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).

触觉传感在物体操纵和检测领域得到了广泛的研究，特别是TacTip触觉传感器，但在步行机器人的触觉脚领域却做得很少。对于脚，存在与手类似的问题：通过与环境交互，机器人遮挡需要量化的表面（无论是纹理，表面方向，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通常使用的非接触方法（例如计算机视觉（CV）、激光雷达或声纳）更合适，这些方法不能感测接触下的表面。触觉脚的添加可以给予行走机器人对脚和地板之间的滑动、地板距离和取向的更可靠的估计以及对光滑/颠簸表面的识别（用于选择稳定的立足点），所有这些都提高了四足动物在不平坦的自然地形上行走和/或跑步的成功率。大多数步行机器人都很难做到这一点。有触觉的脚对那些有触觉的手提出了额外的挑战。硬件必须能够支撑体重，承受行走/跑步的冲击，并能抵抗自然地形中遇到的意外物体（例如尖锐物体）。软件必须处理由所支持的机器人的运动引起的传感器的更大的失真和噪声，其本质上比手中经历的噪声更复杂和动态，并且反馈回路必须更快，因为步行或跑步需要比物体抓取和操纵更快的运动。另外，给定步行机器人的尺寸和移动的性质，任何软件必须在限制计算能力的机载微型计算机上运行，或者必须存在到足够快以实现机器人的真实的时间控制的非机载计算机的高速连接。此外，正如第一年项目中所探索的那样，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数据高效和鲁棒的方法。离线方法往往是数据密集型的，需要所有可能的变化维度的数据，并且无法学习适应测试期间遇到的新输入。此外，模型是针对可能损坏并需要随时更换的特定传感器学习的，虽然更换的传感器将是相似的，但存在足够的差异，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，而是引入了创建全面的在线数据收集策略的挑战，以便自主学习正确的映射。从第一年的项目，探索数据高效的在线传感器训练和控制边缘跟踪任务，一个整洁的扩展是梁平衡机器人的开发。给四足动物装上有触觉的脚，就能让它沿着一根比它腿的跨度稍宽的随机弯曲的横梁行走，而不会摔倒。通过触觉传感，可以识别边缘以及边缘沿传感器的沿着（如第一年项目中使用单个传感器所探索的）。这与不具有触觉感测的机器人形成对比，触觉感测将无法判断脚是否完全在梁上-使用机载摄像机观察脚将被脚或地形（例如草地）遮挡而无法直接看到脚下方，因此可能不可靠。以这种方式使用触觉传感器将允许机器人将脚移回梁上并防止跌倒（在使用地图的更复杂的系统中，可以注意到边缘的位置，以帮助未来规划稳定的课程）。