Impedance Control on Uncertain Objects

机器人已经能够履行最初的承诺，在繁重、危险和重复性的任务中取代人类同行，主要是在位置控制领域，包括拾取和放置部件、弧焊、研磨已知物体，甚至在相当平稳和已知的场地上两足行走。然而，机器人仍然很难在不确定的物体上执行稳定的力控制任务，或者在自然的软地形(草、沙、泥)上行走。就像我们使用左手和右手的方式之间的差异不能仅用它们的生物力学基础来解释一样，机器人在不确定的环境中生存的答案并不是来自于试图建造单独类似人类身体的机器人。从20世纪80年代初开始，科学家们就开始相信，与自然顺应环境稳定互动的秘密将来自机器人本身的顺应能力。内维尔·霍根关于阻抗控制的最初工作就是基于这一概念。从那时起，关于阻抗控制如何应用于各种力控制应用，如康复、按摩、两足行走、外骨骼机器人以及其他几种与人类的直接互动，可以找到相当多的文献。然而，当机器人和环境之间的耦合动力学演化为亚稳态动力学时，如何自适应地管理阻抗控制以保持稳定性，仍然没有答案。亚稳态理论认为，一个不确定的动力学系统可以表现出间歇性的不稳定，尽管它可能在大部分时间保持稳定。使用螺丝刀的人是一个例子，其中与螺丝钉的动态接触可能在大部分时间保持稳定，但由于螺丝钉与周围介质之间的摩擦的不确定性而表现出间歇性滑动。即使是人类步行者也会因为同样的现象而在极少数情况下摔倒。然而，如果一个不确定的动态系统能够预测它可能出现故障的地方，它就可以增强稳定性。亚稳态系统的一些最新进展使用平均首次通过时间(MFPT)的概念作为在不确定环境中评估当前控制策略的指标。在已知机器人与环境耦合动力学的不确定性的情况下，MFPT是到达下一个故障状态的预期时间，因此，本项目旨在建立机器人与不确定环境动态接触的统一阻抗控制理论。一般方法可以在具有部分已知动力学的不确定环境中开始执行稳定的位置/力混合控制，并递归地建立稳健的内模，以在改变其刚度、粘性和惯性的环境上执行稳定的位置/力控制。然后，将开发一种算法，使用上述耦合动力系统的局部线性化模型来估计机器人和环境的MFPT。然后，该MFPT将被用于一种新的实时算法，以适应一组候选阻抗参数集，并自适应地选择适合环境的最佳参数集，以便最大化MFPT。文中将给出严格的稳定性理论证明和方法的实验验证。该项目将使用定制的实验平台来评估和完善将在该项目中开发的基本理论和算法。PI将与影子机器人公司(Shadow Robotics Company)和意大利理工学院达尔文·考德威尔(Darwin Caldwell)教授领导的机器人小组密切合作，I-Cub是一家总部位于英国的中小企业，开发仿生机器人手。在意大利理工学院，研究人员努力使人形机器人i-Cub能够与自然不确定的环境互动。因此，这个项目将受益于合作者已经在真实机器人与自然环境互动方面收集的丰富经验。

Robots have been able to serve the original promise to replace human counterparts in laborious, hazardous, and repetitive tasks mainly in the area of position control that includes tasks such as pick and place of components, arc welding, grinding known objects, and even in bipedal walking on fairly smooth and known grounds. However, robots still find it hard to carry out stable force control tasks on uncertain objects or walk on natural soft terrains (grass, sand, mud). Just like the difference between the way we use the left hand and the right hand can not be explained using their biomechanical basis alone, the answer to robotic survival in uncertain environments does not come from an attempt to build robots that resemble human bodies alone. From early 1980s, scientists have begun to believe that the secrets of stable interactions with natural compliant environments will come from an ability of the robot itself to be compliant. The original work of Neville Hogan on impedance control was based on this concept. Since then, a considerable body of literature can be found on how impedance control is applied in various force control applications such as rehabilitation, massaging, bipedal walking, exoskeletal robotics, and several other direct interactions with humans. However, still there is no answer to how impedance control should be adaptively managed to sustain stability when the coupled dynamics between the robot and the environment evolves metastable dynamics. The theory of Metastability states that an uncertain dynamics system can exhibit intermittent instability though it may stay stable most of the time. A human using a screw driver is one example, where the dynamic contact with the screw may stay stable most of the time, but exhibit intermittent slipping due to uncertainty in the friction between the screw and the surrounding medium. Even a human walker can fall down in rare situations due to the same phenomenon. However, an uncertain dynamic system can enhance stability if it can predict where it is likely to fail. A number of recent advances in metastable systems use the concept of mean first passage time (MFPT) as an indicator to assess the current control policy in an uncertain environment. MFPT is the expected time to the next failure situation given the current knowledge of the uncertain dynamics of the coupled dynamics of the robot and the environment.Therefore, this project aims at developing a unifying theory of impedance control for robots that are in dynamic contact with uncertain environments. The generic method that can start to perform stable hybrid position/force control on an uncertain environment with partially known dynamics and recursively build a robust internal model to perform stable position/force control on an environment that changed its stiffness, viscosity, and inertia. Then an algorithm will be developed to use a locally linearised model of the above coupled dynamic system to estimate the MFPT of the robot and the environment. This MFPT will then be used in a novel real-time algorithm to adapt a bank of candidate impedance parameter sets and adaptively choose the best parameter set to suit the environment in order to maximise the MFPT. Rigorous theoretical proofs of stability and experimental validation of methods will be given. The project will use a custom built experimental platform to evaluate and refine the fundamental theories and algorithms that will be developed in this project. The PI will closely collaborate with Shadow Robotics Company, a UK based SME who develops biomimetic robotic hands, and the robotics group led by Professor Darwin Caldwell at the Italian Institute of Technology, where the researchers strive to enable the humanoid robot i-Cub to interact with natural uncertain environments. Therefore, this project will benefit from a wealth of experiences the collaborators have already gathered on real robots interacting with natural environments.