Enhanced Robotic Gripper Optimisation: Simulation utilising Machine-Learning (ERGO:SuM)

增强型机器人夹具优化：利用机器学习进行模拟 (ERGO:SuM)

• 批准号: 411517575
• 负责人: Professor Dr. Thorsten Pöschel
• 金额: --
• 依托单位: Lehrstuhl für Multiscale Simulation of Particulate Systems (MSS)
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/411517575?language=en
• 关键词: Enhanced; Robotic; Gripper; Optimisation; Simulation

Robotic granular grippers, which exploit the process of granular jamming to manipulate a vast range of diverse objects, represent a hugely promising nascent technology, with potential applicability spanning almost all industrial sectors. While these soft-robotic grippers carry the potential to prove transformative to the modern manufacturing process, current-generation systems remain unoptimised and unreliable, and their microscopic dynamics and structures almost entirely unresearched.The goal of this project is to develop a new breed of highly-adaptable robotic gripper systems whose function can be rapidly and autonomously optimised for the strong, precise and safe manipulation of a vast range of objects. Our work will take the novel approach of applying numerical particle simulation methods coupled with artificial evolution algorithms both to develop new designs via virtual prototyping, and to calibrate and refine the operation of the prototypes themselves in order to optimally handle significantly differing objects. Further, these computational methods will be used to drive novel machine-learning processes, enabling our systems to `learn' how best to manipulate new `classes' of objects and thus instantaneously optimise their behaviour when encountering a previously unknown object falling within a familiar class.The computational approach pioneered here will allow the aforementioned tasks to be performed more rapidly, more safely, more easily and at a reduced cost as compared to conventional techniques involving physical prototypes and `trial and error' experimentation and testing. Our unique methodology also enables us to investigate a parameter space that would be unfeasible using conventional methods, and to explore more venturesome, "high-risk, high reward" new designs and approaches without fearing the costs of failure. In addition to developing advanced techniques for the design, `training' and optimisation of granular gripper systems, we will also create and test - both in simulation and physically - a new generation of gripper whose strength and adaptability are enhanced via the use of internal piezoelectric transducers capable of producing targeted patterns of localised granular fluidisation and arrest.Finally, we will also conduct a first, in-depth experimental study concerning the interior dynamics and structure of operational granular gripper systems using state-of-the-art three-dimensional imaging techniques including Positron Emission Particle Tracking and X-ray computed tomography. The detailed information obtained will allow us to connect the macroscopic behaviours of granular grippers to their microscopic details, thus enabling us to further our understanding of these important systems on a fundamental level.

机器人颗粒抓取器利用颗粒堵塞的过程来操纵各种不同的物体，代表着一项前景广阔的新兴技术，潜在的适用性遍及几乎所有的工业部门。虽然这些软机器人夹爪可能会改变现代制造流程，但当前一代的系统仍然不优化和不可靠，其微观动力学和结构几乎完全没有研究。该项目的目标是开发一种新的高度适应性的机器人夹爪系统，其功能可以快速和自主地优化，以便对广泛的物体进行强大、精确和安全的操作。我们的工作将采用数值粒子模拟方法与人工进化算法相结合的新方法，通过虚拟原型开发新的设计，并校准和改进原型本身的操作，以便以最佳方式处理显著不同的对象。此外，这些计算方法将被用来驱动新的机器学习过程，使我们的系统能够“学习”如何最好地操纵新的“类”对象，从而在遇到属于熟悉类的以前未知的对象时即时优化它们的行为。与涉及物理原型和“试错”实验和测试的传统技术相比，这里首创的计算方法将允许以更快、更安全、更容易和更低的成本执行上述任务。我们独特的方法还使我们能够研究使用传统方法不可行的参数空间，并探索更冒险、“高风险、高回报”的新设计和方法，而不必担心失败的成本。除了开发设计、培训和优化颗粒抓取系统的先进技术外，我们还将在模拟和物理上创建和测试新一代抓取工具，其强度和适应性通过使用内部压电换能器来增强，这些内部压电换能器能够产生局部颗粒流态化和拦截的目标模式。最后，我们还将使用最先进的三维成像技术，包括正电子发射粒子跟踪和X射线计算机断层扫描，对运行中的颗粒抓取系统的内部动力学和结构进行首次深入的实验研究。获得的详细信息将使我们能够将颗粒抓取的宏观行为与其微观细节联系起来，从而使我们能够在基本水平上进一步了解这些重要的系统。