Cultural evolution of military camouflage

Three-dimensional thermochromic liquid crystal elastomer structures with reversible shape-morphing and color-changing capabilities for soft robotics

Functional structures with reversible shape-morphing and color-changing capabilities are promising for applications including soft robotics and biomimetic camouflage devices. Despite extensive studies, there are few reports on achieving both reversible shape-switching and color-changing capabilities within one structure. Here, we report a facile and versatile strategy to realize such capabilities via spatially programmed liquid crystal elastomer (LCE) structures incorporated with thermochromic dyes. By coupling the shape-changing behavior of LCEs resulting from the nematic-to-isotropic transition of liquid crystals with the color-changing thermochromic dyes, 3D thermochromic LCE structures change their shapes and colors simultaneously, which are controlled by the nematic-isotropic transition temperature of LCEs and the critical color-changing temperature of dyes, respectively. Demonstrations, including the simulated blooming process of a resembled flower, the camouflage behavior of a "butterfly"/"chameleon" robot in response to environmental changes, and the underwater camouflage of an "octopus" robot, highlight the reliability of this strategy. Furthermore, integrating micro-ferromagnetic particles into the "octopus" thermochromic LCE robot allows it to respond to thermal-magnetic dual stimuli for "adaptive" motion and diverse biomimetic motion modes, including swimming, rolling, rotating, and crawling, accompanied by color-changing behaviors for camouflage. The reversibly reconfigurable and color-changing thermochromic LCE structures are promising for applications including soft camouflage robots and multifunctional biomimetic devices.

Biomimetic chameleon soft robot with artificial crypsis and disruptive coloration skin

Development of an artificial camouflage at a complete device level remains a vastly challenging task, especially under the aim of achieving more advanced and natural camouflage characteristics via high-resolution camouflage patterns. Our strategy is to integrate a thermochromic liquid crystal layer with the vertically stacked, patterned silver nanowire heaters in a multilayer structure to overcome the limitations of the conventional lateral pixelated scheme through the superposition of the heater-induced temperature profiles. At the same time, the weaknesses of thermochromic camouflage schemes are resolved in this study by utilizing the temperature-dependent resistance of the silver nanowire network as the process variable of the active control system. Combined with the active control system and sensing units, the complete device chameleon model successfully retrieves the local background color and matches its surface color instantaneously with natural transition characteristics to be a competent option for a next-generation artificial camouflage.

Realizing an artificial camouflage device with a high spatial resolution by adapting to the surrounding environment in real-time is a challenging task, mainly associated with device fabrication and integration with sensor and control circuits. To overcome these limitations, the authors utilize thermochromic liquid crystal ink that reacts to the feedback control system of the vertically stacked silver nanowire heater.

Fast Self-Adaptive Digital Camouflage Design Method Based on Deep Learning

Traditional digital camouflage is mainly designed for a single background and state. Its camouflage performance is appealing in the specified time and place, but with the change of place, season, and time, its camouflage performance is greatly weakened. Therefore, camouflage technology, which can change with the environment in real-time, is the inevitable development direction of the military camouflage field in the future. In this paper, a fast-self-adaptive digital camouflage design method based on deep learning is proposed for the new generation of adaptive optical camouflage. Firstly, we trained a YOLOv3 model that could identify four typical military targets with mean average precision (mAP) of 91.55%. Secondly, a pre-trained deepfillv1 model was used to design the preliminary camouflage texture. Finally, the preliminary camouflage texture was standardized by the k-means algorithm. The experimental results show that the camouflage pattern designed by our proposed method is consistent with the background in texture and semantics, and has excellent camouflage performance in optical camouflage. Meanwhile, the whole pattern generation process takes a short time, less than 0.4 s, which meets the camouflage design requirements of the near-real-time camouflage in the future.

Colour change and behavioural choice facilitate chameleon prawn camouflage against different seaweed backgrounds

Camouflage is driven by matching the visual environment, yet natural habitats are rarely uniform and comprise many backgrounds. Therefore, species often exhibit adaptive traits to maintain crypsis, including colour change and behavioural choice of substrates. However, previous work largely considered these solutions in isolation, whereas many species may use a combination of behaviour and appearance to facilitate concealment. Here we show that green and red chameleon prawns (Hippolyte varians) closely resemble their associated seaweed substrates to the vision of predatory fish, and that they can change colour to effectively match new backgrounds. Prawns also select colour-matching substrates when offered a choice. However, colour change occurs over weeks, consistent with seasonal changes in algal cover, whereas behavioural choice of matching substrates occurs in the short-term, facilitating matches within heterogeneous environments. We demonstrate how colour change and behaviour combine to facilitate camouflage against different substrates in environments varying spatially and temporally.

Optimizing colour for camouflage and visibility using deep learning: the effects of the environment and the observer's visual system

Avoiding detection can provide significant survival advantages for prey, predators, or the military; conversely, maximizing visibility would be useful for signalling. One simple determinant of detectability is an animal's colour relative to its environment. But identifying the optimal colour to minimize (or maximize) detectability in a given natural environment is complex, partly because of the nature of the perceptual space. Here for the first time, using image processing techniques to embed targets into realistic environments together with psychophysics to estimate detectability and deep neural networks to interpolate between sampled colours, we propose a method to identify the optimal colour that either minimizes or maximizes visibility. We apply our approach in two natural environments (temperate forest and semi-arid desert) and show how a comparatively small number of samples can be used to predict robustly the most and least effective colours for camouflage. To illustrate how our approach can be generalized to other non-human visual systems, we also identify the optimum colours for concealment and visibility when viewed by simulated red-green colour-blind dichromats, typical for non-human mammals. Contrasting the results from these visual systems sheds light on why some predators seem, at least to humans, to have colouring that would appear detrimental to ambush hunting. We found that for simulated dichromatic observers, colour strongly affected detection time for both environments. In contrast, trichromatic observers were more effective at breaking camouflage.

Animal coloration research: why it matters

While basic research on animal coloration is the theme of this special edition, here we highlight its applied significance for industry, innovation and society. Both the nanophotonic structures producing stunning optical effects and the colour perception mechanisms in animals are extremely diverse, having been honed over millions of years of evolution for many different purposes. Consequently, there is a wealth of opportunity for biomimetic and bioinspired applications of animal coloration research, spanning colour production, perception and function. Fundamental research on the production and perception of animal coloration is contributing to breakthroughs in the design of new materials (cosmetics, textiles, paints, optical coatings, security labels) and new technologies (cameras, sensors, optical devices, robots, biomedical implants). In addition, discoveries about the function of animal colour are influencing sport, fashion, the military and conservation. Understanding and applying knowledge of animal coloration is now a multidisciplinary exercise. Our goal here is to provide a catalyst for new ideas and collaborations between biologists studying animal coloration and researchers in other disciplines.This article is part of the themed issue 'Animal coloration: production, perception, function and application'.

The current and future state of animal coloration research

Animal colour patterns are a model system for understanding evolution because they are unusually accessible for study and experimental manipulation. This is possible because their functions are readily identifiable. In this final paper of the symposium we provide a diagram of the processes affecting colour patterns and use this to summarize their functions and put the other papers in a broad context. This allows us to identify significant 'holes' in the field that only become obvious when we see the processes affecting colour patterns, and their interactions, as a whole. We make suggestions about new directions of research that will enhance our understanding of both the evolution of colour patterns and visual signalling but also illuminate how the evolution of multiple interacting traits works.This article is part of the themed issue 'Animal coloration: production, perception, function and application'.