Tactile Feet and Online Learning for Robust Control of a Quadruped Robot in Challenging Terrain

触觉足和在线学习在具有挑战性的地形中实现四足机器人的鲁棒控制

• 批准号: 1951503
• 金额: --
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1951503
• 关键词: Tactile; Feet; Online; Learning; Robust

Tactile sensing has been extensively explored in the domain of object manipulation and detection, especially with the TacTip tactile sensor, but very little has been done in the domain of tactile feet for walking robots. With feet there is a similar issue as in hands: by interacting with the environment the robot occludes the surface that needs quantifying (whether this be in texture, surface orientation, edge detection etc). Therefore the application of a tactile sensor, which directly measures the contacted surface, seems more appropriate than commonly used non-contact methods such as computer vision (CV), lidar or sonar which cannot sense the surface under contact. The addition of tactile feet may give a walking robot more reliable estimation of slip between the feet and floor, floor distance and orientation and identification of smooth/bumpy surfaces (for selection of stable footholds), all of which improve the success of a quadruped walking and/or running over uneven natural terrain. Most walking robots struggle to do this. Tactile feet pose additional challenges to those faced in tactile hands. The hardware must be capable of supporting the body weight and withstand the impact from walking/running and be resistant to unexpected objects encountered in natural terrain (e.g. sharp objects). The software must handle the greater distortion and noise of the sensor caused by the movements of the supported robot, which is inherently more complex and dynamic than the noise experienced in hands, and the feedback loop must be faster as walking or running require faster movements than with object grasping and manipulation. Additionally given the size and mobile nature of a walking robot, any software must either run on an onboard microcomputer, which restricts computational power, or there must be a high speed connection to an offboard computer which is fast enough to enable real time control of the robot. Additionally, as explored in the first year project, the use of online learning methods to learn the mapping between tactile data and useful control inputs could be a data efficient and robust way of training the robot. Offline methods tend to be extremely data intensive, needing data across all possible dimensions of variation, and cannot learn to adapt to novel inputs encountered during testing. Additionally the models are learnt for specific sensors which may break and need replacing at any time, and while replacement sensors will be similar there is enough difference that the same models cannot be used and all training must be repeated. The use of online learning solves these problems, introducing instead the challenge of creating comprehensive online data collection policies in order for the correct mappings to be learnt autonomously. A neat extension from the first year project, which explored data-efficient online sensor training and control in edge following tasks, is the development of a beam-balancing robot. Fitting a quadruped with tactile feet would enable it to walk along a randomly curving beam only slightly wider than its leg span, without falling. With tactile sensing it is possible to identify an edge and how far along the sensor the edge is (as explored with a single sensor in the first year project). This is in contrast to a robot without tactile sensing which would be unable to tell if the foot is fully on the beam or not - the use of onboard cameras watching the feet would be obscured from seeing directly under the feet by the feet or the terrain (e.g. grass) so may be unreliable. Using tactile sensors in this way would allow the robot to move the feet back onto the beam and prevent falling (in a more complex system using maps the locations of the edge could be noted to aid future planning of stable courses).

在物体操纵和检测的领域，尤其是在Tactip触觉传感器的领域中，对触觉感应进行了广泛的探索，但是在步行机器人的触觉脚的范围内完成了很少的操作。脚有一个类似的问题：通过与环境相互作用，机器人会阻塞需要量化的表面（无论是纹理，表面取向，边缘检测等）。因此，直接测量接触表面的触觉传感器的应用似乎比常用的非接触方法（例如计算机视觉（CV），激光雷达或声纳或声纳，这些传感器都无法感觉到接触中的表面。触觉脚的增加可能会使步行机器人对脚和地板之间的滑动，地板距离，方向以及识别光滑/颠簸的表面（用于选择稳定的立足点）的更可靠估计，所有这些都改善了四足步行的成功和/或在不均匀的自然地形上奔跑的成功。大多数步行机器人都难以这样做。触觉脚面临着触觉的人面临的其他挑战。该硬件必须能够支撑体重并承受步行/跑步的影响，并抵抗自然地形（例如锋利的物体）中遇到的意外物体。该软件必须处理由受支持的机器人的运动引起的传感器的更大失真和噪声，该机器人的运动本质上比手中经历的噪声更复杂和动态，并且反馈回路必须比对象抓握和操纵更快，因为步行或跑步需要更快的运动。此外，鉴于步行机器人的大小和移动性质，任何软件都必须在限制计算功率的机载微型计算机上运行，​​或者必须与卸货计算机建立高速连接，该计算机足够快以实现机器人的实时控制。此外，正如第一年项目中所探讨的那样，使用在线学习方法来学习触觉数据和有用的控制输入之间的映射可能是训练机器人的一种有效且强大的方法。离线方法往往是非常数据密集型的，需要在所有可能的变化方面进行数据，并且无法学会适应测试过程中遇到的新颖输入。此外，对于特定传感器而言，这些模型是在任何时候都可能破坏和需要替换的特定传感器的，尽管替换传感器将相似，但是有足够的差异，无法使用相同的模型，并且必须重复所有训练。在线学习的使用解决了这些问题，而是引入制定全面的在线数据收集政策的挑战，以便自主地学习正确的映射。第一年项目的整洁扩展是在任务后探索了数据效率的在线传感器培训和控制，这是横梁平衡机器人的开发。将四倍的触觉脚贴合可以使其沿着随机弯曲的光束行走，而不会稍宽，而不会掉落。通过触觉传感，可以识别边缘以及边缘传感器的距离（正如第一年项目中的单个传感器所探索的那样）。这与没有触觉感应的机器人形成鲜明对比的是，该机器人无法确定脚是否完全在光束上 - 使用板上摄像机看着脚会被脚或地形（例如草）直接看到脚下的脚下看见，因此可能是不可靠的。以这种方式使用触觉传感器将使机器人可以将脚移回光束上并防止掉落（在更复杂的系统中使用地图，可以注意到边缘的位置可以帮助稳定课程的未来计划）。