让球形机器人被动激发激光雷达，提升近地感知能力。
PERAL: Perception-Aware Motion Control for Passive LiDAR Excitation in Spherical Robots
- 通过控制驱动电机耦合传感器姿态，生成非周期性震荡扫描
- 地图完整度达96%，轨迹误差降低27%，可检测近地人体
- 无需额外硬件，适合低成本智能移动平台
自主移动机器人越来越多依赖激光雷达-惯性里程计进行导航与建图，但如MID360这类水平安装的激光雷达难以获取近地回波，限制地形感知，在特征稀疏环境下性能下降。现有方案——静态倾斜、主动旋转或高密度传感器——或牺牲水平感知，或增加执行机构、成本与功耗。本文提出PERAL，一种面向球形机器人的感知意识运动控制框架，实现无需专用硬件的被动激光雷达激励。通过建模内部差速驱动与传感器姿态间的耦合关系，PERAL在常规目标跟踪或轨迹控制指令上叠加有界、非周期性振荡，丰富垂直扫描多样性，同时保持导航精度。在紧凑型球形机器人上实现并验证于实验室、走廊及战术环境。实验表明，地图完整度最高达96%，轨迹跟踪误差减少27%，近地人体检测稳健，且相比静态倾斜、主动旋转和固定水平基线方案，具有更低重量、功耗与成本。设计与代码将在录用后开源。
原文摘要 · Abstract (English)
Autonomous mobile robots increasingly rely on LiDAR-IMU odometry for navigation and mapping, yet horizontally mounted LiDARs such as the MID360 capture few near-ground returns, limiting terrain awareness and degrading performance in feature-scarce environments. Prior solutions - static tilt, active rotation, or high-density sensors - either sacrifice horizontal perception or incur added actuators, cost, and power. We introduce PERAL, a perception-aware motion control framework for spherical robots that achieves passive LiDAR excitation without dedicated hardware. By modeling the coupling between internal differential-drive actuation and sensor attitude, PERAL superimposes bounded, non-periodic oscillations onto nominal goal- or trajectory-tracking commands, enriching vertical scan diversity while preserving navigation accuracy. Implemented on a compact spherical robot, PERAL is validated across laboratory, corridor, and tactical environments. Experiments demonstrate up to 96 percent map completeness, a 27 percent reduction in trajectory tracking error, and robust near-ground human detection, all at lower weight, power, and cost compared with static tilt, active rotation, and fixed horizontal baselines. The design and code will be open-sourced upon acceptance.
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