  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.
