Hyperelastic Modeling and Validation of Hybrid-Actuated Soft Robot with Pressure-Stiffening

Editorial for the Special Issue on Soft Actuators: Design, Fabrication and Applications

The topic of soft robotics combines robotics, biology, and material sciences to develop the next generation of robots that are better suited to complex uncertain natural environments and human-centered operations with strict safety requirements [...].

Quasi-Static Modeling Framework for Soft Bellow-Based Biomimetic Actuators

Soft robots that incorporate elastomeric matrices and flexible materials have gained attention for their unique capabilities, surpassing those of rigid robots, with increased degrees of freedom and movement. Research has highlighted the adaptability, agility, and sensitivity of soft robotic actuators in various applications, including industrial grippers, locomotive robots, wearable assistive devices, and more. It has been demonstrated that bellow-shaped actuators exhibit greater efficiency compared to uniformly shaped fiber-reinforced actuators as they require less input pressure to achieve a comparable range of motion (ROM). Nevertheless, the mathematical quantification of the performance of bellow-based soft fluidic actuators is not well established due to their inherent non-uniform and complex structure, particularly when compared to fiber-reinforced actuators. Furthermore, the design of bellow dimensions is mostly based on intuition without standardized guidance and criteria. This article presents a comprehensive description of the quasi-static analytical modeling process used to analyze bellow-based soft actuators with linear extension. The results of the models are validated through finite element method (FEM) simulations and experimental testing, considering elongation in free space under fluidic pressurization. This study facilitates the determination of optimal geometrical parameters for bellow-based actuators, allowing for effective biomimetic robot design optimization and performance prediction.

Stereo Visual Servoing Control of a Soft Endoscope for Upper Gastrointestinal Endoscopic Submucosal Dissection

Quickly and accurately completing endoscopic submucosal dissection (ESD) operations within narrow lumens is currently challenging because of the environment's high flexibility, invisible collision, and natural tissue motion. This paper proposes a novel stereo visual servoing control for a dual-segment robotic endoscope (DSRE) for ESD surgery. Departing from conventional monocular-based methods, our DSRE leverages stereoscopic imaging to rapidly extract precise depth data, enabling quicker controller convergence and enhanced surgical accuracy. The system's dual-segment configuration enables agile maneuverability around lesions, while its compliant structure ensures adaptability within the surgical environment. The implemented stereo visual servo controller uses image features for real-time feedback and dynamically updates gain coefficients, facilitating rapid convergence to the target. In visual servoing experiments, the controller demonstrated strong performance across various tasks. Even when subjected to unknown external forces, the controller maintained robust performance in target tracking. The feasibility and effectiveness of the DSRE were further verified through ex vivo experiments. We posit that this novel system holds significant potential for clinical application in ESD surgeries.

Design Optimization of a Hybrid-Driven Soft Surgical Robot with Biomimetic Constraints

The current study investigated the geometry optimization of a hybrid-driven (based on the combination of air pressure and tendon tension) soft robot for use in robot-assisted intra-bronchial intervention. Soft robots, made from compliant materials, have gained popularity for use in surgical interventions due to their dexterity and safety. The current study aimed to design a catheter-like soft robot with an improved performance by minimizing radial expansion during inflation and increasing the force exerted on targeted tissues through geometry optimization. To do so, a finite element analysis (FEA) was employed to optimize the soft robot's geometry, considering a multi-objective goal function that incorporated factors such as chamber pressures, tendon tensions, and the cross-sectional area. To accomplish this, a cylindrical soft robot with three air chambers, three tendons, and a central working channel was considered. Then, the dimensions of the soft robot, including the length of the air chambers, the diameter of the air chambers, and the offsets of the air chambers and tendon routes, were optimized to minimize the goal function in an in-plane bending scenario. To accurately simulate the behavior of the soft robot, Ecoflex 00-50 samples were tested based on ISO 7743, and a hyperplastic model was fitted on the compression test data. The FEA simulations were performed using the response surface optimization (RSO) module in ANSYS software, which iteratively explored the design space based on defined objectives and constraints. Using RSO, 45 points of experiments were generated based on the geometrical and loading constraints. During the simulations, tendon force was applied to the tip of the soft robot, while simultaneously, air pressure was applied inside the chamber. Following the optimization of the geometry, a prototype of the soft robot with the optimized values was fabricated and tested in a phantom model, mimicking simulated surgical conditions. The decreased actuation effort and radial expansion of the soft robot resulting from the optimization process have the potential to increase the performance of the manipulator. This advancement led to improved control over the soft robot while additionally minimizing unnecessary cross-sectional expansion. The study demonstrates the effectiveness of the optimization methodology for refining the soft robot's design and highlights its potential for enhancing surgical interventions.