Improved robotic locomotion performance through morphological computation and active control

通过形态计算和主动控制提高机器人运动性能

• 批准号: 2593232
• 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: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2593232
• 关键词: Improved; robotic; locomotion; performance; through

Robots with legged locomotion have, over the years, demonstrated high flexibility, excellent dynamic stability and adaptability to different terrains and obstacles [1]. They have demonstrated these characteristics in different environments, especially in areas such as rescue, reconnaissance, health-care, security and marine environments [2]. Robots have beenshown to be advantageous in applications involving dull, dirty and dangerous environments, and vital tasks such as nuclear decommissioning remain a key national priority. It is widely believed that over 50% of the earth's surface is inaccessible to wheels or tracks [2], and legged robots have an increasing role to play, judging by recent commercial successes such as ANYbotics [3] and cost-effective, open source systems such as Stanford Doggo [4] and the ODRI [5].Despite the development of legged robots demonstrating safety, robustness and high performance, it is evident that the commercially available robots are designed with centralised control, with every joint actuated. Here, it is pertinent to introduce Morphological Computation (MC), which, in the context of embodied (artificial) intelligence, refers to processes, which are conducted by the body (and environment) that otherwise would have to be performed by the brain [6]. MC is relevant in the study of biological and robotic systems as illustrated in the Passive Dynamic Walker [7], which is a purely mechanical system. The Passive Dynamic Walker shows that walking can result from the interaction of the system's physical properties and its environment without actuation. In the context of robotics, this means that systems with high morphological computation only need to generate motor commands when they are needed. Not only does such a control scheme increase the durability of the systems (because the wear-out of the actuators is decreased), it also means that robots with high MC will have a reduced energy demand for their actuation [8]. This is useful for legged robots, since they need to be untethered for autonomous functioning and transporting payloads over challenging terrains. While there is good acceptance of the role of MC in biological systems, Ghazi-Zahediet al [8], in their exploration on recenttrends, state that its application in robotics remains underexplored. One of the main reasons emphasised by the authors is that the conventional control paradigms treat the body as something that needs to be dominated rather than being used as a computing resource. There is a tendency to suppress any undesirable morphological behaviours like nonlinearity, underactuation or noise through the use of control systems and servo motors. It is quite remarkable that these same complex morphological properties play a key role in the behaviour of natural systems, as described in the research by Abad et al in the MC of a goat hoof in slip reduction [9] and the Puppy [7], an under-actuated robot. At the same time, one of the challenges set out by Deimal et al [10] is to ensure that the systems take advantage of the morphology ('good MC') while avoiding harmful body-environment interactions with respect to the desired functionality ('bad MC'). To achieve the right balance of MC and active control, a formal method is required to measure MC in the system. In their paper detailed with algorithms, Ghazi-Zahedi et al [6] have demonstrated two methods of measuring MC, one of which is to compare behaviour complexity with controller complexity, the former by information of world states and the latter by information of sensor states. The conclusion from review of current landscape of academia and industry is that there is a huge potential for robotics systems that use the inter-play of computational power, centralised control, and morphological features to be more energy efficient and versatile across different applications

多年来，腿式运动机器人表现出了高度的灵活性、优异的动态稳定性以及对不同地形和障碍物的适应性[1]。它们在不同的环境中展示了这些特性，特别是在救援、侦察、医疗保健、安全和海洋环境等领域[2]。事实证明，机器人在涉及枯燥、肮脏和危险环境的应用中具有优势，而核退役等重要任务仍然是国家的重点优先事项。人们普遍认为，超过 50% 的地球表面是轮子或履带无法到达的 [2]，从 ANYbotics [3] 等最近的商业成功和具有成本效益的开源来看，腿式机器人发挥着越来越大的作用。系统，如斯坦福 Doggo [4] 和 ODRI [5]。尽管腿式机器人的发展证明了安全性、稳健性和高性能，但很明显，商用机器人是采用集中控制设计的，每个关节已启动。在这里，有必要介绍一下形态计算（MC），在体现（人工智能）智能的背景下，它指的是由身体（和环境）执行的过程，否则必须由大脑执行。 6]。 MC 与生物和机器人系统的研究相关，如被动动态步行器 [7] 所示，它是一个纯机械系统。被动动态步行器表明，步行可以是系统物理特性与其环境相互作用的结果，无需驱动。在机器人技术的背景下，这意味着具有高形态计算能力的系统只需要在需要时生成电机命令。这种控制方案不仅提高了系统的耐用性（因为执行器的磨损减少了），而且还意味着具有高 MC 的机器人的驱动能量需求将减少 [8]。这对于腿式机器人非常有用，因为它们需要不受束缚才能在具有挑战性的地形上自主运行和运输有效负载。虽然 MC 在生物系统中的作用得到了很好的认可，但 Ghazi-Zahediet 等人 [8] 在对近期趋势的探索中指出，其在机器人技术中的应用仍未得到充分探索。作者强调的主要原因之一是传统的控制范式将身体视为需要被支配的东西，而不是被用作计算资源。人们倾向于通过使用控制系统和伺服电机来抑制任何不良的形态行为，例如非线性、欠驱动或噪声。值得注意的是，这些相同的复杂形态特性在自然系统的行为中发挥着关键作用，正如 Abad 等人在山羊蹄防滑 MC 和小狗 [7] 中的研究中所描述的那样，欠驱动机器人。同时，Deimal 等人[10]提出的挑战之一是确保系统利用形态（“良好的 MC”），同时避免与所需功能相关的有害的身体环境相互作用（“坏MC'）。为了实现 MC 和主动控制的正确平衡，需要一种正式的方法来测量系统中的 MC。 Ghazi-Zahedi 等人[6]在详细介绍算法的论文中展示了两种测量 MC 的方法，其中一种是将行为复杂性与控制器复杂性进行比较，前者通过世界状态信息进行比较，后者通过传感器状态信息进行比较。回顾当前学术界和工业界的情况得出的结论是，利用计算能力、集中控制和形态特征的相互作用的机器人系统具有巨大的潜力，可以在不同的应用中提高能源效率和多功能性