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Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

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Dynamic robotic tracking of underwater targets using reinforcement learning.

I Masmitja^1,2, M Martin^3,4, T O'Reilly²

¹Institut de Ciències del Mar (ICM), CSIC, Barcelona 95062, Spain.

Science Robotics

|July 26, 2023

Summary

Deep reinforcement learning optimizes autonomous underwater robot path planning for tracking marine animals. This advancement enhances ocean observation capabilities and aids in understanding deep-sea populations and conservation strategies.

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Area of Science:

Marine robotics
Deep-sea ecology
Artificial intelligence in oceanography

Background:

Autonomous underwater robots offer scalable ocean observation but require advanced techniques for complex tasks.
Tracking mobile underwater targets is crucial for studying marine animal connectivity and deep-sea population distribution, representing a significant knowledge gap.
Current methods for sensor placement and path planning in dynamic underwater environments are challenging.

Purpose of the Study:

To investigate the application of reinforcement learning for optimizing range-only underwater target tracking.
To evaluate the effectiveness of reinforcement learning as a path planning system for autonomous surface vehicles tracking mobile underwater targets.

Main Methods:

Implementation of a reinforcement learning method as a path planning system for an autonomous surface vehicle.
Evaluation using an open-source model, performance metrics in simulated environments, and over 15 hours of at-sea field experiments.

Main Results:

Demonstrated the successful application of deep reinforcement learning for autonomous underwater target tracking.
Validated the approach through simulations and extensive real-world ocean experiments.

Conclusions:

Deep reinforcement learning is a powerful tool for enhancing the capabilities of autonomous robots in marine environments.
This approach encourages the deployment of advanced algorithms for effective monitoring of marine biological systems.