Modification of Adhesion and Friction by Surface Structuring

Enhanced and selective adhesion, and controlled friction between contact surfaces are highly desirable mechanical properties for high-level functional materials. There are many instances in nature where such properties have been obtained by design of near-surface architecture. Inspired by many highly functional biological systems, we have explored bio-mimetic materials with different surface patterning, with the goal of designing surfaces that have unique combinations of contact mechanical properties. In the studies presented here, we show how: (a) highly selective adhesion can be achieved by complementarity of patterned charge and shape, and (b) how friction can be modulated by spatial variation in stiffness, and how structured surfaces interact with surface roughness.We consider how adhesion selectivity can be accomplished by complementarity of shape and inter-surface forces. We have studied an example each of charge and shape complementarity for selective adhesion between extended surfaces. First, we studied theoretically how surfaces patterned with stripes of charge interact with each other, and exhibit strong selectivity on rigid surfaces. However, deformability of the surfaces plays a crucial role in modulating adhesion by accommodating mismatches. To achieve shape complementarity, we designed and fabricated patterned elastomeric surfaces with lines of channels and complementary ridges with dimensions at the micrometer scale. We show that such surfaces have highly enhanced effective adhesion for shape complementary pairs and low adhesion between surfaces with a shape mismatch. We find that the pillar/channel combinations form defects to accommodate interfacial misalignment. These defects are interfacial dislocations. Adhesion between complementary surfaces is enhanced by crack trapping and friction, and attenuated due to the energy released by dislocation structures. In addition to enhanced adhesion, we studied the deliberate control of friction through near-surface micro-structures. Friction measurements on elastomeric surfaces patterned with periodic variation in stiffness show that it undergoes an auto-roughening transition under shear and this process can strongly attenuate overall sliding friction. Friction reduction is due to reduction of real contact area, as the initially full contact breaks up into partial contact at the interface. Finite element analysis demonstrates how auto-roughening depends on the modulus mismatch, frictional stress and normal displacement.A surface with random roughness is used to study sliding friction against micro-channel structures under fixed normal force. In contrast to a smooth surface, against which structured surfaces all have highly reduced sliding friction, the roughened surface can exhibit significantly larger frictional force on a structured surface. The enhancement of sliding friction is governed by channel depth, spacing and applied normal force

Robust Correlation Tracking for UAV with Feature Integration and Response Map Enhancement

Recently, correlation filter (CF)-based tracking algorithms have attained extensive interest in the field of unmanned aerial vehicle (UAV) tracking. Nonetheless, existing trackers still struggle with selecting suitable features and alleviating the model drift issue for online UAV tracking. In this paper, a robust CF-based tracker with feature integration and response map enhancement is proposed. Concretely, we develop a novel feature integration method that comprehensively describes the target by leveraging auxiliary gradient information extracted from the binary representation. Subsequently, the integrated features are utilized to learn a background-aware correlation filter (BACF) for generating a response map that implies the target location. To mitigate the risk of model drift, we introduce saliency awareness in the BACF framework and further propose an adaptive response fusion strategy to enhance the discriminating capability of the response map. Moreover, a dynamic model update mechanism is designed to prevent filter contamination and maintain tracking stability. Experiments on three public benchmarks verify that the proposed tracker outperforms several state-of-the-art algorithms and achieves a real-time tracking speed, which can be applied in UAV tracking scenarios efficiently

A Study of AI Population Dynamics with Million-agent Reinforcement Learning

We conduct an empirical study on discovering the ordered collective dynamics obtained by a population of intelligence agents, driven by million-agent reinforcement learning. Our intention is to put intelligent agents into a simulated natural context and verify if the principles developed in the real world could also be used in understanding an artificially-created intelligent population. To achieve this, we simulate a large-scale predator-prey world, where the laws of the world are designed by only the findings or logical equivalence that have been discovered in nature. We endow the agents with the intelligence based on deep reinforcement learning (DRL). In order to scale the population size up to millions agents, a large-scale DRL training platform with redesigned experience buffer is proposed. Our results show that the population dynamics of AI agents, driven only by each agent's individual self-interest, reveals an ordered pattern that is similar to the Lotka-Volterra model studied in population biology. We further discover the emergent behaviors of collective adaptations in studying how the agents' grouping behaviors will change with the environmental resources. Both of the two findings could be explained by the self-organization theory in nature.Comment: Full version of the paper presented at AAMAS 2018 (International Conference on Autonomous Agents and Multiagent Systems