Data-Driven and Compliance-Based Fault-Tolerance for a Flexible and Extendable Robotic Implant Coupled to a Growing Tissue
Data-Driven and Compliance-Based Fault-Tolerance for a Flexible and Extendable Robotic Implant Coupled to a Growing Tissue
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基于数据驱动和顺应性的柔性和可扩展机器人植入物的容错性
DOI：
10.1109/lra.2023.3243440
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发表时间：
2023-04
影响因子：
5.2
通讯作者：
M. Pontin;Dana D. Damian
中科院分区：
文献类型：
--
作者：
M. Pontin;Dana D. Damian
用于实时和长期监测和治疗的机器人植入物正在研究中，可能会在医疗领域开辟新的前沿。然而，要使这些设备得到广泛采用，仍然需要克服包括可靠性在内的关键挑战。多年来，已经开发了许多计算技术来为机器人和工业设备提供容错能力。然而，由于缺乏关于软组织的复杂行为（例如生长、粘弹性）和机器人-组织相互作用的信息，这些方法在机器人植入物中的应用仍然具有挑战性。在这封信中，提出了一种新的故障检测框架的灵活的可扩展的机器人植入物。基于典型相关分析，该方法利用机器人的灵活性，外推信息的故障识别的目的。实验是用软组织模拟器进行的，它可以模拟组织的粘弹性以及它的生长，提供了一个逼真的测试平台。实验证明了柔性可扩展机器人植入物的可靠性及其对系统级外部干扰的鲁棒性。还提供了长期测试，其中植入物延伸80 mm至其全长，在24小时内抵消模拟硬件故障，并为未来的体内试验提供了有希望的基础。
Robotic implants for real-time and long-term monitoring and therapies are being researched and could open new frontiers in the medical field. For these devices to see widespread adoption, though, key challenges still need to be overcome, including reliability. Over the years, many computational techniques have been developed to impart fault-tolerance to robots and industrial plants. However, the application of these approaches to robotic implants is still challenging, due to the lack of information about the complex behavior of soft tissue (e.g. growth, viscoelasticity) and robot-tissue interaction. In this letter, a novel fault detection framework for a flexible extendable robotic implant is presented. Based on Canonical Correlation Analysis, the approach exploits the flexibility of the robot to extrapolate information for fault identification purposes. The experiments are conducted with a soft tissue simulator, which can emulate the viscoelastic properties of tissue as well as its growth, providing a realistic testing platform. The experiments prove the reliability of the flexible extendable robotic implant and its robustness to system-level external disturbances. Long-term tests are also presented, where the implant extends 80 mm, to its full length, counteracting simulated hardware faults over a 24-hour period, and provide a promising basis for future in-vivo trials.