Calibration of sound source localisation for robots using multiple adaptive filter models of the cerebellum

The aim of this research was to investigate the calibration of Sound Source Localisation (SSL) for robots using the adaptive filter model of the cerebellum and how this could be automatically adapted for multiple acoustic environments. The role of the cerebellum has mainly been identified in the context of motor control, and only in recent years has it been recognised that it has a wider role to play in the senses and cognition. The adaptive filter model of the cerebellum has been successfully applied to a number of robotics applications but so far none involving auditory sense. Multiple models frameworks such as MOdular Selection And Identification for Control (MOSAIC) have also been developed in the context of motor control, and this has been the inspiration for adaptation of audio calibration in multiple acoustic environments; again, application of this approach in the area of auditory sense is completely new. The thesis showed that it was possible to calibrate the output of an SSL algorithm using the adaptive filter model of the cerebellum, improving the performance compared to the uncalibrated SSL. Using an adaptation of the MOSAIC framework, and specifically using responsibility estimation, a system was developed that was able to select an appropriate set of cerebellar calibration models and to combine their outputs in proportion to how well each was able to calibrate, to improve the SSL estimate in multiple acoustic contexts, including novel contexts. The thesis also developed a responsibility predictor, also part of the MOSAIC framework, and this improved the robustness of the system to abrupt changes in context which could otherwise have resulted in a large performance error. Responsibility prediction also improved robustness to missing ground truth, which could occur in challenging environments where sensory feedback of ground truth may become impaired, which has not been addressed in the MOSAIC literature, adding to the novelty of the thesis. The utility of the so-called cerebellar chip has been further demonstrated through the development of a responsibility predictor that is based on the adaptive filter model of the cerebellum, rather than the more conventional function fitting neural network used in the literature. Lastly, it was demonstrated that the multiple cerebellar calibration architecture is capable of limited self-organising from a de-novo state, with a predetermined number of models. It was also demonstrated that the responsibility predictor could learn against its model after self-organisation, and to a limited extent, during self-organisation. The thesis addresses an important question of how a robot could improve its ability to listen in multiple, challenging acoustic environments, and recommends future work to develop this ability

Underwater & out of sight: towards ubiquity in underwater robotics

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution September 2019.The Earth's oceans holds a wealth of information currently hidden from us. Effective measurement of its properties could provide a better understanding of our changing climate and insights into the creatures that inhabit its waters. Autonomous underwater vehicles (AUVs) hold the promise of penetrating the ocean environment and uncovering its mysteries; and progress in underwater robotics research over the past three decades has resulted in vehicles that can navigate reliably and operate consistently, providing oceanographers with an additional tool for studying the ocean. Unfortunately, the high cost of these vehicles has stifled the democratization of this technology. We believe that this is a consequence of two factors. Firstly, reliable navigation on conventional AUVs has been achieved through the use of a sophisticated sensor system, namely the Doppler velocity log (DVL)-aided inertial navigation system (INS), which drives up vehicle cost, power use and size. Secondly, deployment of these vehicles is expensive and unwieldy due to their complexity, size and cost, resulting in the need for specialized personnel for vehicle operation and maintenance. The recent development of simpler, low-cost, miniature underwater robots provides a solution that mitigates both these factors; however, removing the expensive DVL-aided INS means that they perform poorly in terms of navigation accuracy. We address this by introducing a novel acoustic system that enables AUV self-localization without requiring a DVL-aided INS or on-board active acoustic transmitters. We term this approach Passive Inverted Ultra-Short Baseline (piUSBL) positioning. The system uses a single acoustic beacon and a time-synchronized, vehicle-mounted, passive receiver array to localize the vehicle relative to this beacon. Our approach has two unique advantages: first, a single beacon lowers cost and enables easy deployment; second, a passive receiver allows the vehicle to be low-power, low-cost and small, and enables multi-vehicle scalability. Providing this new generation of small and inexpensive vehicles with accurate navigation can potentially lower the cost of entry into underwater robotics research and further its widespread use for ocean science. We hope that these contributions in low-cost underwater navigation will enable the ubiquitous and coordinated use of robots to explore and understand the underwater domain.This research was funded and supported by a number of sponsors; we gratefully acknowledge them below. Defense Advanced Research Projects Agency (DARPA) and SSC Pacific via Applied Physical Sciences Corp. (APS) under contract number N66001-11-C-4115. SSC Pacific via Applied Physical Sciences Corp. (APS) under award number N66001-14-C-4031. Air Force via Lincoln Laboratory under award number FA8721-05-C-0002. Office of Naval Research (ONR) via University of California-San Diego under award number N00014-13-1-0632. Defense Advanced Research Projects Agency (DARPA) via Applied Physical Sciences Corp. (APS) under award number HR0011-18-C-0008. Office of Naval Research (ONR) under award number N00014-17-1-2474