Tendon actuated multisection continuum arms have high potential for inspection applications in highly constrained spaces. They generate motion by axial and bending deformations. However, because of the high mechanical coupling between continuum sections, variable length-based kinematic models produce poor results. A new mechanics model for tendon actuated multisection continuum arms is proposed in this paper. The model combines the continuum arm curve parameter kinematics and concentric tube kinematics to correctly account for the large axial and bending deformations observed in the robot. Also, the model is computationally efficient and utilizes tendon tensions as the joint space variables thus eliminating the actuator length related problems such as slack and backlash. A recursive generalization of the model is also presented. Despite the high coupling between continuum sections, numerical results show that the model can be used for generating correct forward and inverse kinematic results. The model is then tested on a thin and long multisection continuum arm. The results show that the model can be used to successfully model the deformation.
This content will become publicly available on October 1, 2024
Kinematics and Stiffness Modeling of Soft Robot With a Concentric Backbone
Abstract Soft robots can undergo large elastic deformations and adapt to complex shapes. However, they lack the structural strength to withstand external loads due to the intrinsic compliance of fabrication materials (silicone or rubber). In this paper, we present a novel stiffness modulation approach that controls the robot’s stiffness on-demand without permanently affecting the intrinsic compliance of the elastomeric body. Inspired by concentric tube robots, this approach uses a Nitinol tube as the backbone, which can be slid in and out of the soft robot body to achieve robot pose or stiffness modulation. To validate the proposed idea, we fabricated a tendon-driven concentric tube (TDCT) soft robot and developed the model based on Cosserat rod theory. The model is validated in different scenarios by varying the joint-space tendon input and task-space external contact force. Experimental results indicate that the model is capable of estimating the shape of the TDCT soft robot with an average root-mean-square error (RMSE) of 0.90 (0.56% of total length) mm and average tip error of 1.49 (0.93% of total length) mm. Simulation studies demonstrate that the Nitinol backbone insertion can enhance the kinematic workspace and reduce the compliance of the TDCT soft robot by 57.7%. Two more »
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