Tactile sensing provides critical information, such as force, texture, shape or temperature, in manipulation tasks. In particular, tactile sensors traditionally used in robotics are emphasized in contact force determination for grasping control and object recognition. Nevertheless, slip detection is also crucial to successfully manipulate an object. Several approaches have appeared to detect slipping, the majority being a combination of complex sensors with complex algorithms. In this paper, we deal with simplicity, analyzing how a novel, but simple, algorithm, based on micro-vibration detection, can be used in a simple, but low-cost and durable, force sensor. We also analyze the results of using the same principle to detect slipping in other force sensors based on flexible parts. In particular, we show and compare the slip detection with: (i) a flexible finger, designed by the authors, acting as a force sensor; (ii) the finger torque sensor of a commercial robotic hand; (iii) a commercial six-axis force sensor mounted on the wrist of a robot; and (iv) a fingertip piezoresistive matrix sensor.

Different 2D-layered materials of transition metal dichalcogenides (TMDCs) such as boron nitride (BN) or molybdenum disulphide (MoS₂) have been theorised to have piezoelectric behaviour.

A flexible cylindrical beam is used to design a sixaxis column type force and moment sensor for robotic applications where accurate small force and moment measurements are required. One of the ends of the beam is fixed and the other remains free. A rigid cover with a mushroom shape is placed at the free end. This cover is the contact surface between the sensor and the object being touched and transmits the contact forces and moments to the flexible beam. Twelve miniature strain gauges are strategically placed on the surface of the beam to measure the contact forces and moments by using a simple model based on the classical theory of elasticity. The calibration method of the sensor is performed by using the least-square fitting method and experimental results demonstrate that maximum and mean relative errors are below 9% and 3%, respectively, of full-scale in all measured variables.