An embarrassingly simple approach for visual navigation of forest environments.

Navigation in forest environments is a challenging and open problem in the area of field robotics. Rovers in forest environments are required to infer the traversability of a priori unknown terrains, comprising a number of different types of compliant and rigid obstacles, under varying lighting and weather conditions. The challenges are further compounded for inexpensive small-sized (portable) rovers. While such rovers may be useful for collaboratively monitoring large tracts of forests as a swarm, with low environmental impact, their small-size affords them only a low viewpoint of their proximal terrain. Moreover, their limited view may frequently be partially occluded by compliant obstacles in close proximity such as shrubs and tall grass. Perhaps, consequently, most studies on off-road navigation typically use large-sized rovers equipped with expensive exteroceptive navigation sensors. We design a low-cost navigation system tailored for small-sized forest rovers. For navigation, a light-weight convolution neural network is used to predict depth images from RGB input images from a low-viewpoint monocular camera. Subsequently, a simple coarse-grained navigation algorithm aggregates the predicted depth information to steer our mobile platform towards open traversable areas in the forest while avoiding obstacles. In this study, the steering commands output from our navigation algorithm direct an operator pushing the mobile platform. Our navigation algorithm has been extensively tested in high-fidelity forest simulations and in field trials. Using no more than a 16 × 16 pixel depth prediction image from a 32 × 32 pixel RGB image, our algorithm running on a Raspberry Pi was able to successfully navigate a total of over 750 m of real-world forest terrain comprising shrubs, dense bushes, tall grass, fallen branches, fallen tree trunks, small ditches and mounds, and standing trees, under five different weather conditions and four different times of day. Furthermore, our algorithm exhibits robustness to changes in the mobile platform's camera pitch angle, motion blur, low lighting at dusk, and high-contrast lighting conditions.

Sparse Robot Swarms: Moving Swarms to Real-World Applications.

Robot swarms are groups of robots that each act autonomously based on only local perception and coordination with neighboring robots. While current swarm implementations can be large in size (e.g., 1,000 robots), they are typically constrained to working in highly controlled indoor environments. Moreover, a common property of swarms is the underlying assumption that the robots act in close proximity of each other (e.g., 10 body lengths apart), and typically employ uninterrupted, situated, close-range communication for coordination. Many real world applications, including environmental monitoring and precision agriculture, however, require scalable groups of robots to act jointly over large distances (e.g., 1,000 body lengths), rendering the use of dense swarms impractical. Using a dense swarm for such applications would be invasive to the environment and unrealistic in terms of mission deployment, maintenance and post-mission recovery. To address this problem, we propose the sparse swarm concept, and illustrate its use in the context of four application scenarios. For one scenario, which requires a group of rovers to traverse, and monitor, a forest environment, we identify the challenges involved at all levels in developing a sparse swarm-from the hardware platform to communication-constrained coordination algorithms-and discuss potential solutions. We outline open questions of theoretical and practical nature, which we hope will bring the concept of sparse swarms to fruition.

Towards Functional Droplet Architectures: a Belousov-Zhabotinsky Medium for Networks.

The confluence of droplet-compartmentalised chemical systems and architectures composed of interacting droplets points towards a novel technology mimicking core features of the cellular architecture that dominates biology. A key challenge to achieve such a droplet technology is long-term stability in conjunction with interdroplet communication. Here, we probed the parameter space of the Belousov-Zhabotinsky (BZ) medium, an extensively studied model for non-equilibrium chemical reactions, pipetted as 2.5 mm droplets in hexadecane oil. The presence of asolectin lipids enabled the formation of arrays of contacted BZ droplets, of which the wave patterns were characterised over time. We utilised laser-cut acrylic templates with over 40 linear oil-filled slots in which arrays are formed by pipetting droplets of the desired BZ composition, enabling parallel experiments and automated image analysis. Using variations of conventional malonic acid BZ medium, wave propagation over droplet-droplet interfaces was not observed. However, a BZ medium containing both malonic acid and 1,4-cyclohexanedione was found to enable inter-droplet wave propagation. We anticipate that the chemical excitation properties of this mixed-substrate BZ medium, in combination with the droplet stability of the networks demonstrated here for nearly 400 droplets in a template-defined topology, will facilitate the development of scalable functional droplet networks.

Autonomous droplet architectures.

The quintessential living element of all organisms is the cell-a fluid-filled compartment enclosed, but not isolated, by a layer of amphiphilic molecules that self-assemble at its boundary. Cells of different composition can aggregate and communicate through the exchange of molecules across their boundaries. The astounding success of this architecture is readily apparent throughout the biological world. Inspired by the versatility of nature's architecture, we investigate aggregates of membrane-enclosed droplets as a design concept for robotics. This will require droplets capable of sensing, information processing, and actuation. It will also require the integration of functionally specialized droplets into an interconnected functional unit. Based on results from the literature and from our own laboratory, we argue the viability of this approach. Sensing and information processing in droplets have been the subject of several recent studies, on which we draw. Integrating droplets into coherently acting units and the aspect of controlled actuation for locomotion have received less attention. This article describes experiments that address both of these challenges. Using lipid-coated droplets of Belousov-Zhabotinsky reaction medium in oil, we show here that such droplets can be integrated and that chemically driven mechanical motion can be achieved.

Excitability Modulation of Oscillating Media in 3D-Printed Structures.

Excitation and oscillation are central to living systems. For excitable systems, which can be brought into oscillation by an external stimulus, the excitation threshold is a crucial parameter. This is evident for neurons, which only generate an action potential when exposed to a sufficiently high concentration of excitatory neurotransmitters, which may only be achieved when multiple presynaptic axons deliver their action potential simultaneously to the synaptic cleft. Dynamic systems composed of relatively simple chemicals are of interest because they can serve as a model for physiological processes or can be exploited to implement chemical computing. With these applications in mind, we have studied the properties of the oscillatory Belousov-Zhabotinsky (BZ) reaction in 3D-printed reaction vessels with open channels of different dimensions. It is demonstrated that the channel geometry can be used to modulate the excitability of the BZ medium, switching a continuously oscillating medium to an excitable medium. Because large networks of channel-connected reaction wells of different depth can easily be fabricated by 3D printing, local excitability modulation could be built into the structure of the reaction vessel itself, opening the way to more extensive experimentation with networks of chemical oscillators.

Interdroplet bilayer arrays in millifluidic droplet traps from 3D-printed moulds.

In droplet microfluidics, aqueous droplets are typically separated by an oil phase to ensure containment of molecules in individual droplets of nano-to-picoliter volume. An interesting variation of this method involves bringing two phospholipid-coated droplets into contact to form a lipid bilayer in-between the droplets. These interdroplet bilayers, created by manual pipetting of microliter droplets, have proved advantageous for the study of membrane transport phenomena, including ion channel electrophysiology. In this study, we adapted the droplet microfluidics methodology to achieve automated formation of interdroplet lipid bilayer arrays. We developed a 'millifluidic' chip for microliter droplet generation and droplet packing, which is cast from a 3D-printed mould. Droplets of 0.7-6.0 µL volume were packed as homogeneous or heterogeneous linear arrays of 2-9 droplets that were stable for at least six hours. The interdroplet bilayers had an area of up to 0.56 mm(2), or an equivalent diameter of up to 850 µm, as determined from capacitance measurements. We observed osmotic water transfer over the bilayers as well as sequential bilayer lysis by the pore-forming toxin melittin. These millifluidic interdroplet bilayer arrays combine the ease of electrical and optical access of manually pipetted microdroplets with the automation and reproducibility of microfluidic technologies. Moreover, the 3D-printing based fabrication strategy enables the rapid implementation of alternative channel geometries, e.g. branched arrays, with a design-to-device time of just 24-48 hours.

In-silico design of computational nucleic acids for molecular information processing.

Within recent years nucleic acids have become a focus of interest for prototype implementations of molecular computing concepts. During the same period the importance of ribonucleic acids as components of the regulatory networks within living cells has increasingly been revealed. Molecular computers are attractive due to their ability to function within a biological system; an application area extraneous to the present information technology paradigm. The existence of natural information processing architectures (predominately exemplified by protein) demonstrates that computing based on physical substrates that are radically different from silicon is feasible. Two key principles underlie molecular level information processing in organisms: conformational dynamics of macromolecules and self-assembly of macromolecules. Nucleic acids support both principles, and moreover computational design of these molecules is practicable. This study demonstrates the simplicity with which one can construct a set of nucleic acid computing units using a new computational protocol. With the new protocol, diverse classes of nucleic acids imitating the complete set of boolean logical operators were constructed. These nucleic acid classes display favourable thermodynamic properties and are significantly similar to the approximation of successful candidates implemented in the laboratory. This new protocol would enable the construction of a network of interconnecting nucleic acids (as a circuit) for molecular information processing.

Towards molecular computing: co-development of microfluidic devices and chemical reaction media.

Microfluidics provides a powerful technology for both the production of molecular computing components and for the implementation of molecular computing architectures. The potential commercial applications of microfluidics drive rapid progress in this field-but at the same time focus interest on materials that are compatible with physiological aqueous conditions. For engineering applications that consider a broader range of physico-chemical conditions the narrow set of established materials for microfluidics can be a challenge. As a consequence of the large surface to volume ratio inherent in microfluidic technology the material of the device can greatly affect the chemistry in the channels of the device. In practice it is necessary to co-develop the chemical medium to be used in the device together with the microfluidic devices. We describe this process for a molecular computing architecture that makes use of excitable lipid-coated droplets of Belousov-Zhabotinsky reaction medium as its active processing components. We identify fluoropolymers with low melting temperature as a suitable substrate for microfluidics to be used in conjunction with Belousov-Zhabotinsky droplets in decane.

On architectures of circuits implemented in simulated Belousov-Zhabotinsky droplets.

When lipid vesicles filled with Belousov-Zhabotinsky (BZ) excitable chemical medium are packed in tight assembles, waves of excitation may travel between the vesicles. When several waves meet in a vesicle some fragments may deflect, others can annihilate or continue their travel undisturbed. By interpreting waves as Boolean values we can construct logical gates and assemble them in large circuits. In numerical modelling we show two architectures of one-bit half-adders implemented in BZ-vesicles.

Design of interacting multi-stable nucleic acids for molecular information processing.

Despite an exponential increase in computing power over the past decades, present information technology falls far short of expectations in areas such as cognitive systems and micro robotics. Organisms demonstrate that it is possible to implement information processing in a radically different way from what we have available in present technology, and that there are clear advantages from the perspective of power consumption, integration density, and real-time processing of ambiguous data. Accordingly, the question whether the current silicon substrate and associated computing paradigm is the most suitable approach to all types of computation has come to the fore. Macromolecular materials, so successfully employed by nature, possess uniquely promising properties as an alternate substrate for information processing. The two key features of macromolecules are their conformational dynamics and their self-assembly capabilities. The purposeful design of macromolecules capable of exploiting these features has proven to be a challenge, however, for some groups of molecules it is increasingly practicable. We here introduce an algorithm capable of designing groups self-assembling of nucleic acid molecules with multiple conformational states. Evaluation using natural and artificially designed nucleic acid molecules favours this algorithm significantly, as compared to the probabilistic approach. Furthermore, the thermodynamic properties of the generated candidates are within the same approximation as the customised trans-acting switching molecules reported in the laboratory.

On-chip electrical impedance tomography for imaging biological cells.

Electrical impedance tomography is an imaging technology that spatially characterizes the electrical properties of an object. We present a miniaturized electrical impedance tomography system that can image the electrical conductivity distribution within a two-dimensional cell culture. A chip containing a circular 16-electrode array was fabricated using printed circuit board developing technology and used to inject current and to measure spatial voltage across the object. The signal stimulation and voltage data acquisition were performed using an impedance analyzer, operating in four-electrode mode. An open source software, EIDORS was used for image reconstruction. Finite element modelling was used to simulate the image reconstruction process by imaging two ellipsoidal phantoms in the circular 16-electrode array. The effect of the regularization parameter in the reconstruction algorithm and the influence from noise on the fidelity of the images has been numerically analyzed. Experimentally, we show reconstructed images of a multi-nuclear single cellular organism, Physarum Polycephalum, demonstrating the first step towards impedance imaging of single cells in culture. Our system provides a non-invasive lab-on-a-chip technology for spatially mapping the electrical properties of single cells, which would be significant and useful for diagnostic and clinical applications.

Adaptation in multisensory neurons: impact on cross-modal enhancement.

Adaptation is a ubiquitous property of sensory neurons. Multisensory neurons, receiving convergent input from different sensory modalities, also likely exhibit adaptation. The responses of multisensory superior colliculus neurons have been extensively studied, but the impact of adaptation on these responses has not been examined. Multisensory neurons in the superior colliculus exhibit cross-modal enhancement, an often non-linear and non-additive increase in response when a stimulus in one modality is paired with a stimulus in a different modality. We examine the possible impact of adaptation on cross-modal enhancement within the framework of a simple model of adaptation for a neuron employing a saturating, logistic response function. We consider how adaptation to an input's mean and standard deviation affects cross-modal enhancement, and also how the statistical correlations between two different modalities influence cross-modal enhancement. We determine the optimal bimodal stimuli to present a bimodal neuron that evoke the largest changes in cross-modal enhancement under adaptation to input statistics. The model requires separate gains for each modality, unless the statistics specific to each modality have been standardised by prior adaptation in earlier, unisensory neurons. The model also predicts that increasing the correlation coefficient between two modalities reduces a multisensory neuron's overall gain.

An invariance principle for maintaining the operating point of a neuron.

Sensory neurons adapt to changes in the natural statistics of their environments through processes such as gain control and firing threshold adjustment. It has been argued that neurons early in sensory pathways adapt according to information-theoretic criteria, perhaps maximising their coding efficiency or information rate. Here, we draw a distinction between how a neuron's preferred operating point is determined and how its preferred operating point is maintained through adaptation. We propose that a neuron's preferred operating point can be characterised by the probability density function (PDF) of its output spike rate, and that adaptation maintains an invariant output PDF, regardless of how this output PDF is initially set. Considering a sigmoidal transfer function for simplicity, we derive simple adaptation rules for a neuron with one sensory input that permit adaptation to the lower-order statistics of the input, independent of how the preferred operating point of the neuron is set. Thus, if the preferred operating point is, in fact, set according to information-theoretic criteria, then these rules nonetheless maintain a neuron at that point. Our approach generalises from the unimodal case to the multimodal case, for a neuron with inputs from distinct sensory channels, and we briefly consider this case too.

Protein folding and the robustness of cells.

The intricate intracellular infrastructure of all known life forms is based on proteins. The folded shape of a protein determines both the protein's function and the set of molecules it will bind to. This tight coupling between a protein's function and its interconnections in the molecular interaction network has consequences for the molecular course of evolution. It is also counter to human engineering approaches. Here we report on a simulation study investigating the impact of random errors in an abstract metabolic network of 500 enzymes. Tight coupling between function and interconnectivity of nodes is compared to the case where these two properties are independent. Our results show that the model system under consideration is more robust if function and interconnection are intertwined. These findings are discussed in the context of nanosystems engineering.

Robot control with biological cells.

At present there exists a large gap in size, performance, adaptability and robustness between natural and artificial information processors for performing coherent perception-action tasks under real-time constraints. Even the simplest organisms have an enviable capability of coping with an unknown dynamic environment. Robots, in contrast, are still clumsy if confronted with such complexity. This paper presents a bio-hybrid architecture developed for exploring an alternate approach to the control of autonomous robots. Circuits prepared from amoeboid plasmodia of the slime mold Physarum polycephalum are interfaced with an omnidirectional hexapod robot. Sensory signals from the macro-physical environment of the robot are transduced to cellular scale and processed using the unique micro-physical features of intracellular information processing. Conversely, the response form the cellular computation is amplified to yield a macroscopic output action in the environment mediated through the robot's actuators.