A Resource-Efficient Decentralized Sequential Planner for Spatiotemporal Wildfire Mitigation

TL;DR

This paper introduces CREDS, a resource-efficient decentralized planner for wildfire mitigation using UAVs, which detects and mitigates fires effectively with high success rates and scalability, reducing coordination complexity.

Contribution

The paper presents a novel three-phased decentralized planning approach with a non-stationary cost function and conflict resolution for wildfire management by UAVs.

Findings

CREDS achieves 100% success rate for UAV ratios up to 4.

Heterogeneous UAV teams outperform homogeneous teams in complex scenarios.

CREDS demonstrates scalability and robustness with high convergence and success rates.

Abstract

This paper proposes a Conflict-aware Resource-Efficient Decentralized Sequential planner (CREDS) for early wildfire mitigation using multiple heterogeneous Unmanned Aerial Vehicles (UAVs). Multi-UAV wildfire management scenarios are non-stationary, with spatially clustered dynamically spreading fires, potential pop-up fires, and partial observability due to limited UAV numbers and sensing range. The objective of CREDS is to detect and sequentially mitigate all growing fires as Single-UAV Tasks (SUT), minimizing biodiversity loss through rapid UAV intervention and promoting efficient resource utilization by avoiding complex multi-UAV coordination. CREDS employs a three-phased approach, beginning with fire detection using a search algorithm, followed by local trajectory generation using the auction-based Resource-Efficient Decentralized Sequential planner (REDS), incorporating the novel non-stationary cost function, the Deadline-Prioritized Mitigation Cost (DPMC). Finally, a conflict-aware consensus algorithm resolves conflicts to determine a global trajectory for spatiotemporal mitigation. The performance evaluation of the CREDS for partial and full observability conditions with both heterogeneous and homogeneous UAV teams for different fires-to-UAV ratios demonstrates a $100\%$ success rate for ratios up to $4$ and a high success rate for the critical ratio of $5$, outperforming baselines. Heterogeneous UAV teams outperform homogeneous teams in handling heterogeneous deadlines of SUT mitigation. CREDS exhibits scalability and $100\%$ convergence, demonstrating robustness against potential deadlock assignments, enhancing its success rate compared to the baseline approaches.