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

Abstract

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.

Keywords: design optimization; finite element simulation; hybrid-driven; minimally invasive intervention; soft robot.

Figure 1

Hybrid-driven soft surgical robot inside the lungs during an intra-bronchial intervention.

Figure 2

Setup architecture of the hybrid air–tendon-driven soft robot for use in RAMIS.

Figure 3

Design variables (a) $D_w$ represents the diameter of the working channel, $D_{ch}$ is the diameter of the air chambers, $D_t$ is the diameter of the tendon passes, $D_o$ is the outer diameter of the soft robot, and $a_{ch}$ and $a_t$ represent the offsets of the air chambers and tendon passes from the center of the cross-section, respectively. (b) $L_{ch}$ represents the length of the air chambers.

Figure 4

Compression test performed with Bose UTM on the Ecoflex-50 samples based on ISO 7743.

Figure 5

(a) Engineering stress–strain compression curve (b) comprehensive compression–tension engineering stress–strain curve for Ecoflex 00-50. The tension data extracted from [61].

Figure 5

(a) Engineering stress–strain compression curve (b) comprehensive compression–tension engineering stress–strain curve for Ecoflex 00-50. The tension data extracted from [61].

Figure 6

Deformation of the soft robot (a) caused by increasing the air pressure inside the air chamber and tendon tension (b) Cross-section of the soft robot, illustrating the chamber undergoing pressurization and the tendon being pulled.

Figure 7

Variation in the bending angle of the soft robot vs. (a) the air chamber diameter, $D_{Ch}$ , (b) tendon offset $a_t$ , (c) air chamber length $L_{Ch}$ , and- (d) air chamber offset $a_{Ch}$ .

Figure 8

Deformation of the soft robot with the optimized parameters (a) variation in the bending angle vs. the air chamber diameter and offset. (b) Variation in the bending angle vs. the tendon force and air chamber pressure. (c) Variation in the outer radius of the soft robot vs. the air chamber pressure and diameter.

Figure 9

Mold design of the hybrid air–tendon-driven soft robot with a central working channel.

Figure 10

Linear actuator of the soft robot: ① NEMA 17 stepper motor, ② shaft coupler, ③ EPOS4 3-axes digital positioning controller of the motors, ④ silicone tube, ⑤ holder of the robotic arm, ⑥ double bearing and lead screw, ⑦ bearing, ⑧ screws, and ⑨ brushless DC motor with Hall sensors.

Figure 11

Integrated hybrid-driven soft robot into the CRS robotic arm and the phantom model.