A Configuration Sensing Approach for Fabric-Reinforced Inflatable Soft Robots Based on Interior Illumination

Pose measurement and contact realization for soft robots are important but are also challenging to perform. In this article, a new approach is proposed which uses light transmission through the bore of the robot to sense its pose. By combining optical signals with inertial measurement units and pressure signals, along with governing models and machine learning algorithms, this sensing approach allows us to measure the pose of the soft robot in free space. It also allows the applied machine learning algorithm to estimate the robot configuration during contact events, predicting the location, direction, and magnitude of the contact force by learning from prior contact data in a training phase. The sensing devices used are affordable and widely available so that the robot can be mass-produced. Experimental results show that the proposed sensing system successfully estimates these quantities with good accuracy.