【标题】SOFT ROBOTS: Increasing the payload capacity of soft robot arms by localized stiffening
【参考中译】软机器人：通过局部加强来提高软机器人手臂的有效载荷能力
【类型】 期刊
【作者】 Daniel Bruder; Moritz A. Graule; Clark B. Teeple; Robert J. Wood
【摘要】 Soft robot arms offer safety and adaptability due to their passive compliance, but this compliance typically limits their payload capacity and prevents them from performing many tasks. This paper presents a model-based design approach to effectively increase the payload capacity of soft robot arms. The proposed approach uses localized body stiffening to decrease the compliance at the end effector without sacrificing the robot's range of motion. This approach is validated on both a simulated and a real soft robot arm, where experiments show that increasing the stiffness of localized regions of their bodies reduces the compliance at the end effector and increases the height to which the arm can lift a payload. By increasing the payload capacity of soft robot arms, this approach has the potential to improve their efficacy in a variety of tasks including object manipulation and exploration of cluttered environments.
【参考中译】 软机械臂由于其被动合规性而提供了安全性和适应性，但这种合规性通常限制了它们的有效载荷能力，并阻止它们执行许多任务。提出了一种基于模型的设计方法，有效地提高了软机械臂的承载能力。该方法在不牺牲机器人S运动范围的情况下，使用局部身体刚化来降低末端执行器的柔度。该方法在模拟和真实的软机械臂上都得到了验证，实验表明，增加机器人身体局部区域的刚度可以降低末端执行器的柔度，并增加手臂可以举起有效载荷的高度。通过增加软机械臂的有效载荷能力，该方法有可能提高其在各种任务中的效率，包括物体操纵和探索杂乱的环境。
【来源】 Science Robotics 2023, vol.8, no.81
【入库时间】 2023/11/28
【标题】MEDICAL ROBOTS: Robotic self-modulation enhances implantable long-acting drug delivery devices
【参考中译】医疗机器人：机器人自我调节增强了可植入的长效药物输送装置
【作者】 Tejal Desai; Alessandro Grattoni
【摘要】 Integrating fibrotic capsule sensing with soft robotics may boost long-term performance of implantable drug delivery devices. Long-acting (LA) drug delivery implants play an important role in medicine because they offer a solution for sustained and controlled release of medications, reducing the need for frequent dosing and improving patient compliance. These implants hold great potential for preventing and treating chronic conditions and provide therapeutic benefits over an extended period, enhancing the overall drug effectiveness. Upon deployment, LA drug delivery implants trigger foreign body response (FBR), which involves inflammation, tissue remodeling, and fibrosis (1). In numerous instances, FBR leads to a complete fibrotic encapsulation of the implant, which could compromise its function and longevity. Sustained low-dose LA devices relying on drug diffusion across nanoporous or nanofluidic membranes are unaffected by fibrotic encapsulation (2). In contrast, fibrosis can substantially influence the performance of LA systems that use convection to achieve drug delivery across micro- and macro-orifices. This is the case of infusion pumps and intrathecal drug delivery systems that rely on catheters to convectively deliver medications into the body and specific tissues. Fibrotic encapsulation may also impair the function of drug delivery implants designed for pulsatile drug delivery (3) or emergency medicine, requiring larger boluses of medication to be delivered within the body within seconds or minutes. The formation of fibrous tissue around the orifices can obstruct drug delivery and alter the release profile, leading to unpredictable and suboptimal therapeutic outcomes.
【参考中译】 将纤维性胶囊传感与软机器人技术相结合，可能会提高植入型药物输送设备的长期性能。长效(LA)给药植入物在医学上发挥着重要作用，因为它们为药物的持续和受控释放提供了解决方案，减少了频繁给药的需要，并提高了患者的依从性。这些植入物在预防和治疗慢性病方面具有巨大潜力，并在较长时间内提供治疗益处，提高整体药物效果。在部署时，LA药物输送植入物会引发异体反应(FBR)，其中涉及炎症、组织重塑和纤维化(1)。在许多情况下，FBR会导致植入物完全被纤维包裹，这可能会影响其功能和寿命。依赖于药物通过纳米孔膜或纳米流体膜扩散的持续低剂量LA设备不受纤维包裹的影响(2)。相比之下，纤维化可以显著影响LA系统的性能，该系统利用对流通过微孔和宏孔实现药物输送。这就是输液泵和鞘内药物输送系统的情况，它们依靠导管对流地将药物输送到体内和特定组织。纤维性胶囊也可能损害为脉冲式药物输送(3)或急诊药物设计的药物输送植入物的功能，需要在几秒钟或几分钟内在体内输送更大剂量的药物。孔口周围纤维组织的形成可能会阻碍药物的输送，改变药物的释放情况，导致不可预测的和次优的治疗结果。
【标题】SOFT ROBOTS: Control of soft robots with inertial dynamics
【参考中译】软机器人：具有惯性动力学的软机器人控制
【作者】 David A. Haggerty; Michael J. Banks; Ervin Kamenar; Alan B. Cao; Patrick C. Curtis; Igor Mezic; Elliot W. Hawkes
【摘要】 Soft robots promise improved safety and capability over rigid robots when deployed near humans or in complex, delicate, and dynamic environments. However, infinite degrees of freedom and the potential for highly nonlinear dynamics severely complicate their modeling and control. Analytical and machine learning methodologies have been applied to model soft robots but with constraints: quasi-static motions, quasi-linear deflections, or both. Here, we advance the modeling and control of soft robots into the inertial, nonlinear regime. We controlled motions of a soft, continuum arm with velocities 10 times larger and accelerations 40 times larger than those of previous work and did so for high-deflection shapes with more than 110° of curvature. We leveraged a data-driven learning approach for modeling, based on Koopman operator theory, and we introduce the concept of the static Koopman operator as a pregain term in optimal control. Our approach is rapid, requiring less than 5 min of training; is computationally low cost, requiring as little as 0.5 s to build the model; and is design agnostic, learning and accurately controlling two morphologically different soft robots. This work advances rapid modeling and control for soft robots from the realm of quasi-static to inertial, laying the ground-work for the next generation of compliant and highly dynamic robots.
【参考中译】 当软机器人部署在人类附近或复杂、精细和动态的环境中时，与刚性机器人相比，软机器人有望提高安全性和能力。然而，无限多的自由度和高度非线性动力学的可能性严重地使它们的建模和控制变得复杂。分析和机器学习方法已被应用于软机器人的建模，但带有约束：准静态运动、准线性偏转或两者兼而有之。在这里，我们将软机器人的建模和控制推进到惯性、非线性区域。我们控制了一个柔软、连续的手臂的运动，速度是以前工作的10倍，加速度是以前工作的40倍，对于曲率超过110°的大挠度形状也是如此。我们利用基于Koopman算子理论的数据驱动学习方法进行建模，并引入静态Koopman算子的概念作为最优控制中的预增益项。我们的方法快速，只需要不到5分钟的训练；计算成本低，只需要0.5%的S就可以建立模型；并且不依赖于设计，学习和精确控制两个形态不同的软机器人。这项工作将软机器人的快速建模和控制从准静态领域推进到惯性领域，为下一代柔顺和高动态机器人奠定了基础。