Development of small biomimetic robotic fish with onboard fine-grained localization

The recent interest in migrating dense wireless sensor networks to aquatic environments has challenged the existing protocols that once were sufficient in ground-based networks. One challenge is achieving decimeter-scale localization resolution underwater, for use in dense aquatic wireless sensor networks. Firstly, this thesis presents the development of a mobile platform in the form of a small biomimetic robotic fish, meeting node constraints for building a scalable and dense aquatic mobile sensing network. Secondly, this thesis investigates inter-node ranging using time-of-flight of underwater acoustics. Four methods of estimating signal arrival time is investigated, with emphasis on a recursive algorithm (SDFT) based on the discrete Fourier transform and capable of joint time-frequency analysis. Analytical and empirical study of the SDFT method reveals signal transients to be the root cause of detection latency. A method for compensation is outlined and implemented online, allowing for detection within 1.4 wavelengths of the ranging signal. Finally, the robotic fish is tracked, demonstrating onboard fine-grained localization capabilities of the platform.