<p>Soft robotics is concerned with the modeling and designing of devices fabricated from materials with low Young’s moduli—much less than that of metal— that mimic the input/output operation and physical task utility of robotics.  The inherent compliance of soft robots lends these devices an adaptability and a capacity for human-machine interaction beyond that of conventional robotics. Multistable soft robotic grippers are a subset of the technology at the intersection of soft robotics and multistable structures. Multistable structures are continuum systems that exhibit more than one statically stable state, each associated with a strain energy minimum. The existence of these energetic minima allows the structures to adopt different stable configurations that can provide a reference point for open loop control schemes. Multistable soft robotics takes advantage of both the adaptability of soft robotics and the potential for simplified control of multistable structures.</p> <p>Achieving simplified control for soft robotics is a necessary milestone in creating functional and applied soft robots. </p> <p>This work presents a means for simple open-loop control of a multistable soft robotic gripper that is adaptable, controllable, and robust. The behavior is illustrated through a gripper geometry described by specific design parameters resulting in a near infinite design space. An analytical model based on lumped parameter springs is derived, allowing us to search the design space in a tractable fashion. Specifically, we predict the system’s stable states for any given design instance by searching for local minima in the energy landscape formed by a spring lattice representation of our device. The lattice is composed of linear, bistable, and torsional springs—each of which contributes to the energy landscape of the system. We validate our model against Finite Element simulations of our device, showing good agreement with the proposed model. The aptitude of the model sheds light on the fundamental mechanics of our soft robotic gripper topology, laying the foundation for efficient design optimization and simplified control of soft robots.</p>

<p dir="ltr">Teletaction, the transmission of tactile feedback or touch, is a crucial aspect in the</p><p dir="ltr">field of teleoperation. High-quality teletaction feedback allows users to remotely manipulate</p><p dir="ltr">objects and increase the quality of the human-machine interface between the operator and</p><p dir="ltr">the robot, making complex manipulation tasks possible. Advances in the field of teletaction</p><p dir="ltr">for teleoperation however, have yet to make full use of the high-resolution 3D data provided</p><p dir="ltr">by modern vision-based tactile sensors. Existing solutions for teletaction lack in one or more</p><p dir="ltr">areas of form or function, such as fidelity or hardware footprint. In this thesis, we showcase</p><p dir="ltr">our research into a low-cost teletaction device for teleoperation that can utilize the real-time</p><p dir="ltr">high-resolution tactile information from vision-based tactile sensors, through both physical</p><p dir="ltr">3D surface reconstruction and shear displacement. We present our device, the Feelit, which</p><p dir="ltr">uses a combination of a pin-based shape display and compliant mechanisms to accomplish</p><p dir="ltr">this task. The pin-based shape display utilizes an array of 24 servomotors with miniature</p><p dir="ltr">Bowden cables, giving the device a resolution of 6x4 pins in a 15x10 mm display footprint.</p><p dir="ltr">Each pin can actuate up to 3 mm in 200 ms, while providing 80 N of force and 3 um of</p><p dir="ltr">depth resolution. Shear displacement and rotation is achieved using a compliant mechanism</p><p dir="ltr">design, allowing a minimum of 1 mm displacement laterally and 10 degrees of rotation. This</p><p dir="ltr">real-time 3D tactile reconstruction is achieved with the use of a vision-based tactile sensor,</p><p dir="ltr">the GelSight, along with an algorithm that samples the depth data and marker tracking to</p><p dir="ltr">generate actuator commands. With our device we perform a series of experiments including</p><p dir="ltr">shape recognition and relative weight identification, showing that our device has the potential</p><p dir="ltr">to expand teletaction capabilities in the teleoperation space.</p>

<p>  </p> <p>Architectured materials offer engineers more options for choosing materials with their desired properties. Segmenting materials to create topological interlocking materials (TIMs) creates materials, which can deform in greater amounts without failure and absorb more strain energy. Previous research on TIMs has shown that the stiffness and reaction force of these materials can be directly controlled by controlling the boundary forces offered by the frame which constrains these materials.</p> <p>The research presented in this paper investigated a TIM made into a 1-Dimension beam like structure called a lintel. This research investigated not only the mechanics of this structure, but also developed a method of directly controlling the reaction force at a given displacement using shape memory alloy (SMA) wires. These wires would actuate the boundary pieces used to constrain the system. These actuation wires coupled with force sensors imbedded into the lintel allowed a feedback control loop to be established, which would control the reaction force. The reaction force was then controlled to create a smart structure which could optimize the strain energy absorption under the constraint of a maximum allowable load, similar to cellular solids used in packaging and padding materials.</p> <p>To develop this smart structure, four separate investigations occurred. The first was finite element analysis (FEA) performed to model the loading response of the lintel. This experiment demonstrated that the Mises Truss Model was effective at modelling the lintel. The second was an experimental validation of the FEA model performed in the first investigation. This experiment validated the Mises Truss Model for the lintel. The third investigation simulated the active lintel using computational software and the model of the lintel established in the first two investigations. This experiment demonstrated computationally the ability of SMA wires to control the reaction force as desired in an idealized case. The fourth and final investigation experimentally validated the ability to create and active lintel and created a functioning prototype. This demonstrated experimentally the ability of the active lintel to control reaction force as desired.</p> <p>This project has demonstrated the viability to create smart structures using segmented materials, which in the future may be used in a variety of applications including robotics and adaptive structures in harsh environments. </p>

<p dir="ltr">Over 150,000 people undergo lower-extremity amputations yearly in the United States. In recent years, multiple efforts have been made to improve the human-robot interaction between amputees and active lower limb prostheses. Using lightweight wearable technologies has been a viable solution to implement algorithms that can estimate gait kinematics and prosthesis users’ intent. Examples of wearable technologies include inertial measurement units, strain gauges, and electromyography sensors. Kinematic and force data is inputted into an Error-State Kalman filter to estimate the inversion-eversion, external-internal, and dorsiflexion-plantarflexion ankle angle. The filter tracked the ankle angle with an accuracy of 0.7724°, 0.8826°, and 1.3520°, respectively. The gait phase was estimated using a linear regression model based on a shank kinematics ground truth pattern with an average normalized accuracy of 97.79 %. A numerical simulation of a gait emulator in the form of a 3-Revolute-Prismatic-Revolute (3-RPR) manipulator. The gait emulator can test lower limb prostheses independent of human subjects, eliminating many hurdles associated with human subject testing. The manipulator was simulated with two control strategies: a traditional PID and a hybrid PID + Active Force Control controller (AFC). The hybrid PID+AFC provided higher accuracy in tracking the desired end-effector trajectory due to improved disturbance rejection. A low-cost surface electromyography (sEMG) platform was developed to robustly acquire sEMG signals, with an overall component cost of 35.06 US$. The sEMG platform integrates directly into a Micro:bit microcontroller through an expansion board. During testing with human subjects, sEMG Micro:bit platform had a reported average signal-to-noise ratio of 24.7 dB.</p>

<p>This thesis presents solutions to some of the problems facing Autonomous Surface Vehicle (ASV) deployments in inland waterways through the development of navigational and control systems. Fluvial systems are one of the hardest inland waterways to navigate and are thus used as a use-case for system development. The systems are built to reduce the reliance on a-prioris during ASV operation. This is crucial for exceptionally dynamic environments such as fluvial bodies of water that have poorly defined routes and edges, can change course in short time spans, carry away and deposit obstacles, and expose or cover shoals and man-made structures as their water level changes. While navigation of fluvial systems is exceptionally difficult potential autonomous data collection can aid in important scientific missions in under studied environments.</p> <p><br></p> <p>The work has four contributions targeting solutions to four fundamental problems present in fluvial system navigation and control. To sense the course of fluvial systems for navigable path determination a fluvial segmentation study is done and a novel dataset detailed. To enable rapid path computations and augmentations in a fast moving environment a Dubins path generator and augmentation algorithm is presented ans is used in conjunction with an Integral Line-Of-Sight (ILOS) path following method. To rapidly avoid unseen/undetected obstacles present in fluvial environments a Deep Reinforcement Learning (DRL) agent is built and tested across domains to create dynamic local paths that can be rapidly affixed to for collision avoidance. Finally, a custom low-cost and deployable ASV, BREAM (Boat for Robotic Engineering and Applied Machine-Learning), capable of operating in fluvial environments is presented along with an autonomy package used in providing base level sensing and autonomy processing capability to varying platforms.</p> <p><br></p> <p>Each of these contributions form a part of a larger documented Fluvial Navigation Control Architecture (FNCA) that is proposed as a way to aid in a-priori free navigation of fluvial waterways. The architecture relates the navigational structures into high, mid, and low-level controller Guidance and Navigational Control (GNC) layers that are designed to increase cross vehicle and domain deployments. Each component of the architecture is documented, tested, and its application to the control architecture as a whole is reported.</p>

<p>    </p> <p>This thesis studies networked Gaussian linear control systems with random delays. Networked control systems is a popular topic these years because of their versatile applications in daily life, such as smart grid and unmanned vehicles. With the development of these systems, researchers have explored this area in two directions. The first one is to derive the inherent rate-cost relationship in the systems, that is the minimal transmission rate needed to achieve an arbitrarily given stability requirement. The other one is to design achievability schemes, which aim at using as less as transmission rate to achieve an arbitrarily given stability requirement. In this thesis, we explore both directions. We assume the sensor-to-controller channels experience independently and identically distributed random delays of bounded support. Our work separates into two parts. In the first part, we consider networked systems with only one sensor. We focus on deriving a lower bound, R_{LB}(D), of the rate-cost tradeoff with the cost function to be E{| <strong>x^</strong>T<strong>x </strong>|} ≤ D, where <strong>x </strong>refers to the state to be controlled. We also propose an achievability scheme as an upper bound, R_{UB}(D), of the optimal rate-cost tradeoff. The scheme uses lattice quantization, entropy encoder, and certainty-equivalence controller. It achieves a good performance that roughly requires 2 bits per time slot more than R_{LB}(D) to achieve the same stability level. We also generalize the cost function to be of both the state and the control actions. For the joint state-and-control cost, we propose the minimal cost a system can achieve. The second part focuses on to the covariance-based fusion scheme design for systems with multiple > 1 sensors. We notice that in the multi-sensor scenario, the outdated arrivals at the controller, which many existing fusion schemes often discard, carry additional information. Therefore, we design an implementable fusion scheme (CQE) which is the MMSE estimator using both the freshest and outdated information at the controller. Our experiment demonstrates that CQE out-performances the MMSE estimator using the freshest information (LQE) exclusively by achieving a 15% smaller average L2 norm using the same transmission rate. As a benchmark, we also derive the minimal achievable L2 norm, Dmin, for the multi-sensor systems. The simulation shows that CQE approaches Dmin significantly better than LQE. </p>

<p dir="ltr">This project aimed to develop an instrumentation and control system for a micro-drilling robot based on Industrial Internet of Things (IIoT) technologies. The automation system integrated IIoT technological tools to create a robust automation system capable of being used in drilling operations. The system incorporated industrial-grade sensors, which carried out direct measurements of the critical variables of the process. The indirect variables relevant to the control of the robot were calculated from the measured parameters. The system also considered the telemetry architecture necessary to reliably transmit data from the down-the-hole (DTH) robot to a receiver on the surface. Telemetry was based on wireless communication through long-range radio frequency (LoRa). The system developed had models based on Artificial Intelligence (AI) and Machine Learning (ML) for determining the mode of operation, detecting changes in the process, and changes in drilling variables in critical hydraulic components for the drilling process. Algorithms based on AI and ML models also allowed the user to make better decisions based on the variables' correlation to optimize the drilling process (e.g., dynamic change of flow, pressure, and RPMs based on automatic rock identification). A user interface (UI) was developed, and digital tools to perform data analysis were implemented. Safety assessment in all robot systems (e.g., electrical, hardware, software) was contemplated as a critical design component. The result of this research project provides innovative micro-drilling robots with the necessary technological tools to optimize the drilling process. The system made drilling more efficient, reliable, and safe, providing diagnostic and prognostic tools that allowed planning maintenance based on the actual health of the devices. The system that was developed was tested in a test bench under controlled conditions within a laboratory to characterize the system and collect data that allowed ML models' development, training, validation, and testing. The prototype of a micro-drilling robot installed on the test bench served as a case study to assess the implemented models' reliability and the proposed telemetry.</p>