Gesture-Based Steering Framework for Redundant Soft Robots

Traditional soft robot steering using different devices, such as a mouse or joystick, provides convenient ways to move the robot's tip around in omni-direction, but they may need to be more user-friendly for redundant soft robots as the null space motion can be difficult to teleoperate. In this work, we propose an intuitive approach as a framework to steer a two-segment soft-bodied robot in 3-D space using motile gestures. A lightweight hand-tracking device is employed as the input device to define the position and orientation of the robot tip, and the tip motion can be synchronized from the hand gesture - which is estimated and enhanced by a Kalman filter and low-pass filter with spherical linear interpolation - in real-time. The inverse kinematics (IK) can be rapidly computed based on the convex optimization online. Different hand gestures can be understood to execute the prescribed constrained robot motion. A hardware-in-the-loop simulator and extensive prototype experiments are presented to verify the performance and robustness of the framework. Experiment results show that the master-follower control runs with a steady 11 Hz update rate, and the measured robot's configurations are within 2 mm (for a 100-mm slender manipulator) in either axis in free space steering. This work contributes an intuitive, comprehensible, and low-cost steering framework for redundant soft robots with multimode motion, rapid IK computation, and satisfactory accuracy.