Sahu Sujit Kumar, Sozer Canberk, Rosa Benoit, Tamadon Izadyar, Renaud Pierre, Menciassi Arianna
The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy.
Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa, Italy.
Front Robot AI. 2021 Nov 19;8:758411. doi: 10.3389/frobt.2021.758411. eCollection 2021.
Soft and continuum robots are transforming medical interventions thanks to their flexibility, miniaturization, and multidirectional movement abilities. Although flexibility enables reaching targets in unstructured and dynamic environments, it also creates challenges for control, especially due to interactions with the anatomy. Thus, in recent years lots of efforts have been devoted for the development of shape reconstruction methods, with the advancement of different kinematic models, sensors, and imaging techniques. These methods can increase the performance of the control action as well as provide the tip position of robotic manipulators relative to the anatomy. Each method, however, has its advantages and disadvantages and can be worthwhile in different situations. For example, electromagnetic (EM) and Fiber Bragg Grating (FBG) sensor-based shape reconstruction methods can be used in small-scale robots due to their advantages thanks to miniaturization, fast response, and high sensitivity. Yet, the problem of electromagnetic interference in the case of EM sensors, and poor response to high strains in the case of FBG sensors need to be considered. To help the reader make a suitable choice, this paper presents a review of recent progress on shape reconstruction methods, based on a systematic literature search, excluding pure kinematic models. Methods are classified into two categories. First, sensor-based techniques are presented that discuss the use of various sensors such as FBG, EM, and passive stretchable sensors for reconstructing the shape of the robots. Second, imaging-based methods are discussed that utilize images from different imaging systems such as fluoroscopy, endoscopy cameras, and ultrasound for the shape reconstruction process. The applicability, benefits, and limitations of each method are discussed. Finally, the paper draws some future promising directions for the enhancement of the shape reconstruction methods by discussing open questions and alternative methods.
柔性和连续体机器人因其灵活性、小型化和多向运动能力正在改变医疗干预方式。尽管灵活性使机器人能够在非结构化和动态环境中到达目标，但它也给控制带来了挑战，尤其是由于与解剖结构的相互作用。因此，近年来，随着不同运动学模型、传感器和成像技术的发展，人们在形状重建方法的开发上投入了大量精力。这些方法可以提高控制动作的性能，并提供机器人操纵器相对于解剖结构的尖端位置。然而，每种方法都有其优缺点，在不同情况下都可能有价值。例如，基于电磁（EM）和光纤布拉格光栅（FBG）传感器的形状重建方法，由于其在小型化、快速响应和高灵敏度方面的优势，可用于小型机器人。然而，需要考虑EM传感器的电磁干扰问题，以及FBG传感器在高应变情况下的响应不佳问题。为了帮助读者做出合适的选择，本文基于系统的文献检索，对形状重建方法的最新进展进行了综述，不包括纯运动学模型。方法分为两类。首先，介绍了基于传感器的技术，讨论了使用各种传感器（如FBG、EM和被动可拉伸传感器）来重建机器人形状。其次，讨论了基于成像的方法，这些方法利用来自不同成像系统（如荧光透视、内窥镜摄像头和超声）的图像进行形状重建过程。讨论了每种方法的适用性、优点和局限性。最后，本文通过讨论未解决的问题和替代方法，为形状重建方法的改进提出了一些未来有前景的方向。
