Affiliation:
1. Department of Mechatronics and Robotics Engineering, Egypt-Japan University of Science and Technology (E-JUST), Alexandria 21934, Egypt
2. Department of ICT and Natural Sciences, Norwegian University of Science and Technology, Larsgårdsvegen 2, 6009 Alesund, Norway
Abstract
Soft continuum robots, with their flexible and deformable structures, excel in tasks requiring delicate manipulation and navigation through complex environments. Accurate shape sensing is vital to enhance their performance, safety, and adaptability. Unlike rigid sensors, soft sensors conform to the robot’s flexible surfaces, ensuring consistent measurement of shape and motion. This paper introduces a new approach using soft e-textile resistive sensors, which integrate seamlessly with the robot’s structure. These sensors adjust their resistance in response to movements, capturing multidimensional force data. A deep Convolutional Neural Network (CNN) decodes the sensor signals, enabling precise shape estimation and control. Our findings indicate that soft e-textile sensors may surpass traditional rigid sensors in shape sensing and control, significantly improving the functionality of soft continuum robots in challenging applications.