Motion Planning of Multiple-segment flexible soft, and continuum Flexible Manipulators by Learning from Human Expert Demonstrations
Multiple-segment flexible and soft robotic actuators exhibit compliance but suffer from the difficulty of path planning due to their redundant degrees of freedom, although they are promising in complex tasks such as crossing body cavities to grasp objects. We propose a learning from demonstration method to plan the motion paths of flexible manipulators, by statistics machine-learning algorithms. To encode demonstrated trajectories and estimate suitable paths for the manipulators to reproduce the task, models are built based on Gaussian Mixture Model and Gaussian Mixture Regression respectively. The forward and inverse kinematic models of soft robotic arm are derived for the motion control. A flexible and soft robotic manipulator verifies the learned paths by successfully completing a representative task of navigating through a narrow keyhole.