The sense of touch is essential for reliable mapping between the environment and a robot which interacts physically with objects. Presumably, an artificial tactile skin would facilitate safe interaction of the robots with the environment. In this work, we present our color-coded tactile sensor, incorporating plastic optical fibers (POF), transparent silicone rubber and an off-the-shelf color camera. Processing electronics are placed away from the sensing surface to make the sensor robust to harsh environments. Contact localization is possible thanks to the lower number of light sources compared to the number of camera POFs. Classical machine learning techniques and a hierarchical classification scheme were used for contact localization. Specifically, we generated the mapping from stimulation to sensation of a robotic perception system using our sensor. We achieved a force sensing range up to 18 N with the force resolution of around 3.6 N and the spatial resolution of 8 mm. The color-coded tactile sensor is suitable for tactile exploration and might enable further innovations in robust tactile sensing.

Tactile sensing arrays are used to detect contacts of robotic systems with the environment. They are particularly useful for scenarios in which vision-based sensors cannot be used. Thanks to the presence of multiple sensing elements, tactile arrays also provide spatial information about the contact location. In this work, we present our series elastic tactile array to enable tactile exploration for position-controlled robot manipulators. Sixteen compliant sensing elements are arranged as a 4×4 array. This allows the position-controlled robot to explore objects via palpation. Tactile sensing was accomplished by measuring the change of the magnetic field caused by neodymium magnets embedded into the series elastic elements. We demonstrate the efficacy of our sensor with two sets of experiments involving physical interaction scenarios. Firstly, we show that the sensor can be used to differentiate between rigid and deformable objects. Secondly, we show that point clouds of objects can be generated quickly with our sensor module attached to a position-controlled robot manipulator as an end-effector.