Towards Robot-aided Gait Rehabilitation and Assistance via Characterization and Estimation of Human Locomotion

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Description
Walking and mobility are essential aspects of our daily lives, enabling us to engage in various activities. Gait disorders and impaired mobility are widespread challenges faced by older adults and people with neurological injuries, as these conditions can significantly impact

Walking and mobility are essential aspects of our daily lives, enabling us to engage in various activities. Gait disorders and impaired mobility are widespread challenges faced by older adults and people with neurological injuries, as these conditions can significantly impact their quality of life, leading to a loss of independence and an increased risk of mortality. In response to these challenges, rehabilitation, and assistive robotics have emerged as promising alternatives to conventional gait therapy, offering potential solutions that are less labor-intensive and costly. Despite numerous advances in wearable lower-limb robotics, their current applicability remains confined to laboratory settings. To expand their utility to broader gait impairments and daily living conditions, there is a pressing need for more intelligent robot controllers. In this dissertation, these challenges are tackled from two perspectives: First, to improve the robot's understanding of human motion and intentions which is crucial for assistive robot control, a robust human locomotion estimation technique is presented, focusing on measuring trunk motion. Employing an invariant extended Kalman filtering method that takes sensor misplacement into account, improved convergence properties over the existing methods for different locomotion modes are shown. Secondly, to enhance safe and effective robot-aided gait training, this dissertation proposes to directly learn from physical therapists' demonstrations of manual gait assistance in post-stroke rehabilitation. Lower-limb kinematics of patients and assistive force applied by therapists to the patient's leg are measured using a wearable sensing system which includes a custom-made force sensing array. The collected data is then used to characterize a therapist's strategies. Preliminary analysis indicates that knee extension and weight-shifting play pivotal roles in shaping a therapist's assistance strategies, which are then incorporated into a virtual impedance model that effectively captures high-level therapist behaviors throughout a complete training session. Furthermore, to introduce safety constraints in the design of such controllers, a safety-critical learning framework is explored through theoretical analysis and simulations. A safety filter incorporating an online iterative learning component is introduced to bring robust safety guarantees for gait robotic assistance and training, addressing challenges such as stochasticity and the absence of a known prior dynamic model.
Date Created
2023
Agent

Dynamic Modeling, Robust Control and Contact Estimation of Soft Robotics

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Description
Soft robotics has garnered attention for its substantial prospective in various domains, such as manipulation and interactions with humans, by offering competitive advantages against rigid robotic systems, including inherent compliance and variable stiffness. Despite these benefits, their theoretically infinite degrees

Soft robotics has garnered attention for its substantial prospective in various domains, such as manipulation and interactions with humans, by offering competitive advantages against rigid robotic systems, including inherent compliance and variable stiffness. Despite these benefits, their theoretically infinite degrees of freedom and prominent nonlinearities pose significant challenges in developing dynamic models and guiding the robots along desired paths. Additionally, soft robots may exhibit rigid behaviors and potentially collide with their surroundings during path tracking tasks, particularly when possible contact points are unknown. In this dissertation, reduced-order models are used to describe the behaviors of three different soft robot designs, including both linear parameter varying (LPV) and augmented rigid robot (ARR) models. While the reduced-order model captures the majority of the soft robot's dynamics, modeling uncertainties notably remain. Non-repeated modeling uncertainties are addressed by categorizing them as a lumped disturbance, employing two methodologies, $H_\infty$ method and nonlinear disturbance observer (NDOB) based sliding mode control, for its rejection. For repeated disturbances, an iterative learning control (ILC) with a P-type learning function is implemented to enhance trajectory tracking efficacy. Furthermore,for non-repeated disturbances, the NDOB facilitates the contact estimation, and its results are jointly used with a switching algorithm to modify the robot trajectories. The stability proof of all controllers and corresponding simulation and experimental results are provided. For a path tracking task of a soft robot with multi-segments, a robust control strategy that combines a LPV model with an innovative improved nonlinear disturbance observer-based adaptive sliding mode control (INASMC). The control framework employs a first-order LPV model for dynamic representation, leverages an improved disturbance observer for accurate disturbance forecasting, and utilizes adaptive sliding mode control to effectively counteract uncertainties. The tracking error under the proposed controller is proven to be asymptotically stable, and the controller's effectiveness is is validated with simulation and experimental results. Ultimately, this research mitigates the inherent uncertainty in soft robot modeling, thereby enhancing their functionality in contact-intensive tasks.
Date Created
2023
Agent

Affordable Soft and Semi-rigid Robot Designs -- Case Studies via Compliance Tuning and Mechanism Design

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Description
Robotic technology can be broadly categorized into two main approaches based on the compliance of the robot's materials and structure: hard and soft. Hard, traditional robots, with mechanisms to transmit forces, provide high degrees of freedom (DoFs) and precise manipulation,

Robotic technology can be broadly categorized into two main approaches based on the compliance of the robot's materials and structure: hard and soft. Hard, traditional robots, with mechanisms to transmit forces, provide high degrees of freedom (DoFs) and precise manipulation, making them commonly used in industry and academic research. The field of soft robotics, on the other hand, is a new trend from the past three decades of robotics that uses soft materials such as silicone or textiles as the body or material base instead of the rigid bodies used in traditional robots. Soft robots are typically pre-programmed with specific geometries, and perform well at tasks such as human-robot interaction, locomotion in complex environments, and adaptive reconfiguration to the environment, which reduces the cost of future programming and control. However, full soft robotic systems are often less mobile due to their actuation --pneumatics, high-voltage electricity or magnetics -- even if the robot itself is at a millimeter or centimeter scale. Rigid or hard robots, on the other hand, can often carry the weight of their own power, but with a higher burden of cost for control and sensing. A middle ground is thus sought, to combine soft robotics technologies with rigid robots, by implementing mechanism design principles with soft robots to embed functionalities or utilize soft robots as the actuator on a rigid robotic system towards an affordable robotic system design. This dissertation showcases five examples of this design principle with two main research branches: locomotion and wearable robotics. In the first research case, an example of how a miniature swimming robot can navigate through a granular environment using compliant plates is presented, compared to other robots that change their shape or use high DoF mechanisms. In the second pipeline, mechanism design is implemented using soft robotics concepts in a wearable robot. An origami-inspired, soft "exo-shell", that can change its stiffness on demand, is introduced. As a follow-up to this wearable origami-inspired robot, a geometry-based, ``near" self-locking modular brake is then presented. Finally, upon combining the origami-inspired wearable robot and brake design, a concept of a modular wearable robot is showcased for the purpose of answering a series of biomechanics questions.
Date Created
2023
Agent

Sensing, Modeling, Control and Evaluation of Soft Robots for Wearable Applications

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Description
While wearable soft robots have successfully addressed many inherent design limitations faced by wearable rigid robots, they possess a unique set of challenges due to their soft and compliant nature. Some of these challenges are present in the sensing, modeling,

While wearable soft robots have successfully addressed many inherent design limitations faced by wearable rigid robots, they possess a unique set of challenges due to their soft and compliant nature. Some of these challenges are present in the sensing, modeling, control and evaluation of wearable soft robots. Machine learning algorithms have shown promising results for sensor fusion with wearable robots, however, they require extensive data to train models for different users and experimental conditions. Modeling soft sensors and actuators require characterizing non-linearity and hysteresis, which complicates deriving an analytical model. Experimental characterization can capture the characteristics of non-linearity and hysteresis but requires developing a synthesized model for real-time control. Controllers for wearable soft robots must be robust to compensate for unknown disturbances that arise from the soft robot and its interaction with the user. Since developing dynamic models for soft robots is complex, inaccuracies that arise from the unmodeled dynamics lead to significant disturbances that the controller needs to compensate for. In addition, obtaining a physical model of the human-robot interaction is complex due to unknown human dynamics during walking. Finally, the performance of soft robots for wearable applications requires extensive experimental evaluation to analyze the benefits for the user. To address these challenges, this dissertation focuses on the sensing, modeling, control and evaluation of soft robots for wearable applications. A model-based sensor fusion algorithm is proposed to improve the estimation of human joint kinematics, with a soft flexible robot that requires compact and lightweight sensors. To overcome limitations with rigid sensors, an inflatable soft haptic sensor is developed to enable gait sensing and haptic feedback. Through experimental characterization, a mathematical model is derived to quantify the user's ground reaction forces and the delivered haptic force. Lastly, the performance of a wearable soft exosuit in assisting human users during lifting tasks is evaluated, and the benefits obtained from the soft robot assistance are analyzed.
Date Created
2023
Agent

Towards Model Predictive Control for Acrobatic Quadrotor Flights

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Description
Acrobatic maneuvers of quadrotors present unique challenges concerning trajectorygeneration, control, and execution. Specifically, the flip maneuver requires dynamically feasible trajectories and precise control. Various factors, including rotor dynamics, thrust allocation, and control strategies, influence the successful execution of flips. This research introduces an

Acrobatic maneuvers of quadrotors present unique challenges concerning trajectorygeneration, control, and execution. Specifically, the flip maneuver requires dynamically feasible trajectories and precise control. Various factors, including rotor dynamics, thrust allocation, and control strategies, influence the successful execution of flips. This research introduces an approach for tracking optimal trajectories to execute flip maneuvers while ensuring system stability autonomously. Model Predictive Control (MPC) designs the controller, enabling the quadrotor to plan and execute optimal trajectories in real-time, accounting for dynamic constraints and environmental factors. The utilization of predictive models enables the quadrotor to anticipate and adapt to changes during aggressive maneuvers. Simulation-based evaluations were conducted in the ROS and Gazebo environments. These evaluations provide valuable insights into the quadrotor’s behavior, response time, and tracking accuracy. Additionally, real-time flight experiments utilizing state- of-the-art flight controllers, such as the PixHawk 4, and companion computers, like the Hardkernel Odroid, validate the effectiveness of the proposed control algorithms in practical scenarios. The conducted experiments also demonstrate the successful execution of the proposed approach. This research’s outcomes contribute to quadrotor technology’s advancement, particularly in acrobatic maneuverability. This opens up possibilities for executing maneuvers with precise timing, such as slingshot probe releases during flips. Moreover, this research demonstrates the efficacy of MPC controllers in achieving autonomous probe throws within no-fly zone environments while maintaining an accurate desired range. Field application of this research includes probe deployment into volcanic plumes or challenging-to-access rocky fault scarps, and imaging of sites of interest. along flight paths through rolling or pitching maneuvers of the quadrotor, to use sensorsuch as cameras or spectrometers on the quadrotor belly.
Date Created
2023
Agent

Simultaneous Navigation And Mapping (SNAM) Using Collision Resilient UAV

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Description
Navigation and mapping in GPS-denied environments, such as coal mines ordilapidated buildings filled with smog or particulate matter, pose a significant challenge due to the limitations of conventional LiDAR or vision systems. Therefore there exists a need for a navigation algorithm and

Navigation and mapping in GPS-denied environments, such as coal mines ordilapidated buildings filled with smog or particulate matter, pose a significant challenge due to the limitations of conventional LiDAR or vision systems. Therefore there exists a need for a navigation algorithm and mapping strategy which do not use vision systems but are still able to explore and map the environment. The map can further be used by first responders and cave explorers to access the environments. This thesis presents the design of a collision-resilient Unmanned Aerial Vehicle (UAV), XPLORER that utilizes a novel navigation algorithm for exploration and simultaneous mapping of the environment. The real-time navigation algorithm uses the onboard Inertial Measurement Units (IMUs) and arm bending angles for contact estimation and employs an Explore and Exploit strategy. Additionally, the quadrotor design is discussed, highlighting its improved stability over the previous design. The generated map of the environment can be utilized by autonomous vehicles to navigate the environment. The navigation algorithm is validated in multiple real-time experiments in different scenarios consisting of concave and convex corners and circular objects. Furthermore, the developed mapping framework can serve as an auxiliary input for map generation along with conventional LiDAR or vision-based mapping algorithms. Both the navigation and mapping algorithms are designed to be modular, making them compatible with conventional UAVs also. This research contributes to the development of navigation and mapping techniques for GPS-denied environments, enabling safer and more efficient exploration of challenging territories.
Date Created
2023
Agent

Development, Modeling, and Testing of a Compliant Bistable Anguilliform Robot

Description
Undulatory locomotion is a unique form of swimming that generates thrust through the propagation of a wave through a fish’s body. The proposed device utilizes a constrained compliant material with a single actuator to generate an undulatory motion. This paper

Undulatory locomotion is a unique form of swimming that generates thrust through the propagation of a wave through a fish’s body. The proposed device utilizes a constrained compliant material with a single actuator to generate an undulatory motion. This paper draws inspiration from Anguilliformes and discusses the kinematics and dynamics of wave propagation of an underwater robot. A variety of parameters are explored through modeling and are optimized for thrust generation to better understand the device. This paper validates the theoretical spine behavior through experimentation and provides a path forward for future development in device optimization for various applications. Previous work developed devices that utilized either paired soft actuators or multiple redundant classical actuators that resulted in a complex prototype with intricate controls. The work of this paper contrasts with prior work in that it aims to achieve undulatory motion through passive actuation from a single actively driven point which simplifies the control. Through this work, the goal is to further explore low-cost soft robotics via bistable mechanisms, continuum material properties, and simplified modeling practices.
Date Created
2023
Agent

Enhancing Visual Odometry in Autonomous Vehicles

Description

Visual odometry (VO) plays a crucial role in determining the position and orientation of an autonomous vehicle as it navigates through its environment. However, the performance of visual odometry can be significantly affected by errors in disparity estimation and LIDAR

Visual odometry (VO) plays a crucial role in determining the position and orientation of an autonomous vehicle as it navigates through its environment. However, the performance of visual odometry can be significantly affected by errors in disparity estimation and LIDAR depth measurements. This thesis investigates the use of LIDAR depth correction and Stereo disparity matching, combined with stronger match filtering, to improve the accuracy and reliability of VO estimations. The study utilizes a dataset consisting of a sequence of image frames, ground truth position data, and a range of feature detection, description, and matching techniques. Results indicate that the proposed approach significantly improves the accuracy of VO estimations, providing a valuable contribution to the development of reliable and safe autonomous navigation systems. The proposed method consists of two main components: (1) an advanced disparity matching algorithm to obtain more accurate and robust disparity estimations, and (2) a LIDAR depth correction module that employs a sensor fusion approach to refine the depth information generated by LIDAR sensors. The LIDAR depth correction module combines data from multiple sensors, including LIDAR, camera, and inertial measurement unit (IMU), to produce a more accurate depth estimation. The performance of the proposed approach is evaluated using real-world datasets and benchmark visual odometry challenges. Results demonstrate that the proposed method significantly improves the accuracy and robustness of visual odometry, leading to better localization and navigation performance for autonomous vehicles. This research contributes to the ongoing development of autonomous vehicle technology by addressing critical challenges in visual odometry and offering a practical solution for more accurate and reliable self-localization

Date Created
2023-05
Agent

Collision-Averse and Dynamics-Aware B-Spline Derived Motion Planning for
Autonomous Vehicles

Description

The seamless integration of autonomous vehicles (AVs) into highly interactive and dynamic driving environments requires AVs to safely and effectively communicate with human drivers. Furthermore, the design of motion planning strategies that satisfy safety constraints inherit the challenges involved in

The seamless integration of autonomous vehicles (AVs) into highly interactive and dynamic driving environments requires AVs to safely and effectively communicate with human drivers. Furthermore, the design of motion planning strategies that satisfy safety constraints inherit the challenges involved in implementing a safety-critical and dynamics-aware motion planning algorithm that produces feasible motion trajectories. Driven by the complexities of arriving at such a motion planner, this thesis leverages a motion planning toolkit that utilizes spline parameterization to compute the optimal motion trajectory within a dynamic environment. Our approach is comprised of techniques originating from optimal control, vehicle dynamics, and spline interpolation. To ensure dynamic feasibility of the computed trajectories, we formulate the optimal control problem in relation to the intrinsic state constraints derived from the bicycle state space model. In addition, we apply input constraints to bound the rate of change of the steering angle and acceleration provided to the system. To produce collision-averse trajectories, we enforce extrinsic state constraints extracted from the static and dynamic obstacles in the circumambient environment. We proceed to exploit the mathematical properties of B-splines, such as the Convex Hull Property, and the piecewise composition of polynomial functions. Second, we focus on constructing a highly interactive environment in which the con- figured optimal control problem is deployed. Vehicle interactions are categorized into two distinct cases: Case 1 is representative of a single-agent interaction, whereas Case 2 is representative of a multi-agent interaction. The computed motion trajectories per each case are displayed in simulation.

Date Created
2023-05
Agent

What Do You Want Me To Do? Addressing Model Differences for Human-Aware Decision-Making from A Learning Perspective

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Description
As intelligent agents become pervasive in our lives, they are expected to not only achieve tasks alone but also engage in tasks with humans in the loop. In such cases, the human naturally forms an understanding of the agent, which

As intelligent agents become pervasive in our lives, they are expected to not only achieve tasks alone but also engage in tasks with humans in the loop. In such cases, the human naturally forms an understanding of the agent, which affects his perception of the agent’s behavior. However, such an understanding inevitably deviates from the ground truth due to reasons such as the human’s lack of understanding of the domain or misunderstanding of the agent’s capabilities. Such differences would result in an unmatched expectation of the agent’s behavior with the agent’s optimal behavior, thereby biasing the human’s assessment of the agent’s performance. In this dissertation, I focus on when these differences are due to a biased belief about domain dynamics. I especially investigate the impact of such a biased belief on the agent’s decision-making process in two different problem settings from a learning perspective. In the first setting, the agent is tasked to accomplish a task alone but must infer the human’s objectives from the human’s feedback on the agent’s behavior in the environment. In such a case, the human biased feedback could mislead the agent to learn a reward function that results in a sub-optimal and, potentially, undesired policy. In the second setting, the agent must accomplish a task with a human observer. Given that the agent’s optimal behavior may not match the human’s expectation due to the biased belief, the agent’s optimal behavior may be viewed as inexplicable, leading to degraded performance and loss of trust. Consequently, this dissertation proposes approaches that (1) endow the agent with the ability to be aware of the human’s biased belief while inferring the human’s objectives, thereby (2) neutralize the impact of the model differences in a reinforcement learning framework, and (3) behave explicably by reconciling the human’s expectation and optimality during decision-making.
Date Created
2022
Agent