Hybrid System Analysis and Control of a Soft Robotic Gripper with Embedded Proprioceptive Sensing for Enhanced Gripping Performance

Abstract

Soft robots are considered to have infinite degrees of freedom based on their structural compliance, providing high adaptability to the environments, and recent study has focused mostly on advancement of their physical designs for increasing the adaptability. However, interaction itself with the environment has not been taken into serious account in previous studies despite the importance in applications. A soft robot as a hybrid system described by both discrete and continuous states is considered and a method of analysis for enhanced manipulation is proposed. The method is tested with a soft gripper composed of a pneumatic bending actuator and an embedded soft sensor for a task of object gripping. The optimum sensor location on the actuator based on the calibration map obtained from the actuator characterization is first determined. Using the sensor information, the interaction with the environment (i.e., object) classified into four discrete states is understood. In addition, a control strategy to find the best position to grip the object based on the estimated states is developed. The gripper is able to successfully complete the task using the proposed method for three test scenarios with different initial conditions and control parameters. Finally, the results are demonstrated with supporting videos.