Autonomous, Localization-Free Underwater Data Muling Using Acoustic and Optical Communication

We present a fully autonomous data muling system consisting of hardware and algorithms. The system allows a robot to autonomously find a sensor node and use high bandwidth, short range optical communication to download 1.2 MB of data from the sensor node and then transport the data back to a base station. The hardware of the system consists of an autonomous underwater vehicle (AUV) paired with an underwater sensor node. The robot and the sensor node use two modes of communication acousic for long range communication and and optical for high bandwidth communication. No positioning system is required. Acoustic ranging is used between the sensor node and the AUV. The AUV uses the ranging information to find the sensor node by means of either stochastic gradient descent, or a particle filter. Once it comes close enough to the sensor node where it can use the optical channel it switches to position keeping by means of stochastic gradient descent on the signal quality of the optical link. During this time the optical link is used to download data. Fountain codes are used for data transfer to maximize throughput while minimizing protocol requirements. The system is evaluated in three separate experiments using our Autonomous Modular Optical Underwater Robot (AMOUR), a PANDA sensor node, the UNET acoustic modem, and the AquaOptical modem. In the first experiment AMOUR uses acoustic gradient descent to find the PANDA node starting from a distance of at least 25m and then switches to optical position keeping during which it downloads a 1.2 MB large file. This experiment is completed 10 times successfully. In the second experiment AMOUR is manually steered above the PANDA node and then autonomously maintains position using the quality of the optical link as a measurement. This experiment is performed two times for 10 minutes. The final experiment does not make use of the optical modems and evaluate the performance of the particle filter in finding the PANDA node. This experiment is performed 5 times successfully. Marek Doniec, Daniela Rus Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge, Massachusetts Iulian Topor, Mandar Chitre ARL, Tropical Marine Science Institute National University of Singapore Singapore e-mail: [email protected], [email protected], [email protected], [email protected]