Direct 3D Printing of Highly Anisotropic, Flexible, Constriction-Resistive Sensors for Multidirectional...

A key missing technology for the emerging field of soft robotics is the provision of highly selective multidirectional tactile sensing that can be easily integrated into a robot using simple fabrication techniques. Conventional strain sensors, such as strain gauges, are typically designed to respond to strain in a single direction and are mounted on the external surface of a structure. Herein, we present a technique for three-dimensional (3D) printing of multidirectional, anisotropic, and constriction-resistive strain sensors, which can be directly integrated into the interior of soft robots. Using a carbon-nanotube-reinforced polylactic acid (PLA-CNT), both the sensing element and the conductive interconnect of the sensor system are 3D-printed. The sensor's sensitivity and anisotropy can be adjusted by controlling the air gap between printed adjacent tracks, infill density, and build orientation relative to the main loading direction. In particular, sensors printed with a near-zero air gap, i.e., adjacent tracks forming a kissing bond, can achieve a gauge factor of ∼1342 perpendicular to the raster orientation and a gauge factor of ∼1 parallel to the raster orientation. The maximum directional selectivity of this ultrasensitive sensor is 31.4, which is approximately 9 times greater than the highest value reported for multidirectional sensors so far. The high sensitivity stems from the progressive opening and closing of the kissing bond between adjacent tracks. The potential of this type of sensors and the simple manufacturing process are demonstrated by integrating the sensor with a soft robotic actuator. The sensors are able to identify and quantify the bending deformation and angle in different directions. The ability to fabricate sensors with tailored footprints and directional selectivity during 3D printing of soft robotic systems paves the way toward highly customizable, highly integrated multifunctional soft robots that are better able to sense both themselves and their environments.