G.B.), m.ghajari@imperial.ac.uk (M.G.), r.hewson@imperial.ac.uk (R.H.), A.Alazmani@leeds.ac.uk (A.A.), P.R.Culmer@leeds.ac.uk (P.C.). AbstractTactile sensors are essential for robotic systems to interact safely and effectively with the external world, they also play a vital role in some smart healthcare systems. Despite advances in areas including materials/composites, electronics and fabrication techniques, it remains challenging to develop low cost, high performance, durable, robust, soft tactile sensors for realworld applications. This paper presents the first Soft Inductive Tactile Sensor (SITS) which exploits an inductance-transducer mechanism based on the eddy-current effect. SITSs measure the inductance variation caused by changes in AC magnetic field coupling between coils and conductive films. Design methodologies for SITSs are discussed by drawing on the underlying physics and computational models, which are used to develop a range of SITS prototypes. An exemplar prototype achieves a state-of-the-art resolution of 0.82 mN with a measurement range over 15 N. Further tests demonstrate that SITSs have low hysteresis, good repeatability, wide bandwidth, and an ability to operate in harsh environments. Moreover, they can be readily fabricated in a durable form and their design is inherently extensible as highlighted by a 4x4 SITS array prototype. These outcomes show the potential of SITS systems to further advance tactile sensing solutions for integration into demanding real-world applications.

This work focuses on the proposed mechanisms for the lubrication of synovial joints and applies them to an idealised bearing geometry considering a porohyperelastic material (cartilage) rotating against a stationary rigid impermeable surface. The model captures the behaviour of all lubrication regimes including fluid film formation and boundary contact as the load capacity is increased, representing a major advancement in modelling cartilage mechanics. Transient responses in the fluid phase are shown to be faster than those in the solid phase with the former decaying over time as fluid is exuded from the material. The complex behaviour of fluid migrating to and from the lubricating film is captured which leads to a better understanding of the hydration and friction mechanisms observed.

This paper investigates the design optimisation of a magnetic field based soft tactile sensor, comprised of a magnet and Hall effect module separated by an elastomer. The aim was to minimise sensitivity of the output force with respect to the input magnetic field; this was achieved by varying the geometry and material properties. Finite element simulations determined the magnetic field and structural behaviour under load. Genetic programming produced phenomenological expressions describing these responses. Optimisation studies constrained by a measurable force and stable loading conditions were conducted; these produced Pareto sets of designs from which the optimal sensor characteristics were selected. The optimisation demonstrated a compromise between sensitivity and the measurable force, a fabricated version of the optimised sensor validated the improvements made using this methodology. The approach presented can be applied in general for optimising soft tactile sensor designs over a range of applications and sensing modes.

The frictional performance of a bearing is of significant interest in any mechanical system where there are lubricated surfaces under load and in relative motion. Surface topography plays a major role in determining the coefficient of friction for the bearing because the size of the fluid film and topography are of a comparable order. The problem of optimising topography for such a system is complicated by the separation in scales between the size of the lubricated domain and that of the topography, which is of at least one order of magnitude or more smaller. This paper introduces a multiscale method for optimising the small scale topography for improved frictional performance of the large scale bearing. The approach fully couples the elastohydrodynamic lubrication at both scales between pressure generated in the lubricant and deformation of the bounding surfaces. Homogenised small scale data is used to inform the large scale model and is represented using Moving Least Squares metamodels calibrated by cross validation. An optimal topography for a minimum coefficient of friction for the bearing is identified and comparisons made of local minima in the response, where very different topographies with similar frictional performance are observed. Comparisons of the optimal topography with the smooth surface model demonstrated the complexity of capturing the non-linear effect of topography and the necessity of the multiscale method in capturing this. Deviations from the smooth surface model were quantified by the metamodel coefficients and showed how topographies with a similar frictional performance have very different characteristics.

The mechanisms of friction in natural joints are still relatively unknown and attempts at modelling cartilage-cartilage interfaces are rare despite the huge promise they offer in understanding bio-friction. This article derives a model combining finite strain, porous and thin film flow theories to describe the lubrication of cartilageon-cartilage line contacts. The material is modelled as compliant and poroelastic in which the micro-scale fibrous structure is interstitially filled with synovial fluid. This fluid flows over the interface between the bodies and is coupled to pressure generated by relative motion in the thin-film region formed under load. A Stribeck analysis demonstrated that this type of contact is determinable to conventional elastic lubrication but that the friction performance is improved by this interfacial flow. Moreover, the inclusion of periodic flow conditions when contact is onset is a specific novelty which elucidates new observations in the lubrication mechanisms pertaining to natural joints.