Edge-Enabled UAV Swarm Deployment for Rapid Post-Disaster Search and Rescue

2025-11-03

AI Summary

In post-disaster multi-target regional collaborative search and rescue, jointly optimizing sensing and communication for unmanned aerial vehicle (UAV) swarms remains challenging. Method: This paper proposes a distributed radar-communication integrated (JRC) deployment framework for UAV clusters, jointly optimizing 3D UAV positions and transmit power allocation under NP-hard constraints to simultaneously maximize radar detection performance and communication quality across multiple target regions. Contribution/Results: We introduce a lightweight action-reward mechanism and a multi-agent distributed optimization architecture, reducing computational complexity to polynomial time with iteration count linear in the number of UAVs. Simulation results demonstrate that the proposed method outperforms existing benchmarks in key metrics—including target detection probability, communication rate, and coverage uniformity—while exhibiting strong scalability and real-time deployability.

Technology Category

• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

This paper presents an optimized Joint Radar-Communication (JRC) system utilizing multiple Unmanned Aerial Vehicles (UAVs) to simultaneously achieve sensing and communication objectives. By leveraging UAVs equipped with dual radar and communication capabilities, the proposed framework aims to maximize radar sensing performance across all UAVs in challenging environments. The proposed approach focuses on formulating and solving a UAV positioning and power allocation problem to optimize multi-UAV sensing and communications performance over multiple targets within designated zones. Due to the NP-hard and combinatorial nature of the problem, we propose a Distributed JRC-based (DJRC) solution. This solution employs an efficient reward for potential actions and consistently selects the best action that maximizes the reward while ensuring both communications and sensing performance. Simulation results demonstrate significant performance improvements of the proposed solution over state-of-the-art radar- or communication-centric trajectory planning methods, with polynomial complexity dependent on the number of UAVs and linear dependence on the iteration count.

Problem

• Optimizing UAV positioning and power allocation for search and rescue
• Maximizing radar sensing performance in challenging disaster environments
• Solving NP-hard joint radar-communication optimization for multiple targets

Innovation

• UAVs with dual radar-communication capabilities
• Distributed JRC solution optimizing positioning and power
• Polynomial complexity algorithm maximizing sensing and communication