轻量级无人机用自适应莱维飞行实现高效无通信探索
Decentralized Scalable Exploration via Emergent Adaptive Lévy Walks on Minimal-Sensing Platforms
- 基于方向感知的莱维步长采样与分布导向航向选择
- 开放区域覆盖提升79.6%，杂乱环境碰撞减少1.4%
- 无需通信，适合资源受限的微型无人机集群
小型化纳米无人机在自主探索中面临传感、计算和续航的严重限制。本文提出一种轻量级传感器驱动的莱维飞行（SDLW）控制器，适用于重量低于50克、仅具备稀疏局部感知能力的飞行机器人。该方法结合离散莱维步长采样与基于方向距离测量的反应式航向策略，每个机器人从均匀先验中独立采样莱维指数以实现探索多样性，无需机器人间通信。航向选择采用冯·米塞斯分布，引导运动朝向开阔方向，同时保持超扩散探索特性。控制器计算开销恒定，支持可扩展的多无人机探索。仿真结果表明，在开放区域、房间走廊布局及杂乱环境中，覆盖率分别提升79.6%、43.1%和13.6%，碰撞率相应降低13.0%、7.1%和1.4%，优于统一航向的莱维飞行基线。本工作为资源受限的微型无人机提供了实用的可扩展多机器人探索框架。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。