Researches on biological adhesive systems in nature have changed a perspective view on adhesion that it is not only the area of surface chemistry, but also mechanics of interfacial geometry which can significantly effect on fracture strength and load distribution on the contact interface. Various synthetic fibrillar adhesives in previous works have shown enhanced interfacial bond strength with the capacity of adhesion control by exploiting mechanical deformation of the elastomeric fibrillar structures inspired by geckos. However, control of the interfacial load distribution has been focused on the size of micro-contact with single or a few of micro-/nano-fibers on planar surface, and not for a large contact area on complex three-dimensional (3D) surfaces. This thesis work aims at investigating principles of the interfacial load distribution control in multi-scale, ranging from micro-contact with single micro-fiber to a centimeter-scale contact with a membrane-backed micro-fiber array on non-planar 3D surfaces. The findings are also applied for developing a soft robotic gripper capable of grasping a wide range of complex objects in size, shape, and number, expanding the area of practical applications for bio-inspired adhesives in transfer printing, robotic manipulators, and mobile robots. This paper comprises three main works. First, we investigate the effect of tip-shapes on the interfacial load sharing of mushroom-shaped micro-fibrillar adhesives with precisely defined tipgeometries using high resolution 3D nano-fabrication technique. For a large area of non-planar contact interface, we fabricate fibrillar adhesives on a membrane (FAM) by integrating micro-fibers with a soft backing, which enables robust and controllable adhesion on 3D surfaces. Picking and releasing mechanism for the maximal controllability in adhesion are discussed. Finally, we propose a soft robotic architecture which can control the interfacial load distribution for the FAM on 3D surfaces, solving an inherit dilemma between conformability and high fracture strength with the equal load sharing on complex non-planar 3D surfaces.

We consider the problem of estimating the failure stresses of bundles (i.e. the tensile forces that destroy the bundles), constructed of several statisti-cally similar fibres, given a particular kind of censored data. Each bundle consists of several fibres which have their own independent identically dis-tributed failure stresses, and where the force applied on a bundle at any moment is distributed equally between the unbroken fibres in the bundle. A bundle with these properties is an example of an equal load-sharing sys-tem, often referred to as the Daniels failure model. The testing of several bundles generates a special kind of censored data, which is complexly struc-tured. Strongly consistent non-parametric estimators of the distribution laws of bundles are obtained by applying the theory of martingales, and by using the observed data. It is proved that random sampling, with replace-ment from the statistical data related to each tested bundle, can be used to obtain asymptotically correct estimators for the distribution functions of deviations of non-parametric estimators from true values. In the case when the failure stresses of the fibres are described by a Weibull distribution, we obtain strongly consistent parametric maximum likelihood estimators of the distribution functions of failure stresses of bundles, by using the complexly structured data. Numerical examples illustrate the behavior of the obtained estimators.