Optimized Area Coverage in Disaster Response Utilizing Autonomous UAV Swarm Formations

TL;DR

This paper introduces an autonomous UAV swarm system for disaster response that optimizes area coverage and obstacle avoidance using ESDF maps and TSP-based planning, validated through simulations.

Contribution

It presents a novel integrated framework combining ESDF-based navigation and TSP optimization for UAV swarms in disaster scenarios, enhancing coverage and safety.

Findings

Effective collision avoidance demonstrated in simulations

Optimized area coverage with prioritized POIs

Extended flight duration through sensor distribution

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.