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.

翻译：本文提出了一种冲突感知的资源高效分散式序贯规划器（CREDS），用于利用多架异构无人机（UAV）进行早期野火缓解。多无人机野火管理场景具有非平稳性，表现为空间聚集的动态蔓延火势、潜在的突发火情，以及由于无人机数量和感知范围有限导致的部分可观测性。CREDS的目标是检测并序贯缓解所有正在蔓延的火情，将其作为单无人机任务（SUT），通过快速的无人机干预最小化生物多样性损失，并通过避免复杂的多无人机协调来促进高效的资源利用。CREDS采用三阶段方法：首先使用搜索算法进行火情检测，随后使用基于拍卖的资源高效分散式序贯规划器（REDS）生成本地轨迹，该规划器引入了新颖的非平稳成本函数——截止期优先缓解成本（DPMC）。最后，一个冲突感知共识算法解决冲突，以确定用于时空缓解的全局轨迹。针对部分和完全可观测条件下，异构和同构无人机团队在不同火情-无人机比例下的性能评估表明，CREDS在比例高达$4$时成功率为$100\%$，在关键比例$5$时也保持高成功率，优于基线方法。异构无人机团队在处理SUT缓解的异构截止期方面优于同构团队。CREDS展现出可扩展性和$100\%$的收敛性，证明了其针对潜在死锁分配的鲁棒性，从而相比基线方法提高了成功率。