Zhanchi Wang

Zhanchi Wang, Gaotian Wang, Xiaoping Chen et al.

The 'infinite' passive degrees of freedom of soft robotic arms render their control especially challenging. In this paper, we leverage a previously developed model, which drawing equivalence of the soft arm to a series of universal joints, to design two closed-loop controllers: a configuration space controller for trajectory tracking and a task space controller for position control of the end effector. Extensive experiments and simulations on a four-segment soft arm attest to substantial improvement in terms of: a) superior tracking accuracy of the configuration space controller and b) reduced settling time and steady-state error of the task space controller. The task space controller is also verified to be effective in the presence of interactions between the soft arm and the environment.

Zhanchi Wang, Gaotian Wang, Nikolaos M. Freris

The compliance of soft robotic arms renders the development of accurate kinematic & dynamical models especially challenging. The most widely used model in soft robotic kinematics assumes Piecewise Constant Curvature (PCC). However, PCC fails to effectively handle external forces, or even the influence of gravity, since the robot does not deform with a constant curvature under these conditions. In this paper, we establish three-dimensional (3D) modeling of a multi-segment soft robotic arm under the less restrictive assumption that each segment of the arm is deformed on a plane without twisting. We devise a kinematic and dynamical model for the soft arm by deriving equivalence to a serial universal joint robot. Numerous experiments on the real robot platform along with simulations attest to the modeling accuracy of our approach in 3D motion with load. The maximum position/rotation error of the proposed model is verified 6.7x/4.6x lower than the PCC model considering gravity and external forces.

Hao Jiang, Zhanchi Wang, Yusong Jin et al.

This paper presents the design, control, and applications of a multi-segment soft robotic arm. In order to design a soft arm with large load capacity, several design principles are proposed by analyzing two kinds of buckling issues, under which we present a novel structure named Honeycomb Pneumatic Networks (HPN). Parameter optimization method, based on finite element method (FEM), is proposed to optimize HPN Arm design parameters. Through a quick fabrication process, several prototypes with different performance are made, one of which can achieve the transverse load capacity of 3 kg under 3 bar pressure. Next, considering different internal and external conditions, we develop three controllers according to different model precision. Specifically, based on accurate model, an open-loop controller is realized by combining piece-wise constant curvature (PCC) modeling method and machine learning method. Based on inaccurate model, a feedback controller, using estimated Jacobian, is realized in 3D space. A model-free controller, using reinforcement learning to learn a control policy rather than a model, is realized in 2D plane, with minimal training data. Then, these three control methods are compared on a same experiment platform to explore the applicability of different methods under different conditions. Lastly, we figure out that soft arm can greatly simplify the perception, planning, and control of interaction tasks through its compliance, which is its main advantage over the rigid arm. Through plentiful experiments in three interaction application scenarios, human-robot interaction, free space interaction task, and confined space interaction task, we demonstrate the potential application prospect of the soft arm.