My thesis covers the design and fabrication of novel humanoid robotic eyes and the process of interfacing them with the industry robot, Baxter. The mechanism can reach a maximum saccade velocity comparable to that of human eyes. Unlike current robotic eye designs, these eyes have independent left-right and up-down gaze movements achieved using a servo and DC motor, respectively. A potentiometer and rotary encoder enable closed-loop control. An Arduino board and motor driver control the assembly. The motor requires a 12V power source, and all other components are powered through the Arduino from a PC. Hand-eye coordination research influenced how the eyes were programmed to move relative to Baxter’s grippers. Different modes were coded to adjust eye movement based on the durability of what Baxter is handling. Tests were performed on a component level as well as on the full assembly to prove functionality. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection

This thesis is encompasses the design, construction, control and testing of an improvement upon the novel soft robotic Jennifish platform. The advancement of this platform includes the addition of light and depth sensors as well increasing the separation of tentacle groups from two to three sets. The final vehicle model consists nine PneuNetstyle actuators divided into three groups of three, molded around a machined Delrin pressure vessel. With a 12V submersible impellor pump connected to each actuator grouping, propulsion is created by the filling and emptying of these tentacles with surrounding ambient water. The Jellyfish2.0 is capable of omnidirectional lateral movement as well as upward driven motion. The vehicle also has a temperature sensor and IMU as did the previous of this platform. Qualitative free-swimming testing was conducted, recorded and analyzed as well as quantitative inline load cell testing, to create a benchmark for comparison with other jellyfish like robots. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2019. / FAU Electronic Theses and Dissertations Collection

Indiana University-Purdue University Indianapolis (IUPUI) / There have been many endeavors to design humanoid robots that have human characteristics such as dexterity, autonomy and intelligence. Humanoid robots are intended to cooperate with humans and perform useful work that humans can perform. The main advantage of humanoid robots over other machines is that they are flexible and multi-purpose. In this thesis, a human-like robotic arm is designed and used in a task which is typically performed by humans, namely, catching a ball. The robotic arm was designed to closely resemble a human arm, based on anthropometric studies. A rigid multibody dynamics software was used to create a virtual model of the robotic arm, perform experiments, and collect data. The inverse kinematics of the robotic arm was solved using a Newton-Raphson numerical method with a numerically calculated Jacobian. The system was validated by testing its ability to find a kinematic solution for the catch position and successfully catch the ball within the robot's workspace. The tests were conducted by throwing the ball such that its path intersects different target points within the robot's workspace. The method used for determining the catch location consists of finding the intersection of the ball's trajectory with a virtual catch plane. The hand orientation was set so that the normal vector to the palm of the hand is parallel to the trajectory of the ball at the intersection point and a vector perpendicular to this normal vector remains in a constant orientation during the catch. It was found that this catch orientation approach was reliable within a 0.35 x 0.4 meter window in the robot's workspace. For all tests within this window, the robotic arm successfully caught and dropped the ball in a bin. Also, for the tests within this window, the maximum position and orientation (Euler angle) tracking errors were 13.6 mm and 4.3 degrees, respectively. The average position and orientation tracking errors were 3.5 mm and 0.3 degrees, respectively. The work presented in this study can be applied to humanoid robots in industrial assembly lines and hazardous environment recovery tasks, amongst other applications.