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Research on Q-Learning-Based Cooperative Optimization Methodology for Dynamic Task Scheduling and Energy Consumption

Shan Tao1,2, Lei Yang1,2, Xiaobo Zhang1

  • 1College of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China.

Sensors (Basel, Switzerland)
|August 14, 2025
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Summary

This study introduces an intelligent underwater pan-tilt system that saves energy by activating only when a target is detected. An adaptive Q-learning algorithm optimizes power modes based on biological activity, improving monitoring and reducing energy use.

Keywords:
Q-learningautomatic wake-upenergy consumption optimizationunderwater pan-tilt system

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Area of Science:

  • Robotics and Automation
  • Marine Technology
  • Artificial Intelligence

Background:

  • Underwater pan-tilt systems face challenges with energy consumption due to harsh operating environments.
  • Conventional systems rely on timer-based triggering and fixed observation durations, leading to inefficiencies.
  • Effective energy management is critical for the sustained operation of underwater robotic systems.

Purpose of the Study:

  • To propose an energy-efficient underwater pan-tilt operation method using an automatic wake-up mechanism.
  • To develop a Q-learning algorithm for optimizing operational modes based on real-time environmental conditions.
  • To enhance monitoring effectiveness and reduce energy consumption in underwater surveillance tasks.

Main Methods:

  • Implemented an automatic wake-up mechanism triggered by target detection, replacing traditional timers.
  • Introduced a Q-learning algorithm to dynamically adjust system modes (low-power vs. high-performance) based on biological activity frequency.
  • Simulated the proposed strategy against fixed-duration observation schemes.

Main Results:

  • The proposed strategy demonstrated a 11.11% improvement in monitoring effectiveness compared to fixed-duration methods.
  • Achieved significant energy savings of 16.21% through dynamic mode adjustment.
  • The automatic wake-up mechanism enhanced system responsiveness and reduced unnecessary power usage.

Conclusions:

  • The developed underwater pan-tilt operation method significantly improves energy efficiency and monitoring effectiveness.
  • Q-learning-based dynamic mode optimization is a viable strategy for underwater robotic systems.
  • This approach offers a more sustainable solution for long-term underwater monitoring applications.