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We establish through numerical simulation conditions for optimal undulatory propulsion for a single fish, and for a pair of hydrodynamically interacting fish, accounting for linear and angular recoil. We first employ systematic 2D simulations to identify conditions for minimal propulsive power of a self-propelled fish, and continue with targeted 3D simulations for a danio-like fish. We find that the Strouhal number, phase angle between heave and pitch at the trailing edge, and angle of attack are principal parameters. Angular recoil has significant impact on efficiency, while optimized body bending requires maximum bending amplitude upstream of the trailing edge. For 2D simulations, imposing a deformation based on measured displacement for carangiform swimming provides efficiency of 40%, which increases for an optimized profile to 57%; for a 3D fish, the corresponding increase is from 22% to 35%; all at Reynolds number 5000.Next, we turn to 2D simulation of two hydrodynamically interacting fish. We find that the upstream fish benefits energetically only for small distances. In contrast, the downstream fish can benefit at any position that allows interaction with the upstream wake, provided its body motion is timed appropriately with respect to the oncoming vortices. For an in-line configuration, one body length apart, the optimal efficiency of the downstream fish can increase to 66%; for an offset arrangement it can reach 81%. This proves that in groups of fish, energy savings can be achieved for downstream fish through interaction with oncoming vortices, even when the downstream fish lies directly inside the jet-like flow of an upstream fish. † Current affiliation: EPFL,

The research community has increasing interest in autonomous driving research, despite the resource intensity of obtaining representative real world data. Existing selfdriving datasets are limited in the scale and variation of the environments they capture, even though generalization within and between operating regions is crucial to the overall viability of the technology. In an effort to help align the research community's contributions with real-world selfdriving problems, we introduce a new large scale, high quality, diverse dataset. Our new dataset consists of 1150 scenes that each span 20 seconds, consisting of well synchronized and calibrated high quality LiDAR and camera data captured across a range of urban and suburban geographies. It is 15x more diverse than the largest camera+LiDAR dataset available based on our proposed diversity metric. We exhaustively annotated this data with 2D (camera image) and 3D (LiDAR) bounding boxes, with consistent identifiers across frames. Finally, we provide strong baselines for 2D as well as 3D detection and tracking tasks. We further study the effects of dataset size and generalization across geographies on 3D detection methods. Find data, code and more up-todate information at http://www.waymo.com/open.

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A towed underwater vehicle equipped with a bio-inspired artificial lateral line (ALL) was constructed and tested with the goal of active detection and correction of the vehicle's yaw angle. Preliminary experiments demonstrate that a low number of sensors are sufficient to enable the discrimination between different orientations, and that a basic proportional controller is capable of keeping the vehicle aligned with the direction of flow. We propose that a model based controller could be developed to improve system response. Toward this, we derive a vehicle model based on a first-order 3D Rankine Source Panel Method, which is shown to be competent in estimating the pressure field in the region of interest during motion at constant angles, and during execution of dynamic maneuvers. To solve the inverse problem of estimating the vehicle orientation given specific pressure measurements, an Unscented Kalman Filter is developed around the model. It is shown to provide a close estimation of the vehicle state using experimentally collected pressure measurements. This demonstrates that an artificial lateral line is a promising technology for dynamically mediating the angle of a body relative to the oncoming flow. Michael Triantafyllou, who has supported and guided me throughout my thesis while encouraging me to work my own way. With his patience and knowledge, and impressive ability to draw ideas together into elaborate and inspiring pictures, he has been a powerful source of both guidance and inspiration. I would also like to thank the number of other professors and research scientists at MIT who have helped me along the way -in particular, Prof. David Trumper for his help in resolving the electrical noise issues within my experiment, and Yuming Liu for his help with the formulation of my panel method simulation.In my daily work I have been blessed with a friendly and cheerful group of fellow students and labmates. I would like to say a huge thank you especially to members of the Tow Tank Lab, Heather, Jeff, Audrey, James, Steph, and Jacob. The number of stimulating intellectual conversations we had both brightened my days and kept me thinking. Thank you also to Dr. Jason Dahl, who provided valuable advice in the brainstorming phase of this project and experimental design, and to our colleagues over in Singapore, who have offered helpful advice during our meetings.Finally, I would like to express my love and indebtedness toward my family and my friends. To my dear parents, Tracy and Johnway Gao, thanks for bearing with me through all the happy times and all the hard times, and being a constant source of support and encouragement. I couldn't have done it without you guys! Thanks also for taking me tuna fishing. To my twin brother Allan -my lovable squishy, thanks for always being there for me, for advice, good stories, and good times. To Leah -thanks for always being there to listen and for helping me through many of the most difficult times of the last two years. And last but certainly not least, thanks to my bo...