Efficient Hydrodynamic Modeling for Amphibious Mining Robots using Deep Reinforcement Learning

Lead Researcher: Alnenla L.
Collaborative Intelligence Laboratory

Abstract: This paper presents a novel framework for the autonomous navigation of amphibious robots in complex mining environments. By integrating spatial-temporal feature extraction with a multi-modal sensor fusion approach, we demonstrate a 15% increase in energy efficiency and pathfinding accuracy.

1. Introduction

The development of amphibious robotics has seen a surge in interest due to the increasing demand for deep-sea and sub-surface mineral extraction. However, the non-linear fluid dynamics at the water-land interface pose significant challenges for traditional control algorithms.

2. Proposed Methodology

We propose a Deep Spatial-Temporal (DST) model that leverages recurrent neural networks (RNNs) to predict fluid resistance in real-time. The system utilizes a distributed sensor network to capture micro-fluctuations in pressure.

3. Resources