# Research on Q-Learning-Based Cooperative Optimization Methodology for Dynamic Task Scheduling and Energy Consumption in Underwater Pan-Tilt Systems

## TL;DR

This paper introduces a Q-learning method to optimize energy use and task scheduling in underwater pan-tilt systems, improving monitoring effectiveness while saving energy.

## Contribution

A novel Q-learning-based cooperative optimization methodology for dynamic task scheduling and energy consumption in underwater systems.

## Key findings

- The proposed method achieves 11.11% improvement in monitoring effectiveness.
- It saves 16.21% energy compared to fixed-duration observation strategies.

## Abstract

Given the harsh working conditions of underwater pan-tilt systems, their energy consumption management is particularly crucial. This study proposes an underwater pan-tilt operation method with an automatic wake-up mechanism, which activates only upon target detection, replacing conventional timer-based triggering. Furthermore, departing from fixed-duration observation strategies, we introduce a Q-learning algorithm to optimize operational modes. The algorithm dynamically adjusts working modes based on surrounding biological activity frequency: employing a low-power mode (reduced energy consumption with lower monitoring intensity) during periods of sparse biological presence and switching to a high-performance mode (extended observation duration, higher energy consumption, and enhanced monitoring intensity) during frequent biological activity. Simulation results demonstrate that compared to fixed-duration observation schemes, the proposed optimization strategy achieves a 11.11% improvement in monitoring effectiveness while achieving 16.21% energy savings.