AI 中文总结
该研究为变形虫启发的自主步行机器人，整合传感器与储备池计算实现人工本体感觉，完成地面状况非视觉分类，可依地面切换步态并分析传感器贡献。
AI 中文摘要
针对变形虫启发的自主步行机器人，研究了基于多模态感知方法的地面状况非视觉分类。为在无需图像感知与处理的情况下分类地面状况，我们整合三轴加速度计、8个足底压力传感器与储备池计算（RC）实现人工本体感觉。即便四足机器人步行时的动态运动导致传感器输出出现大幅波动，本系统仍能以高准确率将地面状况分类为平坦或粗糙。我们在机器人上展示了依据地面状况进行步行步态的现场切换，还探讨了各传感器对地面状况分类的贡献。
英文摘要
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.
Comments5 pages, 7 figures, The paper has been submitted to IEEE SCIS ISIS 2026 for consideration
