Wearable sensors are gaining in popularity because they enable outdoor experimental monitoring. This paper presents a cost-effective sensorised insole based on a mesh of tactile capacitive sensors. Each sensor’s spatial resolution is about 4 taxels/cm ${}^2$ in order to have an accurate reconstruction of the contact pressure distribution. As a consequence, the insole provides information such as contact forces, moments, and centre of pressure. To retrieve this information, a calibration technique that fuses measurements from a vacuum chamber and shoes equipped with force/torque sensors is proposed. The validation analysis shows that the best performance achieved a root mean square error (RMSE) of about $7 \mathrm{N}$ for the contact forces and $2 \mathrm{N}\mathrm{m}$ for the contact moments when using the force/torque shoe data as ground truth. Thus, the insole may be an alternative to force/torque sensors for certain applications, with a considerably more cost-effective and less invasive hardware.

The paper presents a stochastic methodology for the simultaneous floating-base estimation of the human whole-body kinematics and dynamics (i.e., joint torques, internal and external forces). The paper builds upon our former work where a fixed-base formulation had been developed for the human estimation problem. The presented approach is validated by presenting experimental results of a health subject equipped with a wearable motion tracking system and a pair of shoes sensorized with force/torque sensors while performing different motion tasks, e.g., walking on a treadmill. The results show that joint torque estimates obtained by using floating-base and fixed-base approaches match satisfactorily, thus validating the present approach.

In this letter, we present algorithms to estimate contact locations, external forces, and joint torques using skin, i.e., distributed tactile sensors, kinematic sensors, and a single Inertial Measurement Unit (IMU) without the need for force-torque sensors. Distributed tactile sensors are an array of discrete tactile sensors (taxels) with gaps between them. To cope with the gaps, we use interpolation techniques to improve accuracy of the measurements in the skin. Then, we estimate the external force. Finally, we use this information combined with the contact location obtained from the skin to estimate the joint torques. Validation was performed using the iCub humanoid robot. Results show a performance comparable to the joint torque estimation using expensive force-torque sensors. Since the skin is less expensive and does not require modification of the internal structure of the robot, using the skin is an interesting solution.

Nonlinear controllers for floating base systems in contact with the environment are often framed as quadratic programming (QP) optimization problems. Common drawbacks of such QP-based controllers are: the control input often experiences discontinuities; no force feedback from force/torque (FT) sensors installed on the robot is taken into account. This article attempts to address these limitations using jerk-based control architectures. The proposed controllers assume the rate-of-change of the joint torques as control input, and exploit the system position, velocity, accelerations, and contact wrenches as measurable quantities. The key ingredient of the presented approach is a one-to-one correspondence between free variables and an inner approximation of the manifold defined by the contact stability constraints. More precisely, the proposed correspondence covers completely the contact stability manifold except for the socalled friction cone, for which there exists a unique correspondence for more than 90% of its elements. The correspondence allows us to transform the underlying constrained optimization problem into one that is unconstrained. Then, we propose a jerk control framework that exploits the proposed correspondence and uses FT measurements in the control loop. Furthermore, we present Lyapunov stable controllers for the system momentum in the jerk control framework. The approach is validated with simulations and experiments using the iCub humanoid robot.

Integrating shape memory polymers into additive manufacturing processes enables a form of 4D printing where a printed part can be manipulated into varying geometries upon the application of external stimuli. The work here explores the raster pattern sensitivity of the shape memory properties of two iterations of a polymer blend system composed of thermoplastic rubber and acrylonitrile butadiene styrene. Tensile test specimens were fabricated in three different raster patterns through the use of material extrusion additive manufacturing and deformed at room (25°C), low (−40°C) and high temperatures (105 and 110°C). Shape memory parameters were assessed and the shape fixation ratio was found to exhibit a sensitivity to raster pattern when deformation occurred at room and low temperatures, while the shape recovery ratio was found to be sensitive to raster pattern when deformation occurred at elevated temperatures. The influence of phase content was also explored and a decrease in rubber content led to an improvement in shape memory properties. The alignment of polymer phases with print raster direction was also found to influence raster pattern sensitivity.