<p> As technology becomes more integrated into our daily lives, the need for machines that can safely and comfortably interact with the human body has grown. While the rigidity of traditional robotic materials, such as metals and plastics, can provide mechanical and electrical stability to these devices, it can also reduce safety and comfort when placed in contact with soft human tissue. In recent years, these issues have been addressed by incorporating compliant materials, like liquids or soft polymers, into wearable or biomedical devices. However, these materials, by virtue of their softness, cannot support the high loads required for operations like stabilization or gripping. To address this apparent trade-off between load-bearing stiffness and conformable softness, several groups have constructed stiffness-tuning devices, capable of alternating between a high-stiffness state and a low-stiffness state. Although there exist a wide variety of mechanisms by which we can achieve this switching behavior, thermally activated phase change provides the highest stiffness ratio between the soft and stiff states. In this work, use low-melting point conductive thermoplastics to create electrically activated stiffness-switching devices. When a voltage is applied across this thermoplastic, the resulting electric current causes the polymer to heat and melt. This phase change corresponds to an effective stiffness change. </p><p> In the first study, we introduce a novel stiffness switch layout that employs liquid metal as compliant electrodes oriented across the face of a conductive thermoplastic. This new layout results in an 80% decrease in required voltage, a 60% decrease in activation time, and the ability to switch the stiffness of arbitrary geometries. </p><p> In the second study, we examine the effects of the composition of a conductive thermoplastic composite on its stiffness-switching properties, and use these findings can help guide the design of stiffness-switching composites for a three soft robotic applications.</p><p>

<p> Research on natural language processing, such as for image and speech recognition, is rapidly changing focus from statistical methods to neural networks. In this study, we introduce speech recognition capabilities along with computer vision to allow a robot to play snooker completely by itself. The color of the ball to be pocketed is provided as an audio input using an audio device such as a microphone. The system is able to recognize the color from the input using a trained deep learning network. The system then commands the camera to locate the ball of the identified color on a snooker table by using computer vision. To pocket the target ball, the system then predicts the best shot using an algorithm. This activity can be executed accurately based on the efficiency of the trained deep learning model.</p><p>

<p> A linear finite element procedure is presented for modeling a single flexible link robotic manipulator system. The model takes into consideration the DC motor, torsional springs/dampers, cantilever beam and the accelerometer at the tip. The finite element method for system modeling is validated through comparison to experimental data using the frequency response. With a validated model for the system, feedback control is implemented for vibration reduction and accurate positioning of the beam tip. The accuracy of the controlled model is compared to experiment and a discrete time transfer matrix method of modelling. The closed-loop finite element method model exhibited an accurate response, related to experiment, with encoder feedback and accelerometer feedback using digital compensators</p>

<p> The goal of this project was to design, build, program, upgrade, and remotely control an existing industrial-grade robotic manipulator on a budget that was equal to a graduate student's salary.</p><p> Inverse kinematic equations were developed to model the CRS arm using Denavit-Hartenberg convention. After converting these equations into a system of Python code, the equations were verified via graphical simulation.</p><p> The CRS arm's obsolete motor driver box was completely removed and replaced with cutting-edge, low-cost microcontrollers. The wiring, mechanics, and controls for the five degrees of freedom (DOF) were reverse engineered based on very limited datasheets, including the arm's motors, brakes, encoder pin-outs, gripper, and fixed global reference frame movement.</p><p> The microcontrollers were then programmed in C/C++ to allow a user to control every electrical and mechanical aspect of the arm. The kinematic equations were implemented on a Python server, which commands how each joint in the robotic arm must move to reach a desired point in space. The user interface was developed in conjunction with Evan Boldt to allow for remote control of the robotic arm and monitoring through webcams, which includes twisting and tilting a tablet.</p>

<p> This work demonstrates the first low cost, all-elastomer capacitive tactile arrays compatible with roll-to-roll manufacturing. A new manufacturing process has been developed in which elastomer sheets are covered with a stencil, spray coated with conductive elastomer on one or both sides, and stacked to create the sensor array. These arrays are highly flexible and can withstand large strains. Sensor costs are below $0.12/sensor in small quantities. Some variants in the fabrication process result in a slightly curved sensor so the change in capacitance is highly nonlinear at low pressures, but approaches theoretical sensitivities at higher pressures. The sensors have been determined to be highly sensitive, with a sensor resolution of 0.5 Pa and reveal a repeatable response from 1 kPa up to 120 kPa. A variety of materials ranging in modulus, thickness and texture were investigated for static, dynamic, and spatial location testing.</p>

<p> We present the background and motivation for ground vehicle autonomy, and focus on uses for space-exploration. Using a simple design example of an autonomous ground vehicle we derive the equations of motion. After providing the mathematical background for nonlinear systems and control we present two common methods for exactly linearizing nonlinear systems, feedback linearization and backstepping. We use these in combination with three adaptive control methods: model reference adaptive control, adaptive sliding mode control, and extremum-seeking model reference adaptive control. We show the performances of each combination through several simulation results. We then consider disturbances in the system, and design nonlinear disturbance observers for both single-input-single-output and multi-input-multi-output systems. Finally, we show the performance of these observers with simulation results.</p>