通过在最小感知平台上的涌现自适应莱维飞行实现分散式可扩展探索
Decentralized Scalable Exploration via Emergent Adaptive Lévy Walks on Minimal-Sensing Platforms
- National University of Singapore（新加坡国立大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对纳米无人机自主探索难题，提出轻量级传感器驱动的莱维飞行控制器，结合离散步长采样与传感器反应策略，各机器人独立采样指数并选航向，实现可扩展多无人机探索，仿真显示覆盖率提升且碰撞减少。
AI中文摘要：
由于在传感、计算和飞行续航方面的严重限制，使用手掌大小的纳米无人机进行高效自主探索仍然具有挑战性。我们为重量低于50克且配备稀疏局部传感的空中机器人提出了一种轻量级传感器驱动的莱维飞行（SDLW）控制器。该方法将离散莱维步长采样与使用方向范围测量的传感器反应航向策略相结合。每个机器人独立地从均匀先验中采样其莱维指数，以在无需机器人间通信进行探索控制的情况下实现探索多样化。然后每个机器人使用冯·米塞斯分布选择航向，该分布使运动偏向开放方向，同时保持超扩散探索特性。控制器以恒定计算成本运行，实现了可扩展的多无人机探索。仿真结果表明，在开放区域覆盖率提高了79.6%，在房间和走廊布局中提高了43.1%，在杂乱环境中提高了13.6%相对于均匀航向莱维飞行基线，碰撞分别减少了13.0%、7.1%和1.4%。这项工作为在最小感知、资源受限的纳米无人机上进行可扩展的多机器人探索提供了一个实用框架。
英文摘要：
Efficient autonomous exploration with palm-sized nano-UAVs remains challenging due to severe limitations in sensing, computation, and flight endurance. We present a lightweight sensor-driven Lévy walk (SDLW) controller for aerial robots weighing under 50 grams and equipped with sparse local sensing. The method combines discrete Lévy step-length sampling with a sensor-reactive heading policy using directional range measurements. Each robot independently samples its Lévy exponent from a uniform prior to diversify exploration without inter-robot communication for exploration control. Each robot then selects headings using a von Mises distribution that biases motion toward open directions while preserving superdiffusive exploration properties. The controller operates at constant computational cost, enabling scalable multi-UAV exploration. Simulation results show coverage improvements of 79.6% in open arenas, 43.1% in rooms-and-corridors layouts, and 13.6% in cluttered environments, with collision reductions of 13.0%, 7.1%, and 1.4%, respectively, relative to a uniform-heading Lévy walk baseline. This work provides a practical framework for scalable multi-robot exploration on minimal-sensing, resource-constrained nano-UAVs.
