Modelless Active Compliance for Continuum Robots using Recurrent Neural Networks Endowing continuum robots with compliance while it interacts with the internal environment of the human body is essential to prevent damage to the robot and the surrounding tissues. Compared with passive compliance, active compliance has the advantages in terms of increasing the force transmission ability and improving safety with monitored force output. Previous studies have demonstrated that active compliance can be achieved based on a complex model of the mechanics combined with a traditional machine learning technique such as a support vector machine. This paper proposes a recurrent neural network (RNN) based approach that avoids the complexity of modeling while capturing nonlinear factors such as hysteresis, friction and delay of the electronics that are not easy to model. The approach is tested on a 3tendon singlesegment continuum robot with force sensors on each cable. Experiments are conducted to demonstrate that the continuum robot with an RNN based feedforward controller is capable of responding to external forces quickly and entering an unknown environment compliantly.

A Learningbased Inverse Kinematics Solver for a MultiSegment Continuum Robot in RobotIndependent Mapping Inverse kinematics (IK) is one of the most fundamental problems in robotics, as it makes use of the kinematics equations to determine the joint configurations necessary to reach a desired endeffector pose. In the field of continuum robot, solving the IK is relatively challenging, owing to kinematic redundancy with infinite number of solutions.In this paper, we present a simplified model to represent a multisegment continuum robot using virtual rigid links. Based on the model, its IK can be solved using a multilayer perceptron (MLP), a class of feedforward neural network (FNN). The transformation between virtual joint space to task space is described using DenavitHartenberg (DH) convention. Using 20,000 established training data for supervised learning, the MLP reaches a mean squared error of 0.022 for a dualsegment continuum robot. The trained MLP is then used to find the joints for different endeffector positions, and the results show a mean relative error of 2.90% can be on the robot configuration. Hence, this simplified model and its MLP provide a simple method to evaluate the IK solution of a twosegment continuum robot, which can also be further generalized and implemented in multisegment cases.