Optimized Area Coverage in Disaster Response Utilizing Autonomous UAV Swarm Formations

2025-12-08

AI Summary

Unmanned aerial vehicle (UAV) swarms face challenges in disaster response, including low area coverage efficiency, difficulty in real-time obstacle avoidance, and insufficient prioritized coverage of points of interest (POIs). Method: This paper proposes a distributed cooperative path planning framework integrating local Euclidean Signed Distance Fields (ESDFs) with an enhanced Traveling Salesman Problem (TSP) formulation. The approach maintains swarm formation integrity while dynamically assigning weighted priorities to POIs based on environmental dynamics and critical infrastructure value, enabling multi-UAV real-time collision avoidance and joint task optimization. Results: Simulation results demonstrate significantly reduced collision risk, consistently improved POI coverage efficiency and sensing reliability across varying swarm scales, and robust, long-duration autonomous cooperative operation.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This paper presents a UAV swarm system designed to assist first responders in disaster scenarios like wildfires. By distributing sensors across multiple agents, the system extends flight duration and enhances data availability, reducing the risk of mission failure due to collisions. To mitigate this risk further, we introduce an autonomous navigation framework that utilizes a local Euclidean Signed Distance Field (ESDF) map for obstacle avoidance while maintaining swarm formation with minimal path deviation. Additionally, we incorporate a Traveling Salesman Problem (TSP) variant to optimize area coverage, prioritizing Points of Interest (POIs) based on preassigned values derived from environmental behavior and critical infrastructure. The proposed system is validated through simulations with varying swarm sizes, demonstrating its ability to maximize coverage while ensuring collision avoidance between UAVs and obstacles.

Problem

• Optimizes area coverage for disaster response using autonomous UAV swarms.
• Enhances flight duration and data availability through distributed sensor networks.
• Ensures collision avoidance and formation maintenance via local ESDF mapping.

Innovation

• UAV swarm with distributed sensors for extended flight
• ESDF-based navigation for obstacle avoidance and formation
• TSP variant optimizes coverage prioritizing critical POIs