Project CETI (Cetacean Translation Initiative) aims to gather millions, if not billions, of high-quality, contextualized vocalizations to decode the communication of sperm whales. However, the difficulty in locating the whales and predicting where they will surface has made it challenging to attach listening devices and collect visual data.
To address this, a research team from Project CETI, led by Stephanie Gil, Assistant Professor of Computer Science at Harvard's John A. Paulson School of Engineering and Applied Sciences, has developed a new reinforcement learning framework with autonomous drones to track sperm whales and predict their surfacing locations.
The study integrates various sensing devices, including Project CETI’s drones equipped with very high-frequency (VHF) signal sensing capabilities. By leveraging signal phase and drone motion, the system effectively mimics an 'antenna array in air' to estimate the directionality of pings received from tags attached to the whales. These measurements, along with predictive models of sperm whale dive behavior, allow the team to forecast when and where the whales will surface. This capability enables the design of efficient drone routes for rendezvousing with whales at the surface, opening the door for conservation efforts such as preventing ship strikes on whales.
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The researchers introduced the AVATARS (Autonomous Vehicles for whAle Tracking And Rendezvous by remote Sensing) framework, which involves two key components: autonomy and sensing. The autonomy system determines the optimal positioning commands for the drones, while the sensing system measures the angle-of-arrival (AOA) from the whale tags to guide decision-making. The AVATARS framework combines data from the autonomous drones, underwater sensors, and whale motion models to minimize missed rendezvous opportunities with whales.
AVATARS is the first integration of VHF sensing and reinforcement learning for maximizing encounters between robots and whales in the ocean, resembling the real-time tracking used in rideshare apps to match drivers with riders. Project CETI's system dynamically tracks whales and coordinates drone routes to meet them at the surface efficiently.
This breakthrough significantly furthers Project CETI’s objective of obtaining extensive, high-quality whale vocalizations by improving location estimates and routing algorithms, thus accelerating the project's progress.
“I’m excited to contribute to this breakthrough for Project CETI. By leveraging autonomous systems and advanced sensor integration, we’re overcoming critical challenges in studying whales in their natural habitats. This is a technological leap that will aid in understanding the complex communications of these incredible creatures,” said Gil.
David Gruber, Founder and Lead of Project CETI, noted, “This research marks a major milestone for Project CETI. We can now gather large-scale datasets on whale vocalizations and their behavioral context, bringing us closer to understanding what sperm whales are trying to communicate.”
“This interdisciplinary work combines wireless sensing, AI, and marine biology, exemplifying how robotics can help us unlock the social behaviors of sperm whales,” said Ninad Jadhav, Harvard PhD candidate and first author of the paper.
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