A. James Clark School of Engineering
Permanent URI for this communityhttp://hdl.handle.net/1903/1654
The collections in this community comprise faculty research works, as well as graduate theses and dissertations.
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Item Bioinspired Robust Underwater Behaviors Using Fluid Flow Sensing(2017) Ranganathan, Badri Narayanan; Humbert, Sean; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The lateral line sense organ in fish detects fluid flow around its body, and is used to perform a wide variety of behaviors such as rheotaxis, wall-following, prey detection, and obstacle and predator avoidance. Currently there are no equivalent engineering analogues that can sense fluid flow perturbation to determine location of obstacles and demonstrate closed loop obstacle avoidance. In this dissertation we examine the potential and limitations of this sensor system with respect to obstacle detection, avoidance and rheotaxis. This dissertation presents the development of a novel bioinspired flow-based perception scheme for small and wide-field objects, design and development of a strain sensor system and a robust controller for closed loop demonstration of rheotaxis and small and wide field object detection and avoidance. Potential flow based models are developed for the above mentioned problems of interest. As the modeling technique is approximate, the uncertainties due to modeling and effect of rotation rate are accounted for and used in the synthesis of a robust H$_\infty$ control system. The perturbation signals are spatially decomposed using wide and small-field integration techniques to arrive at information regarding objects in the environment. A high-fidelity, computational fluid dynamic closed-loop simulation is carried out by interfacing control codes with an off-the-shelf software to demonstrate behaviors of rheotaxis, wall-following, tunnel centering and unstructured wide-field obstacle avoidance. A bio-inspired hair array sensor and its corresponding signal conditioning electronics were developed for detecting flow perturbations related to the behaviors of interest. The sensors that were manufactured were strain based and involved the use of micro and macro fabrication approaches. An instrumentation amplifier-based system was developed for signal conditioning. The hair array sensors along with the signal conditioning electronics weighed about 10 gms, which allows it to be easily carried on small scale fish robots. These sensors were integrated onto an airfoil-shaped robot and perturbation signals due to the motion of the robot near a wall and cylindrical objects were obtained and analyzed. The signals that have been measured by the sensor array help in quantifying the magnitude and structure of perturbation that is observed due to interaction with objects, and establishes requirements for sensor design for deployment on autonomous underwater vehicles. Closed loop behavior of rheotaxis was demonstrated in a flow tank.Item Stochastic Properties of Wide Field Integrated Optic Flow Measurements(2009) Owen, Scott; Humbert, James S; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Wide Field Integration (WFI) is a biologically inspired method of spatially decomposing optic flow estimates to extract relevant behavioral cues for navigation. In this thesis, a framework is developed that allows the direct application of a Kalman filter to improve the state information extracted from optic flow measurements. In addition, the noise properties of optic flow measurements are characterized, and an architecture to propagate the uncertainty in optic flow measurements to WFI state estimates is formalized. The closed-loop performance of a ground robot maneuvering in a straight tunnel using WFI outputs is then analyzed using three different algorithms to compute optic flow. The performance of the robot is characterized by its ability to track the tunnel centerline, and the accuracy of the WFI state estimates are compared with the true state estimates using a visual motion capture system. Lastly, the Kalman filter is implemented on a ground robot and the modified closed-loop performance is analyzed.