用牧羊犬驱赶羊群的思路，让多无人机高效探索大场景。
Frontier Shepherding: A Bio-inspired Multi-robot Framework for Large-Scale Exploration
- 模仿牧羊犬驱赶羊群，用机器人主动引导探索边界。
- 三架无人机平均比现有方法多探索20%的区域。
- 适合复杂环境下的多机协同探索，如搜救与监测。
大规模环境的高效探索仍是机器人领域的关键挑战，应用涵盖环境监测到搜救任务。本文提出一种受生物启发的多机器人探索框架——前沿牧羊（Frontier Shepherding, FroShe），其核心思想是将探索前沿类比为羊群，机器人则扮演牧羊犬角色进行引导。该方法在不同规模和障碍密度的环境中均表现稳健，部署时几乎无需调参。仿真结果显示，使用三架无人机时，该方法在各类复杂环境下探索性能稳定，平均优于当前最优策略20%。进一步在森林类真实环境中验证了单机与双机部署的有效性。
原文摘要 · Abstract (English)
Efficient exploration of large-scale environments remains a critical challenge in robotics, with applications ranging from environmental monitoring to search and rescue operations. This article proposes Frontier Shepherding (FroShe), a bio-inspired multi-robot framework for large-scale exploration. The framework heuristically models frontier exploration based on the shepherding behavior of herding dogs, where frontiers are treated as a swarm of sheep reacting to robots modeled as shepherding dogs. FroShe is robust across varying environment sizes and obstacle densities, requiring minimal parameter tuning for deployment across multiple agents. Simulation results demonstrate that the proposed method performs consistently, regardless of environment complexity, and outperforms state-of-the-art exploration strategies by an average of 20% with three UAVs. The approach was further validated in real-world experiments using single- and dual-drone deployments in a forest-like environment.
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