四足机器人分布式低成本飞行时间传感器的避障与步态规划
Quadruped Obstacle Avoidance and Footstep Planning with Distributed Low-cost Time-of-Flight Sensors
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中文总结 AI 辅助
本研究验证了分布式ToF传感器可作为低成本替代方案，为四足机器人提供厘米级近场地形测绘，支持可靠的避障与步态规划，且成本、能耗和复杂度远低于深度相机。
中文摘要 AI 辅助
四足机器人通常依赖深度相机和激光雷达传感器来绘制其局部环境。然而，这些传感器近距离覆盖范围有限，价格相对昂贵，且功耗较大。本研究探讨了分布式飞行时间（ToF）传感器能否作为深度相机的低成本替代方案，用于近场地形测绘，以支持运动控制和局部导航。我们为ANYbotics ANYmal四足机器人设计了一种分布式ToF传感架构，评估了其环境重建精度，并将其与深度相机在地形测绘和避障方面的性能进行了基准比较。将传感器分布在机器人周围还可以避免传统传感器的盲区。结果表明，尽管分布式ToF传感器分辨率低且测量噪声较高，但它们能够支持可靠的感知运动控制，并具有厘米级的局部测绘精度。所提出的传感策略为近场避障和步态规划提供了足够的几何信息，且成本、能耗和系统复杂度远低于深度相机。
英文摘要
Quadruped robots typically rely on depth cameras and LiDAR sensors to map their local environment. However, these sensors have limited close-range coverage, are relatively expensive, and consume significant power. This study investigates whether distributed Time-of-Flight (ToF) sensors can serve as a low-cost alternative to depth cameras for near-field terrain mapping for locomotion and local navigation. We designed a distributed ToF sensing architecture for the ANYbotics ANYmal quadruped, assessed its environment reconstruction accuracy, and benchmarked it against depth cameras for terrain mapping and obstacle avoidance. Distributing these sensors around the robot can also avoid the blind spots of traditional sensors. Our results show that, despite their low resolution and higher measurement noise, distributed ToF sensors can support reliable perceptual locomotion with centimeter-level local mapping accuracy. The proposed sensing strategy provides sufficient geometric information for near-field obstacle avoidance and footstep planning, at substantially lower cost, energy consumption, and system complexity than depth cameras.
发表机构
- Oxford Robotics Institute, University of Oxford（牛津大学牛津机器人研究所）
- Free University of Bozen-Bolzano（博尔扎诺自由大学）
- University of Edinburgh（爱丁堡大学）
- University of Udine（乌迪内大学）
机构由 AI 辅助整理，请以论文原文为准。
