Stirling Engine class

Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2008.The problem of a lack of science and engineering opportunities for youth has been identified. While other programs and attempted solutions exist, a novel approach involving creating self-contained project classes, called modules, and trading them between institutions is proposed. This idea intends to make these lacking opportunities available while overcoming some of the current problems opposing this availability namely insufficient resources and staff. While limited time and resources prevents the complete testing of the idea, the development of a single module to the point before developing a trading system is implemented. The project chosen is the construction and operation of the Stirling Engine using a design borrowed from MIT course 2.670. The module is tested with 15 4th to 7th grade home-schooled students in Los Angeles, Ca. Observations and participant feedback are gathered. Changes including the shortening of lectures, simplification of the project, and addition of testing are proposed. The information gathered from the test suggests that with a trading system in place, these modules can expose students to science and engineering and generate excitement for the fields.by Patrick R. Barragán.S.B

An efficient drive, sensing, and actuation system using PZT stack actuator cells

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 81-82).The PZT cellular actuator developed in the MIT d'Arbeloff Laboratory utilizes small-strain, high-force PZT stack actuators in a mechanical flexure system to produce a larger-strain, lower-force actuator useful in robotic systems. Many functionalities for these cellular actuators are developed which can have great impact on robotic systems and actuation itself. After initial exploration into other possible circuitry, a circuit is designed to recovery unused energy for the PZT cells. The circuit design is formed around a proposed method of distributed actuation using PZT cells which imposes that different PZT cells will be activated during different periods such that the charge from some cells can be transferred to others. If the application allows actuation which can conform to this criteria, the developed circuit can be used which, without optimization, can save ~41% of the energy used to drive the actuators with a theoretical upper limit on energy efficiency of 100%. A dynamic system consisting of multiple PZT actuators driving a linear gear is analyzed and simulated which can achieve a no load speed 2.4 m/s with minimal actuators. Then, the two-way transforming properties of PZT stack actuators are utilized to allow dual sensing and actuation. This method uses an inactive PZT cell as a sensor. With no additional sensors, a pendulum system driven by antagonistic groups of PZT cells is shown to find its own resonance with no system model. These functionalities of charge recovery, distributed actuation, and dual sensing and actuation set the PZT cellular actuator as an important contribution to robotic actuation and begin to illuminate the possible impacts of the concept. The design and analysis described reveals many possibilities for future applications and developments using the PZT cellular actuator in the fields of actuation and robotics.by Patrick R. BarragánS.M

Interactive Bayesian identification of kinematic mechanisms

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2015.Cataloged from PDF version of thesis.Includes bibliographical references (pages 127-128).This thesis addresses the problem of identifying mechanisms based on data gathered from a robot's interaction with them. We present a decision-theoretic formulation of this problem, using Bayesian filtering techniques to maintain a distributional estimate of the mechanism type and parameters. We begin by implementing a discrete Bayesian filter. We demonstrate the approach on a domain with four primitive and two composite mechanisms. In order to reduce the amount of interaction required to arrive at a confident identification, we select actions explicitly to either a) reduce entropy in the current estimate or b) race the top two hypotheses to clearly distinguish between them. The results show that this approach can correctly identify complex mechanisms including mechanisms which behave different in different parts of their configuration spaces. The results also show that active action selection can significantly decrease the number of actions required to gather the same information while the racing technique does so without increasing step time of the filter over random action selection. We analyze Bayesian filtering in a hybrid space for model comparison. We discuss the appropriateness of continuous state-space, parametric filters for the mechanism identification problem. We seek an alternative strategy because no parametric form is clearly suited to representing the posterior distributions during the filtering process. We find that Bayesian filtering in the hybrid space has some surprising consequences and discuss their effect on inference. Finally, we implement a particle filter which allows filtering in a space expanded from 10 model-parameter pairs to 50,000. We demonstrate that with high accuracy, the particle filter can correctly identify the mechanism type. More crucially, we show that the filter's estimate allows the robot to reasonably predict the motion of the given mechanism regardless of classification. We demonstrate our method in simulation and on a real-world PR2 robot interacting with common mechanisms.by Patrick Rene Barragán.Ph. D