The passive compliance of soft robotic arms renders the development of accurate kinematic models and model-based controllers challenging. The most widely used model in soft robotic kinematics assumes Piecewise Constant Curvature (PCC). However, PCC introduces errors when the robot is subject to external forces or even gravity. In this paper, we establish a three-dimensional (3D) kinematic representation of a soft robotic arm with pseudo universal and prismatic joints that are capable of capturing non-constant curvature deformations of the soft segments. We theoretically demonstrate that this constitutes a more general methodology than PCC. Simulations and experiments on the real robot attest to the superior modeling accuracy of our approach in 3D motions with unknown loads. The maximum position/rotation error of the proposed model is verified 6.7×/4.6× lower than the PCC model considering gravity and external forces. Furthermore, we devise an inverse kinematic controller that is capable of positioning the tip, tracking trajectories, as well as performing interactive tasks in the 3D space.

Soft compliant materials and novel actuation mechanisms ensure flexible motions and high adaptability for soft robots, but also increase the difficulty and complexity of constructing control systems. In this work, we provide an efficient control algorithm for a multi-segment extensible soft arm in 2D plane. The algorithm separate the inverse kinematics into two levels. The first level employs gradient descent to select optimized arm's pose (from task space to configuration space) according to designed cost functions. With consideration of viscoelasticity, the second level utilizes neural networks to figure out the pressures from each segment's pose (from configuration space to actuation space). In experiments with a physical prototype, the control accuracy and effectiveness are validated, where the control algorithm is further improved by an optional feedback strategy.

Soft manipulators have been a rising focus of soft robotics research. Taking advantage of soft materials and flexible, continuous movements, they have promising applicable prospect. However, their highly internal nonlinearity and unpredictable deformation caused by environmental effects make it difficult to build an exact model for control. In this work, we propose a generalized controller for soft manipulators using an estimated Jacobian-based model derived from structural analysis. The model can be simplified from reasonable assumptions of manipulator structure, and updated to balance conformity to reality and stability. In prototype experiments on an 3D multi-segment soft manipulator, the control method exhibits accuracy as well as adaptability to self gravity and external loads.

Most control methods of soft manipulators are developed based on physical models derived from mathematical analysis or learning methods. However, due to internal nonlinearity and external uncertain disturbances, it is difficult to build an accurate model, further, these methods lack robustness and portability among different prototypes. In this work, we propose a model-free control method based on reinforcement learning and implement it on a multi-segment soft manipulator in 2D plane, which focuses on the learning of control strategy rather than the physical model. The control strategy is validated to be effective and robust in prototype experiments, where we design a simulation method to speed up the training process.