Hierarchical Mapping-Partitioning-Search with Attention-Weighted Communication for UAV Swarms in Search and Rescue Operations
subject: Attention-Weighted Communication; Coverage Planning; Deep Reinforcement Learning; Hierarchical Search; Multi-Agent Systems; Quadtree Partitioning; Search and Rescue; UAV Swarms
description: UAV swarm Search and Rescue (SAR) operations demand intelligent coordination to function efficiently in unfamiliar terrains while maintaining communication under bandwidth limitations. To address this, we propose a Hierarchical Mapping-Partitioning-Search (HMPS) framework that combines quadtree-based adaptive partitioning of the search area with deep reinforcement learning for region selection, together with an Attention-Weighted Flooding (AWF) communication protocol to enhance swarm coordination. The HMPS framework adapts search granularity to uncertainty and obstacle density, uses a Deep Q-Network (DQN) to learn a region-selection policy, and employs a lightweight local coverage planner to improve exploration efficiency. The AWF protocol prioritizes message relays based on content and link quality, reducing bandwidth while preserving essential information flow. This paper presents HMPS as a practical option for autonomous swarm SAR operations, and reports encouraging preliminary results in GPS-denied terrains. 2025 IEEE.
creator: Khetal, Ashish Ashok; Joy, Helen K.; Wagh, Rupali Sunil
source: IC-DECON 2025 - 2025 International Conference on Data, Energy and Communication Network, Proceedings;
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 01-01-2025
rights: Restricted Access; Hardcopy may be available in the library
relation: ISBN: 979-833159442-8;
language: English
type: Conference paper
coverage: Khetal A.A., Christ University, Department of Computer Science, Bangalore, India; Joy H.K., Christ University, Department of Computer Science, Bangalore, India; Wagh R.S., Christ University, Department of Computer Science, Bangalore, India