Abstract:This paper introduces a Cosserat rod based mathematical model for modeling a self-controllable variable curvature soft continuum robot. This soft continuum robot has a hollow inner channel and was developed with the ability to perform variable curvature utilizing a growing spine. The growing spine is able to grow and retract while modifies its stiffness through milli-size particle (glass bubble) granular jamming. This soft continuum robot can then perform continuous curvature variation, unlike previous approac… Show more
“…For a comprehensive study on the modeling of continuum robots, see [25,26]. [23] Cosserat Linear × × 7% Wang et al [27] Cosserat Linear × -Dou et al [28] Euler-Bernoulli Linear × Less than 8% Huang et al [29] Variable Curvature Linear × × 2.89% Niu et al [30] Cosserat Linear × × Less than 4% Ghoreishi et al [31] Euler-Bernoulli Linear × -Li et al [32] Cosserat Linear × Less than 5% Caasenbrood et al [33] Piece-wise Constant Curvature Non-Linear × RMS error was ±0.55…”
Soft robots have gained popularity, especially in intraluminal applications, because their soft bodies make them safer for surgical interventions than flexures with rigid backbones. This study investigates a pressure-regulating stiffness tendon-driven soft robot and provides a continuum mechanics model for it towards using that in adaptive stiffness applications. To this end, first, a central single-chamber pneumatic and tri-tendon-driven soft robot was designed and fabricated. Afterward, the classic Cosserat’s rod model was adopted and augmented with the hyperelastic material model. The model was then formulated as a boundary-value problem and was solved using the shooting method. To identify the pressure-stiffening effect, a parameter-identification problem was formulated to identify the relationship between the flexural rigidity of the soft robot and internal pressure. The flexural rigidity of the robot at various pressures was optimized to match theoretical deformation and experiments. The theoretical findings of arbitrary pressures were then compared with the experiment for validation. The internal chamber pressure was in the range of 0 to 40 kPa and the tendon tensions were in the range of 0 to 3 N. The theoretical and experimental findings were in fair agreement for tip displacement with a maximum error of 6.40% of the flexure’s length.
“…For a comprehensive study on the modeling of continuum robots, see [25,26]. [23] Cosserat Linear × × 7% Wang et al [27] Cosserat Linear × -Dou et al [28] Euler-Bernoulli Linear × Less than 8% Huang et al [29] Variable Curvature Linear × × 2.89% Niu et al [30] Cosserat Linear × × Less than 4% Ghoreishi et al [31] Euler-Bernoulli Linear × -Li et al [32] Cosserat Linear × Less than 5% Caasenbrood et al [33] Piece-wise Constant Curvature Non-Linear × RMS error was ±0.55…”
Soft robots have gained popularity, especially in intraluminal applications, because their soft bodies make them safer for surgical interventions than flexures with rigid backbones. This study investigates a pressure-regulating stiffness tendon-driven soft robot and provides a continuum mechanics model for it towards using that in adaptive stiffness applications. To this end, first, a central single-chamber pneumatic and tri-tendon-driven soft robot was designed and fabricated. Afterward, the classic Cosserat’s rod model was adopted and augmented with the hyperelastic material model. The model was then formulated as a boundary-value problem and was solved using the shooting method. To identify the pressure-stiffening effect, a parameter-identification problem was formulated to identify the relationship between the flexural rigidity of the soft robot and internal pressure. The flexural rigidity of the robot at various pressures was optimized to match theoretical deformation and experiments. The theoretical findings of arbitrary pressures were then compared with the experiment for validation. The internal chamber pressure was in the range of 0 to 40 kPa and the tendon tensions were in the range of 0 to 3 N. The theoretical and experimental findings were in fair agreement for tip displacement with a maximum error of 6.40% of the flexure’s length.
“…Dynamic movements primarily arise from the structure of the robot, the loading conditions, and the discontinuous changes in loading, rendering quasi-static assumptions inadequate for accurately estimating deformations in soft robots [26]. Numerous approaches have been proposed in the literature for the dynamic modeling of soft robots [27,28]. For instance, the piecewise constant curvature (PCC) representation has been employed to model robot shapes [29,30].…”
Soft robotics has emerged as a promising field due to the unique characteristics offered by compliant and flexible structures. Overcoming the challenge of precise position control is crucial in the development of such systems that require accurate modeling of soft robots. In response, a hybrid-actuated soft robot employing both air pressure and tendons was proposed, modeled, and validated using the dynamic Cosserat rod theory. This approach comprehensively addresses various aspects of deformation, including bending, torsion, shear, and extension. The designed robot was intended for robot-assisted cardiac ablation, a minimally invasive procedure that is used to treat cardiac arrhythmias. Within the framework of the Cosserat model, dynamic equations were discretized over time, and ordinary differential equations (ODEs) were solved at each time step. These equations of motion facilitated the prediction of the robot’s response to different control inputs, such as the air pressure and tension applied to the tendons. Experimental studies were conducted on a physical prototype to examine the accuracy of the model. The experiments covered a tension range of 0 to 3 N for each tendon and an air pressure range of 0 to 40 kPa for the central chamber. The results confirmed the accuracy of the model, demonstrating that the dynamic equations successfully predicted the robot’s motion in response to diverse control inputs.
“…Piecewise Constant Curvature (PCC) modeling [25][26][27], Cosserot rod theory modeling [18,[28][29][30], disc-thread modeling [31,32], and 3D Finite Element Models [33][34][35] have been vastly explored. However, using Cosserot rod theory and disc-thread modeling presents an overall increased computational difficulty.…”
The rapid advancement in physical human-robot interaction (HRI) has propelled the growth of soft robot design and, in parallel, soft robot controllers. Controlling soft robots is complex due to their wide range of movements, prompting the use of simplified model-based controllers to provide sufficient information for real-time high dynamic response control performance. However, most modeling techniques face computational efficiency and complexity of parameter identification issues and are hard to apply to real-time controls. To alleviate this, we employ a coupled analytical modeling approach based on Pseudo-Rigid Body Modeling and the Logarithmic Decrement Method for parameter estimation (PRBM+LDM). Using a soft robot hand test bed, we demonstrate the accuracy of PRBM+LDM to model position and force output as a function of pressure input and benchmark its performance. We, then, apply the PRBM+LDM model as a basis for a closed-loop position controller and compare its performance against a simple PID controller. Furthermore, we apply the PRBM+LDM model as a closed-loop force controller and compare its performance with simple constant pressure grasping control by performing small contact areas pinching tasks on low-weight, small objects - a screwdriver, a potato chip, and a brass coin. The PRBM+LDM-based position controller (Average Max. Error across all fingers: 4.37°) outperformed the simple PID position controller (Average Max. Error across all fingers: 20.38°). Furthermore, the PRBM+LDM-based force controller (Potato chip: 86%, Screwdriver: 74.42%, Brass coin: 64.75%) achieved a higher success rate than the constant pressure grasping control (Potato chip: 82.5%, Screwdriver: 70%, Brass coin: 35%) in the pinching tasks. We conclude that the PRBM+LDM modeling technique proves to be a convenient and efficient way to model the dynamic behavior of soft actuators closely and can be used to build high-precision position and force controllers. In application, it realizes stable, flexible grasping of small objects by exerting precise contact force on contact areas.
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