Design and Validation of an Octopus-Inspired Suction Cup with High-Resolution Tactile Sensing for Soft Robotic Arms

Abstract

In the field of soft robotics, rigid joints and links are replaced by soft, deformable elements, This causes soft continuum robot arms to excel in unpredictable environments, but to face challenges during control and shape reconstruction. The sensing ability present in octopus suckers provides inspiration for solutions. Octopuses employ their suckers not only to strengthen their grasp but also as tactile sensors to control the shape and position of their soft arms. This has motivated researchers to integrate artificial sensorized suckers in soft continuum robot arms Although various sensorized suckers have already been developed, their employed sensing methods tend to be low in resolution and are often poorly embedded into the overall sucker architecture. In this work, these limits are overcome by presenting an octopus-inspired suction cup with integrated high-resolution tactile sensing abilities. This is achieved by utilizing the Chromatouch Principle, which relies on embedding colored markers in the suction cup membrane. Tracking these markers with a camera produced tactile images containing useful information about forces, deformations and interactions with objects. Fabrication with multi-material additive manufacturing enabled direct integration of these markers into the suction cup membranes. We demonstrated the design’s basic functionality by conducting pull-off and pickup tests. The design exhibited a normal pull-off force of 9.53 N and a shear pull-off force of 5.28 N. It was also able to successfully pick up both flat and curved objects. The sensing ability was showcased by training a Convolutional Neural Network to learn the relationship between the camera images and the orientation of the suction cup with respect to a touching substrate. Using a spherical coordinate system, the orientation could be predicted with an error of less than 2 degrees for latitude and less than 9 degrees for longitude. This performance was validated by using the trained network to successfully correct the orientation when picking up objects under an angle. For a single suction cup, this ability can be utilized to correct the orientation and achieve perpendicular contact with an object, crucial for achieving a seal. On a larger scale, the integration of multiple suction cups in soft continuum robot arms has the potential to form a representation of the arm shape as a whole. It can thereby contribute to overcoming the control challenges faced in the field of soft robotics.