High resolution tactile sensing has great potential in autonomous mobile robotics, particularly for legged robots. One particular area where it has significant promise is the traversal of challenging, varied terrain. Depending on whether an environment is slippery, soft, hard or dry, a robot must adapt its method of locomotion accordingly. Currently many multi-legged robots, such as Boston Dynamic's Spot robot, have preset gaits for different surface types, but struggle over terrains where the surface type changes frequently. Being able to automatically detect changes within an environment would allow a robot to autonomously adjust its method of locomotion to better suit conditions, without requiring a human user to manually set the change in surface type. In this paper we report on the first detailed investigation of the properties of a particular bio-inspired tactile sensor, the TacTip, to test its suitability for this kind of automatic detection of surface conditions. We explored different processing techniques and a regression model, using a custom made rig for data collection to determine how a robot could sense directional and general force on the sensor in a variety of conditions. This allowed us to successfully demonstrate how the sensor can be used to distinguish between soft, hard, dry and (wet) slippery surfaces. We further explored a neural model to classify specific surface textures. Pin movement (the movement of optical markers within the sensor) was key to sensing this information, and all models relied on some form of temporal information. Our final trained models could successfully determine the direction the sensor is heading in, the amount of force acting on it, and determine differences in the surface texture such as Lego vs smooth hard surface, or concrete vs smooth hard surface.

翻译：高分辨率触觉传感在自主移动机器人领域具有巨大潜力，尤其适用于足式机器人。其在复杂多变地形穿越方面展现出显著应用前景。根据环境是否湿滑、柔软、坚硬或干燥，机器人需相应调整其运动方式。目前，诸如波士顿动力公司Spot机器人等多足机器人虽为不同地表类型预设了步态，但在地表类型频繁变化的区域仍存在适应困难。若能实现环境变化的自主检测，机器人即可自动调节运动方式以更好适应环境，无需人工手动设置地表类型变更。本文首次系统研究了特定仿生触觉传感器TacTip的特性，评估其在自动检测环境状态方面的适用性。我们采用定制数据采集装置，探索了不同处理技术与回归模型，以确定机器人如何在多种工况下感知传感器上的方向作用力与总作用力。成功验证了该传感器可区分柔软、坚硬、干燥及（湿）滑表面。进一步采用神经模型实现特定表面纹理分类。传感器内部光学标记的位移是感知此类信息的关键，所有模型均依赖某种形式的时间序列信息。最终训练完成的模型可成功判定传感器运动方向、受力大小，并识别不同表面纹理差异（如乐高积木与光滑硬质表面，或混凝土与光滑硬质表面的区分）。