PERAL: Perception-Aware Motion Control for Passive LiDAR Excitation in Spherical Robots
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912592579002368 |
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| author | Yuan, Shenghai Yee, Jason Wai Hao Guo, Weixiang Liu, Zhongyuan Nguyen, Thien-Minh Xie, Lihua |
| author_facet | Yuan, Shenghai Yee, Jason Wai Hao Guo, Weixiang Liu, Zhongyuan Nguyen, Thien-Minh Xie, Lihua |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_14915 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PERAL: Perception-Aware Motion Control for Passive LiDAR Excitation in Spherical Robots Yuan, Shenghai Yee, Jason Wai Hao Guo, Weixiang Liu, Zhongyuan Nguyen, Thien-Minh Xie, Lihua Robotics 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. |
| title | PERAL: Perception-Aware Motion Control for Passive LiDAR Excitation in Spherical Robots |
| topic | Robotics |
| url | https://arxiv.org/abs/2509.14915 |