Impedance Control on Uncertain Objects

不确定物体的阻抗控制

• 批准号: EP/I028773/1
• 负责人: Thrishantha Nanayakkara
• 金额: $ 12.44万
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2011
• 资助国家: 英国
• 起止时间: 2011 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FI028773%2F1
• 关键词: Impedance; Control; Uncertain; Objects

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.

机器人已经能够实现最初的承诺，在费力，危险和重复的任务中取代人类同行，主要是在位置控制领域，包括诸如拾取和放置组件，电弧焊接，研磨已知物体，甚至在相当光滑和已知的地面上双足行走等任务。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，或者在自然柔软的地形（草地，沙子，泥地）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仅用它们的生物力学基础来解释一样，机器人在不确定环境中生存的答案不仅仅来自于试图建造类似于人类身体的机器人。从20世纪80年代初开始，科学家们开始相信，与自然环境稳定互动的秘密将来自机器人本身的顺应能力。Neville Hogan关于阻抗控制的最初工作就是基于这个概念。从那时起，可以找到相当多的文献，关于阻抗控制如何应用于各种力控制应用，如康复，按摩，双足行走，外骨骼机器人，以及其他几种与人类的直接交互。然而，仍然没有答案，阻抗控制应该如何自适应管理，以维持稳定的机器人和环境之间的耦合动态演变亚稳态动态。亚稳定性理论指出，一个不确定的动力学系统虽然在大部分时间内可能保持稳定，但也可能出现间歇性的不稳定。使用螺丝刀的人是一个示例，其中与螺钉的动态接触可以在大部分时间保持稳定，但是由于螺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，一个不确定的动态系统可以提高稳定性，如果它可以预测它可能会失败。近年来，亚稳态系统的研究取得了一些进展，使用平均首次通过时间（MFPT）的概念作为一个指标，以评估当前的控制策略在一个不确定的环境。MFPT是机器人与环境的耦合动力学的不确定动力学的当前知识下一个故障情况的预期时间。因此，本项目旨在为与不确定环境动态接触的机器人开发一个统一的阻抗控制理论。一般的方法，可以开始执行稳定的混合位置/力控制的不确定环境与部分已知的动态和递归地建立一个鲁棒的内部模型，执行稳定的位置/力控制的环境，改变其刚度，粘度和惯性。然后，将开发一种算法来使用上述耦合动态系统的局部线性化模型来估计机器人和环境的MFPT。该MFPT然后将被用于一种新的实时算法，以适应候选阻抗参数集的银行，并自适应地选择最佳参数集，以适应环境，以最大限度地提高MFPT。将给出方法稳定性的严格理论证明和实验验证。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Shadow Robotics Company（一家开发仿生机器人手的英国中小企业）以及意大利理工学院达尔文考德威尔教授领导的机器人小组密切合作，研究人员努力使人形机器人i-Cub能够与自然的不确定环境互动。因此，该项目将受益于合作者已经收集的关于真实的机器人与自然环境交互的丰富经验。

项目成果

A two party haptic guidance controller via a hard rein

通过硬缰绳的两方触觉引导控制器

• 发表时间: 2013
• 作者: Anuradha Ranasingha

Significance of the Compliance of the Joints on the Dynamic Slip Resistance of a Bioinspired Hoof

• DOI: 10.1109/tro.2019.2930864
• 发表时间: 2019-12-01
• 期刊: IEEE TRANSACTIONS ON ROBOTICS
• 影响因子: 7.8
• 作者: Abad, Sara-Adela;Herzig, Nicolas;Nanayakkara, Thrishantha
• 通讯作者: Nanayakkara, Thrishantha

A bio-inspired electro-active Velcro mechanism using Shape Memory Alloy for wearable and stiffness controllable layers

采用形状记忆合金的仿生电活性 Velcro 机构，用于可穿戴和刚度可控层

• DOI: 10.1109/iciafs.2016.7946574
• 发表时间: 2016
• 作者: Afrisal H

An Optimal State Dependent Haptic Guidance Controller Via a Hard Rein

通过硬缰绳的最佳状态相关触觉引导控制器

• 发表时间: 2013
• 作者: Anuradha Ranasingha

A Method to Guide Local Physical Adaptations in a Robot Based on Phase Portraits

基于相图的机器人局部物理适应引导方法

• DOI: 10.1109/access.2019.2923144
• 发表时间: 2019
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Akhond S