无需视觉，机器人靠身体感知判断地面平坦或崎岖
Nonvisual Classification of Ground-Condition by Artificial Proprioception in an Amoeba-Inspired Autonomous Walking Robot
- 用加速度计、压力传感器与储层计算模拟体感
- 动态行走中仍准确区分平坦与粗糙地面
- 适合无视觉环境下的自主移动机器人研究
针对一种阿米巴启发的自主行走机器人，研究了一种基于多模态感知的非视觉地面条件分类方法。为在不使用图像感知与处理的情况下实现地面条件分类，我们通过集成三轴加速度计、八个足部压力传感器和储层计算（RC）实现了人工本体感觉。即使在四足机器人行走过程中因动态运动导致传感器输出大幅波动，该系统仍能以高精度分类地面为平坦或粗糙。我们在机器人上实现了根据地面条件实时切换步态的功能。同时讨论了各传感器对地面条件分类的贡献。
原文摘要 · Abstract (English)
Nonvisual classification of ground condition based on a multimodal sensing approach was investigated for an amoeba-inspired autonomous walking robot. To classify ground condition without image sensing and processing, we implemented artificial proprioception by integrating a three-axis accelerometer, eight foot pressure sensors, and reservoir computing (RC). Even when large fluctuations in the sensor outputs are caused by dynamic motions of a four-legged robot in walking, our system can classify the ground condition, flat or rough, with high accuracy. We demonstrate on-site switching of walking gait depending on ground condition in the robot. We also discuss the contribution of each sensor to ground condition classification.
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