Hybrid-Neuromorphic Approach for Underwater Robotics Applications: A Conceptual Framework

TL;DR

A unified framework for integrating neuromorphic technologies for perception, pose estimation, and haptic-guided conditional control of underwater vehicles, customized to specific user-defined objectives is proposed.

Abstract

This paper introduces the concept of employing neuromorphic methodologies for task-oriented underwater robotics applications. In contrast to the increasing computational demands of conventional deep learning algorithms, neuromorphic technology, leveraging spiking neural network architectures, promises sophisticated artificial intelligence with significantly reduced computational requirements and power consumption, emulating human brain operational principles. Despite documented neuromorphic technology applications in various robotic domains, its utilization in marine robotics remains largely unexplored. Thus, this article proposes a unified framework for integrating neuromorphic technologies for perception, pose estimation, and haptic-guided conditional control of underwater vehicles, customized to specific user-defined objectives. This conceptual framework stands to revolutionize underwater robotics, enhancing efficiency and autonomy while reducing energy consumption. By enabling greater adaptability and robustness, this advancement could facilitate applications such as underwater exploration, environmental monitoring, and infrastructure maintenance, thereby contributing to significant progress in marine science and technology.

Figures (3)

• Figure 2: High-level framework of a task-oriented underwater control.
• Figure 3: Schematic representation of the task-driven control framework for underwater vehicles. The highlighted blocks in 'red' denote the incorporation of neuromorphic principles within the depicted modules.
• Figure 4: Schematic representation of spike-based CNN-LSTM hybrid framework