用神经网络预测关键位置，让多机器人更高效搜寻紧急区域。
Learning-Augmented Model-Based Multi-Robot Planning for Time-Critical Search and Inspection Under Uncertainty
- 用图神经网络从杂乱传感器数据中判断需关注地点
- 相比基线方法，1~5个机器人的搜索效率提升16.3%~26.7%
- 适用于灾后搜救、巡检等高时效性场景
在灾害响应或监视任务中，快速识别急需处理的区域至关重要，但派遣救援队到所有地点既低效又常不可行。高效执行需协调多机器人团队优先检查更可能需要干预的地点，同时最小化移动时间。这尤其困难，因机器人必须直接观测才能判断是否需额外关注。本文提出一种面向不确定环境下时间敏感的多机器人协同搜索规划框架。该方法利用图神经网络（GNN）从噪声传感器数据中估计兴趣点（PoIs）需关注的概率，并以此指导基于模型的多机器人规划器生成成本效益最优的路径。模拟实验表明，与非学习及学习型基线相比，本方法在1、3、5台机器人情况下性能分别提升至少16.3%、26.7%和26.2%。此外，我们在四旋翼无人机平台上验证了该方法的有效性。
原文摘要 · Abstract (English)
In disaster response or surveillance operations, quickly identifying areas needing urgent attention is critical, but deploying response teams to every location is inefficient or often impossible. Effective performance in this domain requires coordinating a multi-robot inspection team to prioritize inspecting locations more likely to need immediate response, while also minimizing travel time. This is particularly challenging because robots must directly observe the locations to determine which ones require additional attention. This work introduces a multi-robot planning framework for coordinated time-critical multi-robot search under uncertainty. Our approach uses a graph neural network to estimate the likelihood of PoIs needing attention from noisy sensor data and then uses those predictions to guide a multi-robot model-based planner to determine the cost-effective plan. Simulated experiments demonstrate that our planner improves performance at least by 16.3\%, 26.7\%, and 26.2\% for 1, 3, and 5 robots, respectively, compared to non-learned and learned baselines. We also validate our approach on real-world platforms using quad-copters.
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