高精度3次元近距離レーダに関する研究

Tohoku University佐藤源之課

Robust Impedance Control of a Four Degree of Freedom Exercise Robot

The CSU 4OptimX exercise robot provides a platform for future research into advanced exercise and rehabilitation. The robot and its control system will autonomously modify reference trajectories and impedances on the basis of an optimization criterion and physiological feedback. To achieve this goal, a robust impedance control system with trajectory tracking must be implemented as the foundational control scheme. Two control laws will be compared, sliding mode and H-infinity control. The above robust control laws are combined with underlying impedance control laws to overcome uncertain plant model parameters and disturbance anomalies affecting the input signal. The sliding mode control law is synthesized based on a nominal plant model due to its inherent nature of overcoming unspecified, un-modeled dynamics and disturbances. Implementation of the H-infinity control law uses weights as well as the nominal plant, a structured parametric uncertainty model of the plant, and a model with multiplicative uncertainty. The performance and practicality of each controller is discussed as well as the challenges associated with attempts to implement controllers successfully onto the robot. The findings of this thesis indicate that the closed loop controller with sliding mode is the superior control scheme due to its abilities to counter non-linearities. It is chosen as the platform control scheme. The 2 out of 3 H-infinity controllers performed well in simulation but only one was able to successfully control the robot. Challenges associated with H-infinity control implementation toward impedance control include determining proper weight shapes that balance performance and practicality. This challenge is a starting point for future research into general weight shape determination for H-infinity robust impedance control

A Systematic Survey of Control Techniques and Applications: From Autonomous Vehicles to Connected and Automated Vehicles

Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger comfort, transportation efficiency, and energy saving. This survey attempts to provide a comprehensive and thorough overview of the current state of vehicle control technology, focusing on the evolution from vehicle state estimation and trajectory tracking control in AVs at the microscopic level to collaborative control in CAVs at the macroscopic level. First, this review starts with vehicle key state estimation, specifically vehicle sideslip angle, which is the most pivotal state for vehicle trajectory control, to discuss representative approaches. Then, we present symbolic vehicle trajectory tracking control approaches for AVs. On top of that, we further review the collaborative control frameworks for CAVs and corresponding applications. Finally, this survey concludes with a discussion of future research directions and the challenges. This survey aims to provide a contextualized and in-depth look at state of the art in vehicle control for AVs and CAVs, identifying critical areas of focus and pointing out the potential areas for further exploration

Sparse and low rank approximations for action recognition

Action recognition is crucial area of research in computer vision with wide range of applications in surveillance, patient-monitoring systems, video indexing, Human- Computer Interaction and many more. These applications require automated action recognition. Robust classification methods are sought-after despite influential research in this field over past decade. The data resources have grown tremendously owing to the advances in the digital revolution which cannot be compared to the meagre resources in the past. The main limitation on a system when dealing with video data is the computational burden due to large dimensions and data redundancy. Sparse and low rank approximation methods have evolved recently which aim at concise and meaningful representation of data. This thesis explores the application of sparse and low rank approximation methods in the context of video data classification with the following contributions. 1. An approach for solving the problem of action and gesture classification is proposed within the sparse representation domain, effectively dealing with large feature dimensions, 2. Low rank matrix completion approach is proposed to jointly classify more than one action 3. Deep features are proposed for robust classification of multiple actions within matrix completion framework which can handle data deficiencies. This thesis starts with the applicability of sparse representations based classifi- cation methods to the problem of action and gesture recognition. Random projection is used to reduce the dimensionality of the features. These are referred to as compressed features in this thesis. The dictionary formed with compressed features has proved to be efficient for the classification task achieving comparable results to the state of the art. Next, this thesis addresses the more promising problem of simultaneous classifi- cation of multiple actions. This is treated as matrix completion problem under transduction setting. Matrix completion methods are considered as the generic extension to the sparse representation methods from compressed sensing point of view. The features and corresponding labels of the training and test data are concatenated and placed as columns of a matrix. The unknown test labels would be the missing entries in that matrix. This is solved using rank minimization techniques based on the assumption that the underlying complete matrix would be a low rank one. This approach has achieved results better than the state of the art on datasets with varying complexities. This thesis then extends the matrix completion framework for joint classification of actions to handle the missing features besides missing test labels. In this context, deep features from a convolutional neural network are proposed. A convolutional neural network is trained on the training data and features are extracted from train and test data from the trained network. The performance of the deep features has proved to be promising when compared to the state of the art hand-crafted features

Modeling Complex Biological and Mechanical Movements: Applications to Animal Locomotion and Gesture Classification in Robotic Surgery

Mutual interaction between biology and robots can significantly benefit both fields. The richness and diversity in animal locomotion and movement provides an extensive resource for inspiration in engineering design of robots. On the other hand, bio-mimetic and bio-inspired robots play a critical role in testing hypotheses in biology and neuromechanics. Modeling complex biological and mechanical movements is at the core of this mutual interaction. Models and analytical tools are required for decoding and analysis of behavior in biological and mechanical systems, both at low level (sensory systems and control) and high level (activity recognition). This dissertation is focused on modeling approaches for biological and mechanical movements. We first primarily focus on physics-based template modeling to answer a long-standing question in animal locomotion: why do animals often produce substantial forces in directions that do not directly contribute to movement? We examine the weakly electric knifefish, a well-suited model system to investigate the relationship between mutually opposing forces and locomotor control. We use slow-motion videography to study the ribbon-fin motion and develop a physics-based template model at the task-level for tracking behavior. Using the developed physics-based model integrated with experiments with a biomimetic robot, we demonstrate that the production and differential control of mutually opposing forces is a strategy that generates passive stabilization while simultaneously enhancing maneuverability, thereby simplifies neural control. The second part of this work aims to propose a more general data-driven system-theoretic framework for decoding complex behaviors. Specifically we introduce a new class of linear time-invariant dynamical systems with sparse inputs (LDS-SI). In the proposed framework, at each time instant, the input to the system is sparse with respect to a dictionary of inputs. In the context of complex behaviors, the dictionary may represent the dictionary of inputs for all possible simple behaviors. We propose a convex optimization formulation for the state estimation with unknown inputs in LDS-SI. We derive sufficient conditions for the perfect joint recovery and explore the results with simulation. We demonstrate the power of the proposed framework in the analysis of complex gestures in robotic surgery. Results are better than state-of-the-art methods in joint segmentation and classification of surgical gestures in a dataset of suturing task trials performed by different surgeons

Autologistic network model on binary data for disease progression study

This paper focuses on analysis of spatiotemporal binary data with absorbing states. The research was motivated by a clinical study on amyotrophic lateral sclerosis (ALS), a neurological disease marked by gradual loss of muscle strength over time in multiple body regions. We propose an autologistic regression model to capture complex spatial and temporal dependencies in muscle strength among different muscles. As it is not clear how the disease spreads from one muscle to another, it may not be reasonable to define a neighborhood structure based on spatial proximity. Relaxing the requirement for prespecification of spatial neighborhoods as in existing models, our method identifies an underlying network structure empirically to describe the pattern of spreading disease. The model also allows the network autoregressive effects to vary depending on the muscles’ previous status. Based on the joint distribution derived from this autologistic model, the joint transition probabilities of responses among locations can be estimated and the disease status can be predicted in the next time interval. Model parameters are estimated through maximization of penalized pseudo‐likelihood. Postmodel selection inference was conducted via a bias‐correction method, for which the asymptotic distributions were derived. Simulation studies were conducted to evaluate the performance of the proposed method. The method was applied to the analysis of muscle strength loss from the ALS clinical study.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152664/1/biom13111.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152664/2/biom13111-sup-0001-autolog_supp-biom.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152664/3/biom13111-sup-0003-supmat.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152664/4/biom13111_am.pd