Interfacing Soft and Hard: A Spring Reinforced Actuator.

Muscular hydrostats have long been a source of inspiration for soft robotic designs. With their inherent compliance, they excel in unpredictable environments and can gently manipulate objects with ease. However, their performance lacks where high force or a fast-dynamic response is needed. In this study, we propose a novel spring reinforced actuator (SRA) that explores the intermediate state between muscular hydrostats and endoskeletal mechanisms. The result is that we dramatically enhance the robot dynamic performance, which is unprecedented in similar kinds of soft robots, while retaining compliant omnidirectional bending. Analytical modeling of the flexible backbone was built and experimentally validated. This is also the first attempt to perform detailed finite element analysis to investigate the strain-stress behavior of the constraining braided bellow tube. The braided interweaving threads are modeled, in which complex thread-to-thread contacts occur. Experimental evaluation of SRAs was performed for actuation force, stiffness, and dynamic response. We showcase the enhanced actuator's performance in several applications such as locomotion and heavy object manipulation.

Nonparametric Online Learning Control for Soft Continuum Robot: An Enabling Technique for Effective Endoscopic Navigation.

Bioinspired robotic structures comprising soft actuation units have attracted increasing research interest. Taking advantage of its inherent compliance, soft robots can assure safe interaction with external environments, provided that precise and effective manipulation could be achieved. Endoscopy is a typical application. However, previous model-based control approaches often require simplified geometric assumptions on the soft manipulator, but which could be very inaccurate in the presence of unmodeled external interaction forces. In this study, we propose a generic control framework based on nonparametric and online, as well as local, training to learn the inverse model directly, without prior knowledge of the robot's structural parameters. Detailed experimental evaluation was conducted on a soft robot prototype with control redundancy, performing trajectory tracking in dynamically constrained environments. Advanced element formulation of finite element analysis is employed to initialize the control policy, hence eliminating the need for random exploration in the robot's workspace. The proposed control framework enabled a soft fluid-driven continuum robot to follow a 3D trajectory precisely, even under dynamic external disturbance. Such enhanced control accuracy and adaptability would facilitate effective endoscopic navigation in complex and changing environments.