Matching Items (52)
Description
With the substantial development of intelligent robots, human-robot interaction (HRI) has become ubiquitous in applications such as collaborative manufacturing, surgical robotic operations, and autonomous driving. In all these applications, a human behavior model, which can provide predictions of human actions, is a helpful reference that helps robots to achieve intelligent interaction with humans. The requirement elicits an essential problem of how to properly model human behavior, especially when individuals are interacting or cooperating with each other. The major objective of this thesis is to utilize the human intention decoding method to help robots enhance their performance while interacting with humans. Preliminary work on integrating human intention estimation with an HRI scenario is shown to demonstrate the benefit. In order to achieve this goal, the research topic is divided into three phases. First, a novel method of an online measure of the human's reliance on the robot, which can be estimated through the intention decoding process from human actions，is described. An experiment that requires human participants to complete an object-moving task with a robot manipulator was conducted under different conditions of distractions. A relationship is discovered between human intention and trust while participants performed a familiar task with no distraction. This finding suggests a relationship between the psychological construct of trust and joint physical coordination, which bridges the human's action to its mental states. Then, a novel human collaborative dynamic model is introduced based on game theory and bounded rationality, which is a novel method to describe human dyadic behavior with the aforementioned theories. The mutual intention decoding process was also considered to inform this model. Through this model, the connection between the mental states of the individuals to their cooperative actions is indicated. A haptic interface is developed with a virtual environment and the experiments are conducted with 30 human subjects. The result suggests the existence of mutual intention decoding during the human dyadic cooperative behaviors. Last, the empirical results show that allowing agents to have empathy in inference, which lets the agents understand that others might have a false understanding of their intentions, can help to achieve correct intention inference. It has been verified that knowledge about vehicle dynamics was also important to correctly infer intentions. A new courteous policy is proposed that bounded the courteous motion using its inferred set of equilibrium motions. A simulation, which is set to reproduce an intersection passing case between an autonomous car and a human driving car, is conducted to demonstrate the benefit of the novel courteous control policy.
ContributorsWang, Yiwei (Author) / Zhang, Wenlong (Thesis advisor) / Berman, Spring (Committee member) / Lee, Hyunglae (Committee member) / Ren, Yi (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2021
Description
In the development of autonomous ground vehicles (AGVs), how to guarantee vehicle lateral stability is one of the most critical aspects. Based on nonlinear vehicle lateral and tire dynamics, new driving requirements of AGVs demand further studies and analyses of vehicle lateral stability control strategies. To achieve comprehensive analyses and stability-guaranteed vehicle lateral driving control, this dissertation presents three main contributions.First, a new method is proposed to estimate and analyze vehicle lateral driving stability regions, which provide a direct and intuitive demonstration for stability control of AGVs. Based on a four-wheel vehicle model and a nonlinear 2D analytical LuGre tire model, a local linearization method is applied to estimate vehicle lateral driving stability regions by analyzing vehicle local stability at each operation point on a phase plane. The obtained stability regions are conservative because both vehicle and tire stability are simultaneously considered. Such a conservative feature is specifically important for characterizing the stability properties of AGVs.
Second, to analyze vehicle stability, two novel features of the estimated vehicle lateral driving stability regions are studied. First, a shifting vector is formulated to explicitly describe the shifting feature of the lateral stability regions with respect to the vehicle steering angles. Second, dynamic margins of the stability regions are formulated and applied to avoid the penetration of vehicle state trajectory with respect to the region boundaries. With these two features, the shiftable stability regions are feasible for real-time stability analysis.
Third, to keep the vehicle states (lateral velocity and yaw rate) always stay in the shiftable stability regions, different control methods are developed and evaluated. Based on different vehicle control configurations, two dynamic sliding mode controllers (SMC) are designed. To better control vehicle stability without suffering chattering issues in SMC, a non-overshooting model predictive control is proposed and applied. To further save computational burden for real-time implementation, time-varying control-dependent invariant sets and time-varying control-dependent barrier functions are proposed and adopted in a stability-guaranteed vehicle control problem.
Finally, to validate the correctness and effectiveness of the proposed theories, definitions, and control methods, illustrative simulations and experimental results are presented and discussed.
ContributorsHuang, Yiwen (Author) / Chen, Yan (Thesis advisor) / Lee, Hyunglae (Committee member) / Ren, Yi (Committee member) / Yong, Sze Zheng (Committee member) / Zhang, Wenlong (Committee member) / Arizona State University (Publisher)
Created2021
Description
In convective heat transfer processes, heat transfer rate increases generally with a large fluid velocity, which leads to complex flow patterns. However, numerically analyzing the complex transport process and conjugated heat transfer requires extensive time and computing resources. Recently, data-driven approach has risen as an alternative method to solve physical problems in a computational efficient manner without necessitating the iterative computations of the governing physical equations. However, the research on data-driven approach for convective heat transfer is still in nascent stage. This study aims to introduce data-driven approaches for modeling heat and mass convection phenomena. As the first step, this research explores a deep learning approach for modeling the internal forced convection heat transfer problems. Conditional generative adversarial networks (cGAN) are trained to predict the solution based on a graphical input describing fluid channel geometries and initial flow conditions. A trained cGAN model rapidly approximates the flow temperature, Nusselt number (Nu) and friction factor (f) of a flow in a heated channel over Reynolds number (Re) ranging from 100 to 27750. The optimized cGAN model exhibited an accuracy up to 97.6% when predicting the local distributions of Nu and f. Next, this research introduces a deep learning based surrogate model for three-dimensional (3D) transient mixed convention in a horizontal channel with a heated bottom surface. Conditional generative adversarial networks (cGAN) are trained to approximate the temperature maps at arbitrary channel locations and time steps. The model is developed for a mixed convection occurring at the Re of 100, Rayleigh number of 3.9E6, and Richardson number of 88.8. The cGAN with the PatchGAN based classifier without the strided convolutions infers the temperature map with the best clarity and accuracy.
Finally, this study investigates how machine learning analyzes the mass transfer in 3D printed fluidic devices. Random forests algorithm is hired to classify the flow images taken from semi-transparent 3D printed tubes. Particularly, this work focuses on laminar-turbulent transition process occurring in a 3D wavy tube and a straight tube visualized by dye injection. The machine learning model automatically classifies experimentally obtained flow images with an accuracy > 0.95.
ContributorsKang, Munku (Author) / Kwon, Beomjin (Thesis advisor) / Phelan, Patrick (Committee member) / Ren, Yi (Committee member) / Rykaczewski, Konrad (Committee member) / Sohn, SungMin (Committee member) / Arizona State University (Publisher)
Created2022
Description
Autonomous systems inevitably must interact with other surrounding systems; thus, algorithms for intention/behavior estimation are of great interest. This thesis dissertation focuses on developing passive and active model discrimination algorithms (PMD and AMD) with applications to set-valued intention identification and fault detection for uncertain/bounded-error dynamical systems. PMD uses the obtained input-output data to invalidate the models, while AMD designs an auxiliary input to assist the discrimination process. First, PMD algorithms are proposed for noisy switched nonlinear systems constrained by metric/signal temporal logic specifications, including systems with lossy data modeled by (m,k)-firm constraints. Specifically, optimization-based algorithms are introduced for analyzing the detectability/distinguishability of models and for ruling out models that are inconsistent with observations at run time. On the other hand, two AMD approaches are designed for noisy switched nonlinear models and piecewise affine inclusion models, which involve bilevel optimization with integer variables/constraints in the inner/lower level. The first approach solves the inner problem using mixed-integer parametric optimization, whose solution is included when solving the outer problem/higher level, while the second approach moves the integer variables/constraints to the outer problem in a manner that retains feasibility and recasts the problem as a tractable mixed-integer linear programming (MILP). Furthermore, AMD algorithms are proposed for noisy discrete-time affine time-invariant systems constrained by disjunctive and coupled safety constraints. To overcome the issues associated with generalized semi-infinite constraints due to state-dependent input constraints and disjunctive safety constraints, several constraint reformulations are proposed to recast the AMD problems as tractable MILPs. Finally, partition-based AMD approaches are proposed for noisy discrete-time affine time-invariant models with model-independent parameters and output measurement that are revealed at run time. Specifically, algorithms with fixed and adaptive partitions are proposed, where the latter improves on the performance of the former by allowing the partitions to be optimized. By partitioning the operation region, the problem is solved offline, and partition trees are constructed which can be used as a `look-up table' to determine the optimal input depending on revealed information at run time.
ContributorsNiu, Ruochen (Author) / Yong, Sze Zheng S.Z. (Thesis advisor) / Berman, Spring (Committee member) / Ren, Yi (Committee member) / Zhang, Wenlong (Committee member) / Zhuang, Houlong (Committee member) / Arizona State University (Publisher)
Created2022
Description
The increasing availability of data and advances in computation have spurred the development of data-driven approaches for modeling complex dynamical systems. These approaches are based on the idea that the underlying structure of a complex system can be discovered from data using mathematical and computational techniques. They also show promise for addressing the challenges of modeling high-dimensional, nonlinear systems with limited data. In this research expository, the state of the art in data-driven approaches for modeling complex dynamical systems is surveyed in a systemic way. First the general formulation of data-driven modeling of dynamical systems is discussed. Then several representative methods in feature engineering and system identification/prediction are reviewed, including recent advances and key challenges.
ContributorsShi, Wenlong (Author) / Ren, Yi (Thesis advisor) / Hong, Qijun (Committee member) / Jiao, Yang (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2022
Description
The need for autonomous cars has never been more vital, and for a vehicle to be completely autonomous, multiple components must work together, one of which is the capacity to park at the end of a mission. This thesis project aims to design and execute an automated parking assist system (APAS). Traditional Automated parking assist systems (APAS) may not be effective in some constrained urban parking environments because of the parking space dimension. The thesis proposes a novel four-wheel steering (4-WS) vehicle for automated parallel parking to overcome this kind of challenge. Then, benefiting from the maneuverability enabled by the 4WS system, the feasible initial parking area is vastly expanded from those for the conventional 2WS vehicles. In addition, the expanded initial area is divided into four areas where different paths are planned correspondingly. In the proposed novel APAS first, a suitable parking space is identified through ultra-sonic sensors, which are mounted around the vehicle, and then depending upon the vehicle's initial position, various compact and smooth parallel parking paths are generated. An optimization function is built to get the smoothest (i.e., the smallest steering angle change and the shortest path) parallel parking path. With the full utilization of the 4WS system, the proposed path planning algorithm can allow a larger initial parking area that can be easily tracked by the 4WS vehicles. The proposed APAS for 4WS vehicles makes the automatic parking process in restricted spaces efficient. To verify the feasibility and effectiveness of the proposed APAS, a 4WS vehicle prototype is applied for validation through both simulation and experiment results.
ContributorsGujarathi, Kaushik Kumar (Author) / Chen, Yan (Thesis advisor) / Yong, Sze Zheng (Committee member) / Ren, Yi (Committee member) / Arizona State University (Publisher)
Created2022
Description
The distribution and transport of mercury in the human body are poorly constrained. For instance, the long-term persistence and intra-individual distribution of mercury in bones from dental amalgams or environmental exposure have not been studied. A robust method validated for accuracy and precision specifically for mercury in human bones would facilitate studies of mercury in anthropological, forensic, and medical studies. I present a highly precise, accurate mercury concentration analytical method targeted to human bone samples. This method uses commercially commonly available and reliable instruments that are not limited to elemental Hg analysis. This method requires significantly lower sample amounts than existing methods because it has a much lower limit of detection compared to the best mercury analyzers on the market and other analytical methods. With the low limit of detection achieved, this mercury concentration protocol is an excellent fit for studies with a limited amount of samples for destructive analysis. I then use this method to analyze the mercury concentration distribution in modern skeletal collections provided by three U.S. anthropological research facilities. Mercury concentration and distribution were analyzed from 35 donors’ skeletons with 18 different skeletal elements (bones) per donor to evaluate both the intra-individual and inter-individual variation in mercury concentration. Considered factors include geological differences in decomposition sites and the presence of dental amalgam filling. Geological differences in decomposition sites did not statistically affect the mercury concentration in the donor’s skeleton. The presence of dental amalgam significantly affected the inter-individual and intra-individual mercury concentration variation in donors’ skeletal samples. Individuals who had dental amalgam had significantly higher mercury concentration in their skeleton compared to individuals who did not have dental amalgam (p-value <0.01). Mercury concentration in the mandible, occipital bone, patella, and proximal phalanx (foot) was significantly affected by the presence of dental amalgam.
ContributorsRen, Yi (Author) / Gordon, Gwyneth GG (Thesis advisor) / Anbar, Ariel AD (Thesis advisor) / Shock, Everett ES (Committee member) / Knudson, Kelly KJ (Committee member) / Arizona State University (Publisher)
Created2022
Description
Least squares fitting in 3D is applied to produce higher level geometric parameters that describe the optimum location of a line-profile through many nodal points that are derived from Finite Element Analysis (FEA) simulations of elastic spring-back of features both on stamped sheet metal components after they have been plasticly deformed in a press and released, and on simple assemblies made from them. Although the traditional Moore-Penrose inverse was used to solve the superabundant linear equations, the formulation of these equations was distinct and based on virtual work and statics applied to parallel-actuated robots in order to allow for both more complex profiles and a change in profile size. The output, a small displacement torsor (SDT) is used to describe the displacement of the profile from its nominal location. It may be regarded as a generalization of the slope and intercept parameters of a line which result from a Gauss-Markov regression fit of points in a plane. Additionally, minimum zone-magnitudes were computed that just capture the points along the profile. And finally, algorithms were created to compute simple parameters for cross-sectional shapes of components were also computed from sprung-back data points according to the protocol of simulations and benchmark experiments conducted by the metal forming community 30 years ago, although it was necessary to modify their protocol for some geometries that differed from the benchmark.
ContributorsSunkara, Sai Chandu (Author) / Davidson, Joseph (Thesis advisor) / Shah, Jami (Committee member) / Ren, Yi (Committee member) / Arizona State University (Publisher)
Created2023
Description
We propose a new strategy for blackjack, BB-Player, which leverages Hidden Markov Models (HMMs) in online planning to sample a normalized predicted deck distribution for a partially-informed distance heuristic. Viterbi learning is applied to the most-likely sampled future sequence in each game state to generate transition and emission matrices for this upcoming sequence. These are then iteratively updated with each observed game on a given deck. Ultimately, this process informs a heuristic to estimate the true symbolic distance left, which allows BB-Player to determine the action with the highest likelihood of winning (by opponent bust or blackjack) and not going bust. We benchmark this strategy against six common card counting strategies from three separate levels of difficulty and a randomized action strategy. On average, BB-Player is observed to beat card-counting strategies in win optimality, attaining a 30.00% expected win percentage, though it falls short of beating state-of-the-art methods.
ContributorsLakamsani, Sreeharsha (Author) / Ren, Yi (Thesis director) / Lee, Heewook (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor)
Created2023-05
Description
Ultra-fast 2D/3D material microstructure reconstruction and quantitative structure-property mapping are crucial components of integrated computational material engineering (ICME). It is particularly challenging for modeling random heterogeneous materials such as alloys, composites, polymers, porous media, and granular matters, which exhibit strong randomness and variations of their material properties due to the hierarchical uncertainties associated with their complex microstructure at different length scales. Such uncertainties also exist in disordered hyperuniform systems that are statistically isotropic and possess no Bragg peaks like liquids and glasses, yet they suppress large-scale density fluctuations in a similar manner as in perfect crystals. The unique hyperuniform long-range order in these systems endow them with nearly optimal transport, electronic and mechanical properties. The concept of hyperuniformity was originally introduced for many-particle systems and has subsequently been generalized to heterogeneous materials such as porous media, composites, polymers, and biological tissues for unconventional property discovery. An explicit mixture random field (MRF) model is proposed to characterize and reconstruct multi-phase stochastic material property and microstructure simultaneously, where no additional tuning step nor iteration is needed compared with other stochastic optimization approaches such as the simulated annealing. The proposed method is shown to have ultra-high computational efficiency and only requires minimal imaging and property input data. Considering microscale uncertainties, the material reliability will face the challenge of high dimensionality. To deal with the so-called “curse of dimensionality”, efficient material reliability analysis methods are developed. Then, the explicit hierarchical uncertainty quantification model and efficient material reliability solvers are applied to reliability-based topology optimization to pursue the lightweight under reliability constraint defined based on structural mechanical responses. Efficient and accurate methods for high-resolution microstructure and hyperuniform microstructure reconstruction, high-dimensional material reliability analysis, and reliability-based topology optimization are developed. The proposed framework can be readily incorporated into ICME for probabilistic analysis, discovery of novel disordered hyperuniform materials, material design and optimization.
ContributorsGao, Yi (Author) / Liu, Yongming (Thesis advisor) / Jiao, Yang (Committee member) / Ren, Yi (Committee member) / Pan, Rong (Committee member) / Mignolet, Marc (Committee member) / Arizona State University (Publisher)
Created2021