Trustable dexterous manipulation: morphologies and low-level control schemes for next-generation robot hand technologies

多指机械手和将由手抓住和操纵的物体是灵巧操作机器人系统的组成部分。然后，灵巧操纵问题可以被定义为确定如何通过手指的协调运动来改变对对象的抓取以达到其位置和方向的期望改变的动作。在结构化环境中，即在预先已知其特征的世界中，解决灵巧操纵问题归结为优化硬件和软件以处理在所寻求的任务中存在的特定对象和约束。由于在这些情况下所有可能的分支都被记录在案，所以设计优化通常得出结论，带有简单的两指下巴或真空夹持器的多自由度机械臂足以定位和定位被操纵的物体；从而避免了与实现机械手灵巧性相关的困难。换句话说，作为所述分析的结果，精细操纵被粗略操纵所吸收，其指的是通过诸如机械臂或其他类型的肢体等机器人小部件对物体的操纵。这当然是在当前的许多工业应用中观察到的典型情况，正如最近的协作工业机器人的设计所证明的那样。上述推理给出了关于在工业环境中缺乏使用多指灵巧机器人手的简单解释，这一方面最近一直是机器人操作界讨论的主题，并清楚地打开了为什么这种对灵巧操作问题的技术和研究是迫切需要的问题。这样一个问题的答案很简单：解决本世纪及以后一些最相关的社会、环境和经济挑战(例如，高效的医疗保健，应对人口老龄化，管理特大城市)，需要机器人与人类合作，操纵为人类设计的物体。因此，鉴于这种环境固有的多样性和不确定性，机器人操纵技术需要粗略操纵和精细操纵的合作，而不是吸收。然而，尽管在过去的35-40年里，机器人学取得了长足的进步，但在形状多样性和形状不确定性的情况下，用机器人手执行可靠的灵活操作仍然是一个悬而未决的问题。该研究的目的是通过研究新的形态和低层控制方案来极大地增强现有解决方案的灵巧操作能力，以帮助解决这一问题并塑造下一代机械手技术。具体地说，本研究致力于设计基于灵活和自适应的机械部件的机械手，这些机械部件产生能够在开环中控制的手-对象系统的非平凡的可预测行为，即无需反馈控制，无需事先知道对象的特殊性，同时对被操作对象的大小或形状仍具有鲁棒性。这一新方法被称为“可信的灵巧操作”，它与传统的以手为中心的策略不同，它采用了一种全面的观点，既考虑了被操纵的身体，又不失一般性；它有可能重新定义当前灵巧机器人手的设计实践。该项目的成功将使研究人员和实践者受益，这些技术涉及机器人在动态和不确定的环境中跨多个领域与人类合作，包括农业、医疗保健、制造业和极端环境。

A multifingered robotic hand and an object that will be grasped and manipulated by the hand are the components of a dexterous manipulation robotic system. Then, the dexterous manipulation problem can be defined as the act of determining how to alter a grasp of an object through the coordinated motion of the fingers to reach a desired change in its position and orientation.In structured environments, that is, in worlds whose characteristics are well known in advance, solving the dexterous manipulation problem reduces to optimising hardware and software for dealing with the specific objects and constraints present in the sought task. Since in these cases all possible ramifications are documented, the design optimisation usually concludes that multi-degree-of-freedom robot arms with simple two-finger jaw or vacuum grippers are enough to position and orient the manipulated objects; thus avoiding the difficulties associated with implementing robot hand dexterity.In other words, as a result of the described analysis, fine manipulation, which refers to the manipulation of objects by small robot parts such as robotic fingers, hands, and wrists, is absorbed by gross manipulation, which refers to the manipulation of objects by large robot parts such as robotic arms or other types of limbs. This is certainly the typical situation observed in many of the current industrial applications, as demonstrated by the design of the most recent collaborative industrial robots.The above reasoning gives a simple explanation about the lack of use of multifingered dexterous robotic hands in industrial settings, an aspect that have been the subject of discussion in the robotic manipulation community recently, and clearly opens the question about why this technology and research on the dexterous manipulation problem is a pressing need.The answer of such a query is simple: the solution of some of the most relevant social, environmental, and economic challenges of this century, and beyond, (e.g., an efficient healthcare, coping with an ageing population, management of mega cities), requires robots that cooperate with humans to manipulate objects designed for human hands. Thus, given the diversity and uncertainty inherent of such settings, robot manipulation technologies require the cooperation, not the absorption, of gross manipulation and fine manipulation. Solving the problem of manipulating objects dexterously in unstructured environments is then a must.However, despite the substantial progress made in the last 35-40 years in robotics, performing reliable dexterous manipulation operations under both shape diversity and shape uncertainty with a robot hand is still an open question. The aim of this research is to help solving this problem and shaping the next generation of robot hand technologies by investigating novel morphologies and low-level control schemes that drastically enhance the dexterous manipulation capabilities of current solutions.Specifically, this research focuses on devising robot hands based on flexible and adaptive mechanical components that generate non-trivial predictable behaviours of the hand-object system that are able to be controlled in open loop, that is, without feedback control and without knowing the particularities of the object beforehand, while still being robust to the size or shape of the object being manipulated. This novel approach, called 'trustable dexterous manipulation', departs from traditional hand-centred strategies to embrace a holistic view that takes into account the manipulated bodies without losing generality; it has the potential to redefine the current practice in design of dexterous robot hands.The success of this project will benefit researchers and practitioners working on technologies that involve robots collaborating with humans in dynamic and uncertain settings across multiple domains, including agriculture, healthcare, manufacturing, and extreme environments.