用类脑计算提升水下机器人的能效与自主性。
Hybrid-Neuromorphic Approach for Underwater Robotics Applications: A Conceptual Framework
- 结合脉冲神经网络降低水下机器人计算功耗。
- 构建感知、位姿估计与触觉控制统一框架。
- 适合需长时续航的深海探测与监测任务。
本文提出一种面向水下机器人任务的类脑计算概念框架。相较于传统深度学习算法日益增长的算力需求，类脑技术依托脉冲神经网络架构，可在显著降低计算开销和能耗的前提下实现复杂人工智能功能，模拟人脑工作原理。尽管类脑技术已在多个机器人领域取得应用，其在海洋机器人中的探索仍处于空白。为此，本文设计了一套统一框架，用于集成类脑技术实现水下车辆的感知、位姿估计及触觉引导的条件控制，可按用户定义目标定制。该框架有望革新水下机器人，提升效率与自主性，同时降低能耗。通过增强适应性与鲁棒性，该技术可推动深海探测、环境监测与基础设施维护等应用发展，助力海洋科技进步。
原文摘要 · Abstract (English)
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.
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