Title: PERAL: Perception-Aware Motion Control for Passive LiDAR Excitation in Spherical Robots Show Chinese title

Title: PERAL：球形机器人中被动LiDAR激励的感知感知运动控制

Abstract: 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. Show Chinese abstract

Abstract: 自主移动机器人越来越多地依赖LiDAR-IMU里程计进行导航和地图构建，但水平安装的LiDAR如MID360捕获的近地面返回信号很少，这限制了地形感知，并在特征稀缺的环境中降低了性能。 先前的解决方案——静态倾斜、主动旋转或高密度传感器——要么牺牲水平感知，要么增加执行器、成本和功耗。 我们引入了PERAL，这是一个面向球形机器人的感知意识运动控制框架，能够在不使用专用硬件的情况下实现被动LiDAR激励。 通过建模内部差速驱动执行与传感器姿态之间的耦合，PERAL将有限的、非周期性振荡叠加在常规的目标或轨迹跟踪命令上，在保持导航精度的同时丰富垂直扫描多样性。 在紧凑的球形机器人上实现，PERAL在实验室、走廊和战术环境中进行了验证。 实验表明，地图完整性最高可达96%，轨迹跟踪误差减少27%，并且在近地面人类检测方面表现出色，所有这些均在比静态倾斜、主动旋转和固定水平基线更低的重量、功耗和成本下实现。 设计和代码将在接受后开源。