Research on Q-Learning-Based Cooperative Optimization Methodology for Dynamic Task Scheduling and Energy Consumption in Underwater Pan-Tilt Systems
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-ba...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-08-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/15/4785 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849239752985804800 |
|---|---|
| author | Shan Tao Lei Yang Xiaobo Zhang Shengya Zhao Kun Liu Xinran Tian Hengxin Xu |
| author_facet | Shan Tao Lei Yang Xiaobo Zhang Shengya Zhao Kun Liu Xinran Tian Hengxin Xu |
| author_sort | Shan Tao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-bac8dc40cdac4dd2bcc4163d48281752 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-bac8dc40cdac4dd2bcc4163d482817522025-08-20T04:00:50ZengMDPI AGSensors1424-82202025-08-012515478510.3390/s25154785Research on Q-Learning-Based Cooperative Optimization Methodology for Dynamic Task Scheduling and Energy Consumption in Underwater Pan-Tilt SystemsShan Tao0Lei Yang1Xiaobo Zhang2Shengya Zhao3Kun Liu4Xinran Tian5Hengxin Xu6College of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaNational Deep Sea Center, Qingdao 266237, ChinaNational Deep Sea Center, Qingdao 266237, ChinaCollege of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Transportation, Shandong University of Science and Technology, Qingdao 266590, ChinaGiven 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.https://www.mdpi.com/1424-8220/25/15/4785underwater pan-tilt systemenergy consumption optimizationautomatic wake-upQ-learning |
| spellingShingle | Shan Tao Lei Yang Xiaobo Zhang Shengya Zhao Kun Liu Xinran Tian Hengxin Xu Research on Q-Learning-Based Cooperative Optimization Methodology for Dynamic Task Scheduling and Energy Consumption in Underwater Pan-Tilt Systems Sensors underwater pan-tilt system energy consumption optimization automatic wake-up Q-learning |
| title | Research on Q-Learning-Based Cooperative Optimization Methodology for Dynamic Task Scheduling and Energy Consumption in Underwater Pan-Tilt Systems |
| title_full | Research on Q-Learning-Based Cooperative Optimization Methodology for Dynamic Task Scheduling and Energy Consumption in Underwater Pan-Tilt Systems |
| title_fullStr | Research on Q-Learning-Based Cooperative Optimization Methodology for Dynamic Task Scheduling and Energy Consumption in Underwater Pan-Tilt Systems |
| title_full_unstemmed | Research on Q-Learning-Based Cooperative Optimization Methodology for Dynamic Task Scheduling and Energy Consumption in Underwater Pan-Tilt Systems |
| title_short | Research on Q-Learning-Based Cooperative Optimization Methodology for Dynamic Task Scheduling and Energy Consumption in Underwater Pan-Tilt Systems |
| title_sort | research on q learning based cooperative optimization methodology for dynamic task scheduling and energy consumption in underwater pan tilt systems |
| topic | underwater pan-tilt system energy consumption optimization automatic wake-up Q-learning |
| url | https://www.mdpi.com/1424-8220/25/15/4785 |
| work_keys_str_mv | AT shantao researchonqlearningbasedcooperativeoptimizationmethodologyfordynamictaskschedulingandenergyconsumptioninunderwaterpantiltsystems AT leiyang researchonqlearningbasedcooperativeoptimizationmethodologyfordynamictaskschedulingandenergyconsumptioninunderwaterpantiltsystems AT xiaobozhang researchonqlearningbasedcooperativeoptimizationmethodologyfordynamictaskschedulingandenergyconsumptioninunderwaterpantiltsystems AT shengyazhao researchonqlearningbasedcooperativeoptimizationmethodologyfordynamictaskschedulingandenergyconsumptioninunderwaterpantiltsystems AT kunliu researchonqlearningbasedcooperativeoptimizationmethodologyfordynamictaskschedulingandenergyconsumptioninunderwaterpantiltsystems AT xinrantian researchonqlearningbasedcooperativeoptimizationmethodologyfordynamictaskschedulingandenergyconsumptioninunderwaterpantiltsystems AT hengxinxu researchonqlearningbasedcooperativeoptimizationmethodologyfordynamictaskschedulingandenergyconsumptioninunderwaterpantiltsystems |