@inproceedings{11173de65620492c98c863d3f856661c,
title = "Work-in-Progress: Time-Aware Regional Coverage Search Using UGV-UAV Cluster Based on an Improved PPO Algorithm",
abstract = "Search time is an important metric for regional coverage searches conducted by unmanned clusters. This paper proposes an improved proximal policy optimization (IPPO) algorithm to decrease search time while ensuring the coverage rate for a heterogeneous cluster consisting of unmanned ground vehicles and unmanned aerial vehicles (UGV-UAV). The models of the UGV-UAV cluster, search scenario, and search constraints are first developed. Then, the IPPO algorithm is designed to simultaneously learn the cross-domain actions of UGVs and UAVs. The main advantage of the IPPO is that it can achieve the cross-domain cooperative learning (CDCL) mechanism, thus ensuring the collaboration consistency of the UGV-UAV cluster and enhancing search efficiency. To analyze the IPPO-based regional coverage search performance, three state-of-the-art (SOTA) methods are selected for comparison. The validation results demonstrate that the proposed method outperforms these SOTA methods in terms of both search time and coverage rate.",
keywords = "muli-agent system, proximal policy optimization, regional coverage search, search time, time-aware requirement",
author = "Ying Zhang and Rui Ding and Shuo Song and Jinchao Chen and Yingjie Zhang and Chenglie Du",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 46th IEEE Real-Time Systems Symposium, RTSS 2025 ; Conference date: 02-12-2025 Through 05-12-2025",
year = "2025",
doi = "10.1109/RTSS66672.2025.00053",
language = "英语",
series = "Proceedings - Real-Time Systems Symposium",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "580--583",
booktitle = "Proceedings - 2025 IEEE Real-Time Systems Symposium, RTSS 2025",
}