradation as well
as high phase/frequency dynamics. Additionally, finite-dimensional approximants of
flicker frequency noise that are more representative of the truth system than the
tradition Gauss Markov approach are derived. Last, to maintain timing accuracy in
a wide variety of operating environments, novel Banks of Adaptive Extended Kalman
Filters are used to address both stochastic and dynamic uncertainty.
ContributorsEchols, Justin A (Author) / Bliss, Daniel W (Thesis advisor) / Tsakalis, Konstantinos S (Committee member) / Berman, Spring (Committee member) / Mittelmann, Hans (Committee member) / Arizona State University (Publisher)
Created2020
Description
The problem of modeling and controlling the distribution of a multi-agent system has recently evolved into an interdisciplinary effort. When the agent population is very large, i.e., at least on the order of hundreds of agents, it is important that techniques for analyzing and controlling the system scale well with the number of agents. One scalable approach to characterizing the behavior of a multi-agent system is possible when the agents' states evolve over time according to a Markov process. In this case, the density of agents over space and time is governed by a set of difference or differential equations known as a {\it mean-field model}, whose parameters determine the stochastic control policies of the individual agents. These models often have the advantage of being easier to analyze than the individual agent dynamics. Mean-field models have been used to describe the behavior of chemical reaction networks, biological collectives such as social insect colonies, and more recently, swarms of robots that, like natural swarms, consist of hundreds or thousands of agents that are individually limited in capability but can coordinate to achieve a particular collective goal.
This dissertation presents a control-theoretic analysis of mean-field models for which the agent dynamics are governed by either a continuous-time Markov chain on an arbitrary state space, or a discrete-time Markov chain on a continuous state space. Three main problems are investigated. First, the problem of stabilization is addressed, that is, the design of transition probabilities/rates of the Markov process (the agent control parameters) that make a target distribution, satisfying certain conditions, invariant. Such a control approach could be used to achieve desired multi-agent distributions for spatial coverage and task allocation. However, the convergence of the multi-agent distribution to the designed equilibrium does not imply the convergence of the individual agents to fixed states. To prevent the agents from continuing to transition between states once the target distribution is reached, and thus potentially waste energy, the second problem addressed within this dissertation is the construction of feedback control laws that prevent agents from transitioning once the equilibrium distribution is reached. The third problem addressed is the computation of optimized transition probabilities/rates that maximize the speed at which the system converges to the target distribution.
This dissertation presents a control-theoretic analysis of mean-field models for which the agent dynamics are governed by either a continuous-time Markov chain on an arbitrary state space, or a discrete-time Markov chain on a continuous state space. Three main problems are investigated. First, the problem of stabilization is addressed, that is, the design of transition probabilities/rates of the Markov process (the agent control parameters) that make a target distribution, satisfying certain conditions, invariant. Such a control approach could be used to achieve desired multi-agent distributions for spatial coverage and task allocation. However, the convergence of the multi-agent distribution to the designed equilibrium does not imply the convergence of the individual agents to fixed states. To prevent the agents from continuing to transition between states once the target distribution is reached, and thus potentially waste energy, the second problem addressed within this dissertation is the construction of feedback control laws that prevent agents from transitioning once the equilibrium distribution is reached. The third problem addressed is the computation of optimized transition probabilities/rates that maximize the speed at which the system converges to the target distribution.
ContributorsBiswal, Shiba (Author) / Berman, Spring (Thesis advisor) / Fainekos, Georgios (Committee member) / Lanchier, Nicolas (Committee member) / Mignolet, Marc (Committee member) / Peet, Matthew (Committee member) / Arizona State University (Publisher)
Created2020