用无人机提前探路，让地面机器人少走冤枉路。
Scout-Assisted Planning for Heterogeneous Robot Teams under Partially Known Environments
- 无人机根据信息增益优先探查关键路径，减少无效探索。
- 实验显示地面机器人行程成本降低31.9%至37.7%。
- 适合需要多机器人协同的未知环境导航任务。
在部分已知环境中，自主机器人团队常因地面机器人遭遇堵塞道路而产生高昂的回溯代价。本文提出一种异构规划框架Scout-Assisted Planning（SAP），通过无人机主动获取环境信息以优化地面机器人的导航。为聚焦于最具影响的路径探测，提出基于信息增益的动作剪枝方法，评估候选探测动作对地面机器人行为的预期影响。由于精确计算信息增益成本过高，我们构建图神经网络模型，直接从图结构和信念状态预测信息增益值，使规划时间达到实时水平且不牺牲解质量。在三种环境类型上的实验表明，SAP结合信息增益剪枝相比加拿大旅行者问题基线，将地面机器人行程成本降低31.9%–37.7%，并比基于距离的引导方式额外提升8%–14%，验证了基于信息增益的引导策略在实际部署中既更高效又可计算。
原文摘要 · Abstract (English)
Autonomous robot teams navigating partially known environments face costly backtracking when ground robots encounter blocked roads that are only revealed upon physical traversal. We address this with Scout-Assisted Planning, a heterogeneous planning framework in which scouting Unmanned Aerial Vehicles proactively gather environmental information to improve Unmanned Ground Vehicle navigation. To focus scouting on the most consequential edges, we propose Information Gain-based Action Pruning, which scores candidate scouting actions by their expected impact on ground robot behavior. Since exact Information Gain-based Action Pruning computation is prohibitively expensive, we develop a Graph Neural Network based model that predicts information gain values directly from graph structure and belief state, reducing planning time to real-time levels without sacrificing solution quality. Experiments across three environment types show that SAP with Information Gain Action Pruning reduces ground robot travel cost by 31.9--37.7% over the Canadian Traveler Problem baseline, and outperforms proximity-based scouting guidance by an additional 8--14%, confirming that principled information-gain-guided scouting is both more effective and computationally feasible for real-world deployment
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