PERAL: Perception-Aware Motion Control for Passive LiDAR Excitation in Spherical Robots

Abstract

Autonomous mobile robots increasingly rely on LiDAR–IMU odometry for navigation and mapping, yet horizontally mounted LiDARs (e.g., MID360) capture limited near-ground returns, reducing terrain awareness and degrading performance in feature-scarce environments. Prior solutions, such as static tilt, active rotation, or higher-density sensors, either compromise horizontal perception or introduce extra actuation, cost, weight, and power. We introduce PERAL, a perception-aware motion control framework for spherical robots that provides passive LiDAR excitation without dedicated hardware. By modeling the coupling between the internal differential-drive actuation and sensor attitude, PERAL superimposes bounded, non-periodic oscillations onto nominal goal- or trajectory-tracking commands to increase vertical scan diversity while preserving navigation accuracy. Implemented on a compact spherical robot, PERAL is validated in laboratory, corridor, and tactical environments. Experiments show up to 96% map completeness and a 27% reduction in trajectory tracking error (relative to fixed-horizontal baselines), while improving the observability of near-ground targets in the reconstructed map, at lower weight, power, and cost than static tilt and active rotation. Design and code are available at https://github. com/snakehaihai/PERAL_robot_design.