一种用于时空野火缓解的资源高效去中心化序列规划器
A Resource-Efficient Decentralized Sequential Planner for Spatiotemporal Wildfire Mitigation
AI总结：
本文提出CREDS去中心化序列规划器，通过三阶段方法（检测、基于拍卖的轨迹生成、冲突感知共识）利用异构无人机高效缓解野火，在火灾与无人机比例不超过4时成功率达100%，展现出高可扩展性与鲁棒性。
摘要：
本文提出了一种冲突感知资源高效去中心化序列规划器（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是非平稳的，具有空间聚集的动态蔓延火灾、潜在的突发火灾，以及由于无人机数量和传感范围有限导致的部分可观测性。CREDS的目标是检测并按顺序缓解所有正在蔓延的火灾，将其作为单无人机任务（SUT），通过快速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促进资源高效利用。CREDS采用三阶段方法，首先使用搜索算法进行火灾检测，随后使用基于拍卖的资源高效去中心化序列规划器（REDS）进行局部轨迹生成，并融入了新颖的非平稳成本函数——截止期限优先缓解成本（DPMC）。最后，冲突感知共识算法解决冲突，确定用于时空缓解的全局轨迹。在部分和完全可观测条件下，针对不同火灾与无人机比例的异构和同构无人机团队对CREDS的性能评估表明，在比例不超过4时成功率为100%，在关键比例5时也具有高成功率，优于基线方法。异构无人机团队在处理SUT缓解的异构截止期限方面优于同构团队。CREDS展现出可扩展性和100%的收敛性，证明了对潜在死锁分配的鲁棒性，相比基线方法提高了成功率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
