Fine-grained (sub-meter) ranging and localization is critical to the deployment of dense, mobile sensor networks in aquatic environments. However, such a task is faced with a number of challenges, including noisy underwater environments, limitation on size and complexity of localization hardware, and constraints on computing capabilities of sensor platforms. In this paper we present a sliding-window discrete Fourier transform (DFT)-based algorithm for precise detection of the arrival of a monotone acoustic signal, as a key enabling step in measuring the time of flight (TOF) of the acoustic signal for localization of the sensor node. The algorithm accommodates the rise dynamics of the signal and compensates for the latency in detection given the signal model, detection threshold, and steady-state signal amplitude. The algorithm is implemented onboard a small biomimetic robotic fish, and experiments in an indoor pool have shown that the proposed method results in an underwater ranging error of 1.4 wavelengths (74.3 cm), and is thus promising for localization of dense aquatic networks. The proposed method has also shown robustness in comparison with other tested methods including a matched filter-type method.

In this paper a physics-based model is proposed for a biomimetic robotic fish propelled by an ionic polymermetal composite (IPMC) actuator. Inspired by biological fins, a passive plastic foil is attached to the IPMC beam. The model incorporates both IPMC actuation dynamics and the hydrodynamics, and captures the interactions between the IPMC actuator and the plastic foil. Experimental results have shown that the proposed model is able to predict the steady-state cruising speed of the robotic fish under a periodic actuation input. Since the majority of the model parameters are expressed in terms of fundamental physical properties and geometric dimensions, the model is expected to be instrumental in optimal design of the robotic fish.

GPS-free localization is essential for navigation and information tagging in small robotic fish-based aquatic mobile sensor networks. Constraints on size, weight, and onboard computing power, together with noisy underwater environment, have made underwater localization a particularly challenging problem. In this paper a comprehensive experimental study is presented on underwater acoustic ranging methods based on time difference of arrival (TDOA). Performances of four methods are characterized and analyzed, including threshold-crossing, tone-detection, correlation integral, and sliding-window fast Fourier transform (FFT) methods. The study facilitates the understanding of capabilities and limitations of different underwater ranging methods and provides important insight into choice of hardware and algorithms for localization of networks of small aquatic robots.