Telepresence describes technologies that allow users to remotely experience the sensation of being present at an event without being physically present. An avatar exists to represent the user whilst in a remote location and is tasked to collect stimuli from its immediate surroundings to be delivered to the user for consumption. With the advent of recent developments in Virtual Reality technology, viz., head-mounted displays (HMDs), new possibilities have been enabled in the field of Telepresence. The main focus of this thesis is to develop a solution for visual Telepresence, where an HMD is used to control the direction of a camera‟s viewpoint, such that the user‟s head is tracked by the avatar, while providing visual feedback to the user. The design and development of the device follows a mechatronic approach, where a real time operating system (RTOS) is used in conjunction with a microcontroller for mechanical actuator control. The first-generation prototype, HOG-1 (HMD-Operated Gimbal, rev. 1), developed for this thesis serves as a foundation for study; the implementation and analysis of the prototype contributes to the state of the art by providing a clearer glimpse of hardware and software requirements that are necessary to construct an improved model. Additionally, qualitative and quantitative measurements are developed in the process of this research.

As science instruments on CubeSats become more sensitive to the attitude of the spacecraft, better methods must be employed to provide the accuracy needed to complete the planned mission. While systems that provide the accuracy required are available commercially, these solutions are not cost-effective, do not allow the design to be tailored to a specific mission, and most importantly, do not give students hand-on experience with attitude control actuators. This thesis documents the design, modeling, and simulation of a low-cost, student-fabricated, reaction wheel system for use in 3U CubeSat satellites. The entire design process for the development of this reaction wheel is based on fundamental design principles and can be replicated for either larger or smaller spacecraft as needed. Additionally, plans for bringing this design up to a prototyping and testing phase are outlined for continued use of this design in the Cal Poly CubeSat Laboratory.

Acts of insurgency have become an increasing threat resulting in extensive measures being taken by the law enforcement authorities to mitigate their devastating effects on human life and infrastructure. This thesis introduces a magnetometry-based information, and signal processing methodology for detecting concealed ferrous objects in vehicle body panels. From extensive literature research, it was observed that while magnetic sensors have been used in a variety of related applications, but they have not been extensively applied to the on-road detection of firearms and explosives concealed in vehicles. This study utilized an extensive experimental protocol for preliminary concept validation. The main idea behind the approach was that almost all concealed weapons and explosives are made up of a considerable amount of ferrous material, and hence produce a local distortion in the Earth’s magnetic field. This distortion can then be identified by utilizing sensitive magnetic sensors. To detect concealed ferrous objects, magnetic signatures of a vehicle door panel were obtained by using a scanning assembly design in this thesis project, and compared to a base magnetic signature of the same vehicle door panel. The base magnetic signature is the magnetic field data of the same vehicle where no foreign ferrous objects were present. To analyze the data, a signal processing methodology was designed. To achieve the objective of accurately detecting concealed ferrous objects, simple measures such as magnetic field strength and its energy density were computed. These simple measures were then used in conjunction with more sophisticated statistical methods such as, normalized cross-correlation and Mahalanobis distance. Although all these methodologies were able to detect a magnetic footprint anomaly in the presence of a concealed object, the Mahalanobis distance approach, in particular provided the most conclusive results in all the test cases considered.

This thesis document outlines the development of a multibody dynamics simulation of an actively stabilized multiple-input, multiple-output, coupled, balancing cube and the process of verifying the results by implementing the control algorithm in hardware. A non-linear simulation of the system was created in Simscape and used to develop a Linear Quadratic Gaussian control algorithm. To implement this algorithm in actual hardware, the system was first designed, manufactured, and assembled. The structure of the cube and the reaction wheels were milled from aluminum. DC brushless motors were installed into the mechanical system. In terms of electronics, a processor, orientation sensor, motor drivers, analog to digital converters, and a pulse width modulation board were assembled into the cube. Upon completion, the software to control the cube was developed using Simulink and run on a Raspberry Pi computer within the mechanism.

The Pier Portal marine monitoring system is an internet controlled underwater camera that will enable students, researchers, and the general public the ability to monitor underwater marine life and ocean conditions. Installed at Cal Poly’s Center for Coastal Marine Science pier at Avila Beach, California, the camera can be commanded in real-time to raise or lower to any position between the top of the pier and the bottom of the ocean floor, while providing a live-stream video to the operator and general public. This thesis effort focused on the integration of the various subsystem components through software, and the development of an online interface to allow the remote control of the system and the ability to view the live-stream video from the camera. Missing or damaged mechanical and electrical components were successfully redesigned and replaced, and a more compact and serviceable winch and sheave system was designed and partially manufactured. A software set was written in Python, JavaScript, HTML, and IEC 61131-6 successfully connecting the system’s control server, the winch’s industrial PLC, the cameras inside the underwater pod, and the front-facing webserver. The system’s internal network has been designed to allow internal components to communicate, and to allow external users the ability to securely control the system and view the video feed. Due to campus closure in early 2020, the final system was unable to be installed and tested, however, this document contains the entire system design and the next steps required to fully implement the system.

A method for tuning the spring rate and damping rate of a mountain bike suspension based on a data-driven procedure is presented. The design and development of a custom data acquisition system, known as the "MTB DAQ," capable of measuring acceleration data at the front and rear axles of a bike are discussed. These data are input into a model that is used to calculate the vertical acceleration and pitching angular acceleration response of the bike and rider. All geometric and dynamic properties of the bike and rider system are measured and built into the model. The model is tested and validated using image processing techniques. A genetic algorithm is implemented with the model and used to calculate the best spring rate and damping rate of the mountain bike suspension such that the vertical and pitching accelerations of the bike and rider are minimized for a given trail. Testing is done on a variety of different courses and the performance of the bike when tuned to the results of the genetic algorithm is discussed. While more fine tuning of the model is possible, the results show that the genetic algorithm and model accurately predict the best suspension settings for each course necessary to minimize the vertical and pitching accelerations of the bike and rider.

The development and evaluation of the design of a Micro-Ram Air Turbine (µRAT), a device being developed to provide power for an autonomous boundary layer measurement system, has been undertaken. The design tools consist of a rotor model and a generator model. The primary focus was on developing and evaluating the generator model for the prediction of generator brake power and output electrical power with and without rectification as a function of shaft speed and electrical load, with only basic manufacturer specifications given as inputs. A series of motored generator evaluation test were conducted at speeds ranging from 9,000 to 25,000 rpm for loads varying between 1 and 3.02 Ohms with output power of up to 80 Watts. Results demonstrated that predicted generated power was at or below 3% error when compared to measured results with about 1% uncertainty. A rotor model was also developed using basic blade element theory. This model neglected induced flow effects and was therefore expected to over predict rotor torque and power. A second rotor model that includes induced flow effects, the open source program X-Rotor, was also used to predict rotor power and for comparison to the blade element rotor model results. Both rotor models were evaluated through wind tunnel validation tests conducted on a turbine generator with two different 3.25 in diameter rotors, rotor-1 (untwisted blades) and rotor-2 (twisted blades). Wind tunnel validation test airspeeds varied between 71-110 mph with electrical loads ranging from 1-20 ohms. Results indicated power predictions to be 50-75% higher for the blade element model and 20-30% for X-Rotor results. The blade element rotor model was modified by applying the Prandtl tip-loss factor to approximately account for the induced flow effects; this addition brought predictions much closer to X-Rotor results. Based on the motor-driven generator test results, it is believed that most of the discrepancy in baseline rotor/generator validation test between predicted and observed power generated is due to inaccuracy in the rotor performance modelling with likely contributors to error being induced flow effects, crude section lift/drag modelling, and aero-elastic deformation. It is concluded that the proposed generator model is sufficient although direct torque measurements may be desired and further development of the µRAT design tools should focus on an improved rotor performance model.

This thesis contains the design, manufacturing, and testing of a functional eight-legged robotic transporter based on the concept design laid out in U.S. Patent 7,246,671. The device is intended to achieve three different sequences of motion: regular driving, obstacle climbing, and stair climbing. The prototype was carried through concept design, analysis, selection of materials and components, manufacturing, software development, and final assembly and testing. The device can be assembled under multiple configurations, which harbor certain advantages and disadvantages. The results of the testing encourage the continuation of a second iteration of this concept.

No description available.

The goal of this thesis is to develop a soft multiple-degree-of-freedom (multi-DOF) load cell that is robust and light weight for use in robotics applications to sense three axes of force and a single axis of torque. The displacement of the magnet within the elastomer changes the magnetic flux density which is sensed by two 3-axis Hall effect sensors. Experimental measurements of magnetic flux density within the area of interest were used to formulate analytic expressions that relate magnet field strength to the position of the magnet. The displacement and orientation measurement and the material properties of the elastomer are used to calibrate and calculate the applied load. The ability to measure 3-DOF force and axial torque was evaluated with combined loading applied by a robotic arm (KUKA, LBR r820 iiwa). The decoupled results show the 4-DOF load cell was able to distinguish 3-axis force and 1-axis torque with 6.9% averaged error for normal force, 4.3% and 2.6% for shear force in the X and Y axis and 8.6% for the torque. The results show good accuracy for a soft multi-axis sensor that would be applicable in many robotic applications where high accuracy is not required.