UMD Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/3
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.
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Item Leader Based Cyclic Pursuit(2016) Miltenberger, Kenneth L.; Krishnaprasad, P S; Galloway, Kevin S; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this work a system of autonomous agents engaged in cyclic pursuit (under constant bearing (CB) strategy) is considered, for which one informed agent (the leader) also senses and responds to a stationary beacon. Building on the framework proposed in a previous work on beacon-referenced cyclic pursuit, necessary and suffi- cient conditions for the existence of circling equilibria in a system with one informed agent are derived, with discussion of stability and performance. In a physical testbed, the leader (robot) is equipped with a sound sensing apparatus composed of a real time embedded system, estimating direction of arrival of sound by an Interaural Level and Phase Difference Algorithm, using empirically determined phase and level signatures, and breaking front-back ambiguity with appropriate sensor placement. Furthermore a simple framework for implementing and evaluating the performance of control laws with the Robot Operating System (ROS) is proposed, demonstrated, and discussed.Item ROBOTIC SOUND SOURCE LOCALIZATION AND TRACKING USING BIO-INSPIRED MINIATURE ACOUSTIC SENSORS(2013) Sawaqed, Laith Sami; Yu, Miao; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Sound source localization and tracking using auditory systems has been widely investigated for robotics applications due to their inherent advantages over other systems, such as vision based systems. Most existing robotic sound localization and tracking systems utilize conventional microphone arrays with different arrangements, which are inherently limited by a size constraint and are thus difficult to implement on miniature robots. To overcome the size constraint, sensors that mimic the mechanically coupled ear of fly Ormia have been previously developed. However, there has not been any attempt to study robotic sound source localization and tracking with these sensors. In this dissertation, robotic sound source localization and tracking using the miniature fly-ear-inspired sensors are studied for the first time. First, through investigation into the Cramer Rao lower bound (CRLB) and variance of the sound incident angle estimation, an enhanced understanding of the influence of the mechanical coupling on the performance of the fly-ear inspired sensor for sound localization is achieved. It is found that due to the mechanical coupling between the membranes, at its working frequency, the fly-ear inspired sensor can achieve an estimation of incident angle that is 100 time better than that of the conventional microphone pair with same signal-to-noise ratio in detection of the membrane deflection. Second, development of sound localization algorithms that can be used for robotic sound source localization and tracking using the fly-ear inspired sensors is carried out. Two methods are developed to estimate the sound incident angle based on the sensor output. One is based on model-free gradient descent method and the other is based on fuzzy logic. In the first approach, different localization schemes and different objective functions are investigated through numerical simulations, in which two-dimensional sound source localization is achieved without ambiguity. To address the slow convergence due to the iterative nature of the first approach, a novel fuzzy logic model of the fly-ear sensor is developed in the second approach for sound incident angle estimation. This model is studied in both simulations and experiments for localization of a stationary source and tracking a moving source in one dimension with a good performance. Third, nonlinear and quadratic-linear controllers are developed for control of the kinematics of a robot for sound source localization and tracking, which is implemented later in a mobile platform equipped with a microphone pair. Both homing onto a stationary source and tracking of a moving source with pre-defined paths are successfully demonstrated. Through this dissertation work, new knowledge on robotic sound source localization and tracking using fly-ear inspired sensors is created, which can serve as a basis for future study of sound source localization and tracking with miniature robots.Item Understanding and Mimicking the Fly's Directional Hearing: Modeling, Sensor Development, and Experimental Studies(2012) Liu, Haijun; Yu, Miao; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Microphone arrays have been widely used in sound source localization for many applications. In order to locate the sound in a discernible manner, the separation between microphones needs to be greater than a critical distance, which poses a fundamental constraint for the miniaturization of directional microphones. In nature, animal hearing organs are also governed by the size constraint; the smaller the organ size, the smaller the available directional cues for directional hearing. However, with an auditory organ separation of only 520 µm, the fly Ormia ochracea is found to exhibit remarkable ability to pinpoint its host cricket at 5 kHz. The key to this fly's phenomenal directional hearing ability is believed to be the mechanical coupling between the eardrums. This innovative solution can inspire one to find alternative approaches to tackle the challenge of developing miniature directional microphones. The overall goal of this dissertation work is to unravel the underlying physics of the fly ear hearing mechanisms, and to apply this understanding to develop and study novel bio-inspired miniature directional microphones. First, through mechanics and optimization analysis, a fundamental biological conclusion is reached: the fly ear can be viewed as a nature-designed optimal structure that is endowed with the dual optimality characteristic of maximum average directional sensitivity and minimum nonlinearity, at its working frequency of 5 kHz. It is shown that this dual optimality characteristic is only achievable when the right mechanical coupling between the eardrums is used (i.e., proper contributions from both rocking and bending modes are used). More importantly, it is further revealed that the dual optimality characteristic of the fly ear is replicable in a synthetic device, whose structural parameters can be tailored to work at any chosen frequency. Next, a novel bio-inspired directional microphone with mechanically coupled diaphragms is designed to capture the essential dynamics of the fly ear. To study the performance of this design, a novel continuum mechanics model is developed, which features two coupling modules, one for the mechanical coupling of the two diaphragms through a beam and the other for each diaphragm coupled through an air gap. Parametric studies are carried out to explore how the key normalized parameters affect the performance of this directional microphone. Finally, this mechanics model is used to guide the development of a large-scale microphone and a fly-ear sized microphone, both of which are experimentally studied by using a low-coherence fiber optic interferometric detection system. With the large-scale sensor, the importance of using proper contribution from both rocking and bending modes is validated. The fly-ear sized sensor is demonstrated to achieve the dual optimality characteristic at 8 kHz with a ten-fold amplification in the directional sensitivity, which is equivalent to that obtainable from a conventional microphone pair that is ten times larger in size. To best use this sensor for sound source localization, a robotic platform with a control scheme inspired by the fly's localization/lateralization scheme is developed, with which a localization accuracy of better than ±2 degrees (the same as the fly ear) is demonstrated in an indoor lab environment. This dissertation work provides a quantitative and mechanistic explanation for the fly's sound localization ability for the first time, and it provides a framework for the development of fly-ear inspired acoustic sensors that will impact many fronts.