Swarm robotics is a study of simple robots that exhibit complex behaviour only by interacting locally with other robots and their environment. The control in swarm robotics is mainly distributed whereas centralised control is widely used in other fields of robotics. Centralised and decentralised control strategies both pose a unique set of benefits and drawbacks for the control of multi-robot systems. While decentralised systems are more scalable and resilient, they are less efficient compared to the centralised systems and they lead to excessive data transmissions to the human operators causing cognitive overload. We examine the trade-offs of each of these approaches in a human-swarm system to perform an environmental monitoring task and propose a flexible hybrid approach, which combines elements of hierarchical and decentralised systems. We find that a flexible hybrid system can outperform a centralised system (in our environmental monitoring task by 19.2% ) while reducing the number of messages sent to a human operator (here by 23.1% ). We conclude that establishing centralisation for a system is not always optimal for performance and that utilising aspects of centralised and decentralised systems can keep the swarm from hindering its performance.

Navigating autonomous robots through dense forests and rugged terrains is especially daunting when exteroceptive sensors—such as cameras and LiDAR sensors— fail under occlusions, low-light conditions, or sensor noise. We present Blind-Wayfarer, a probing-driven navigation framework inspired by maze-solving algorithms that relies primarily on a compass to robustly traverse complex, unstructured environments. In 1,000 simulated forest experiments, Blind-Wayfarer achieved a 99.7% success rate. In real-world tests in two distinct scenarios—with rover platforms of different sizes—our approach successfully escaped forest entrapments in all 20 trials. Remarkably, our framework also enabled a robot to escape a dense woodland, traveling from 45 m inside the forest to a paved pathway at its edge. These findings highlight the potential of probing-based methods for reliable navigation in challenging perception-degraded field conditions. Videos and code are available on our website https://sites.google.com/view/blind-wayfarer

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.

Off-road navigation in forest environments is a challenging problem in field robotics. Rovers are required to infer their traversability over a priori unknown and dynamically changing forest terrain using noisy onboard navigation sensors. The problem is compounded for small-sized rovers, such as that of a swarm. Their size-proportional low-viewpoint affords them a restricted view for navigation, which may be partially occluded by forest vegetation. Hand-crafted features, typically employed for terrain traversability analysis, are often brittle and may fail to discriminate obstacles in varying lighting and weather conditions. We design a low-cost navigation system tailored for small-sized forest rovers using self-learned features. The MobileNet-V1 and MobileNet-V2 models, trained following an end-to-end learning approach, are deployed to steer a mobile platform, with a human-in-the-loop, towards traversable paths while avoiding obstacles. Receiving a 128 × 96 pixel RGB image from a monocular camera as input, the algorithm running on a Raspberry Pi 4, exhibited robustness to motion blur, low lighting, shadows and high-contrast lighting conditions. It was able to successfully navigate a total of over 3 km of real-world forest terrain comprising shrubs, dense bushes, tall grass, fallen branches, fallen tree trunks, and standing trees, in over five different weather conditions and four different times of day.

One of the main tasks for autonomous robot swarms is to collectively decide on the best available option. Achieving that requires a high quality communication between the agents that may not always be available in a real world environment. In this paper we introduce the communication-constrained collective decision-making problem where some areas of the environment limit the agents' ability to communicate, either by reducing success rate or blocking the communication channels. We propose a decentralised algorithm for mapping environmental features for robot swarms as well as improving collective decision making in communication-limited environments without prior knowledge of the communication landscape. Our results show that making a collective aware of the communication environment can improve the speed of convergence in the presence of communication limitations, at least 3 times faster, without sacrificing accuracy.

Simulation of forest environments has applications from entertainment and art creation to commercial and scientific modelling. Due to the unique features and lighting in forests, a forest-specific simulator is desirable, however many current forest simulators are proprietary or highly tailored to a particular application. Here we review several areas of procedural generation and rendering specific to forest generation, and utilise this to create a generalised, open-source tool for generating and rendering interactive, realistic forest scenes. The system uses specialised L-systems to generate trees which are distributed using an ecosystem simulation algorithm. The resulting scene is rendered using a deferred rendering pipeline, a Blinn-Phong lighting model with real-time leaf transparency and post-processing lighting effects. The result is a system that achieves a balance between high natural realism and visual appeal, suitable for tasks including training computer vision algorithms for autonomous robots and visual media generation.

Rapid progress in embedded computing hardware increasingly enables on-board image processing on small robots. This development opens the path to replacing costly sensors with sophisticated computer vision techniques. A case in point is the prediction of scene depth information from a monocular camera for autonomous navigation. Motivated by the aim to develop a robot swarm suitable for sensing, monitoring, and search applications in forests, we have collected a set of RGB images and corresponding depth maps. Over 100000 RGB/depth image pairs were recorded with a custom rig from the perspective of a small ground rover moving through a forest. Taken under different weather and lighting conditions, the images include scenes with grass, bushes, standing and fallen trees, tree branches, leaves, and dirt. In addition GPS, IMU, and wheel encoder data were recorded. From the calibrated, synchronized, aligned and timestamped frames about 9700 image-depth map pairs were selected for sharpness and variety. We provide this dataset to the community to fill a need identified in our own research and hope it will accelerate progress in robots navigating the challenging forest environment. This paper describes our custom hardware and methodology to collect the data, subsequent processing and quality of the data, and how to access it.

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.

Robot swarms to date are not prepared for autonomous navigation such as path planning and obstacle detection in forest floor, unable to achieve low-cost. The development of depth sensing and embedded computing hardware paves the way for swarm of terrestrial robots. The goal of this research is to improve this situation by developing low cost vision system for small ground robots to rapidly perceive terrain. We develop two depth estimation models and evaluate their performance on Raspberry Pi 4 and Jetson Nano in terms of accuracy, runtime and model size of depth estimation models, as well as memory consumption, power draw, temperature, and cost of above two embedded on-board computers. Our research demonstrated that auto-encoder network deployed on Raspberry Pi 4 runs at a power consumption of 3.4 W, memory consumption of about 200 MB, and mean runtime of 13 ms. This can be to meet our requirement for low-cost swarm of robots. Moreover, our analysis also indicated multi-scale deep network performs better for predicting depth map from blurred RGB images caused by camera motion. This paper mainly describes depth estimation models trained on our own dataset recorded in forest, and their performance on embedded on-board computers.

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.

All known forms of life utilise information processing to maintain their complex organisation. In contrast to conventional information technology built on solid-state semiconductor devices, biological information processing is built on transformations through chemical reactions and interactions mediated by diffusion. The theoretical understanding of reaction-diffusion computing as well as prototype implementations have progressed in parallel over the past decades. We report here on a technique for studying spatially structured networks in which chemicals are compartmentalised as droplets-in-oil and laser-cut topologies impose spatial structure. Experiments with halogen displacement reactions demonstrate that the feature size achievable with laser cutting is well suited to practical diffusion time scales. Further advantages of the technique are the optical accessibility, enabling readout from bromine and iodine production, diffusion and indicator reactions, and the good chemical compatibility between the compartmentalisation medium ( oil) and the structuring medium ( PMMA), while the fast turn-around times enable rapid topology optimisation.

A storable whole-cell biosensor has been a challenge in the whole-cell biosensor research. We developed a long-term storable whole-cell biosensor using the true slime mould, Physarum polycephalum, for toxicity detection. The cell is interfaced to a microfluidic device with impedance measurement system. The oscillatory activity of the cell when exposed to various concentrations of 2,4-dinitrophenol (DNP) is investigated. It has been demonstrated that the Physarum cell can be dry-stored in the device for months and used as bionsensor after revived with rehydration. This is the first implementation of storable whole-cell biosensor for toxicity detection use, and it suggests that the development of long-term storable, and potentially portable, whole-cell biosensor for general toxicity prescreening is possible.

Swarm robotic systems can offer many advantages including robustness, flexibility and scalability. However one of the issues relating to overall swarm performance that needs to be considered is hardware variations inherent in the implementation of individual swarm robots. This variation can bring behavioral diversity within the swarm, resulting in uncontrollable swarm behaviors, low efficiency, etc. If swarm robots could be separated by behaviors, operational advantages could be obtained. In this paper we report an approach to the sorting of large robotic swarms using an approach inspired by chromatography. Hence the tedious and expensive calibration process can be avoided. The results investigate the influence of the internal control parameters, together with environmental effects on the robotic behavioral sorting. We concluded that if the robot has knowledge of previous events coupled with a specific arena pattern density will offer improved behavioral sorting.

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.

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.

Swarm robotic systems can offer advantages of robustness, flexibility and scalability, just like social insects. One of the issues that researchers are facing is the hardware variation when implementing real robotic swarms. Identical software can not guarantee identical behaviors among all robots due to hardware differences between swarm members. We propose a novel approach for sorting swarm robots according to their hardware differences. This method is based on the large number of interactions between robots and the environment. Individual robot's unique hardware circumstance determines its unique decision and reaction during each robotic controlling step, and these unique local reactions accumulate and contribute to the robot's global behavior. Accordingly by separating these hardware-triggered global behaviors, swarm robots can be sorted according to their hardware variations.

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.

Belousov-Zhabotinsky (BZ) excitable chemical medium exhibits a rich variety of spatial patterns of excitation. In a sub-excitable light-sensitive chemical medium, an asymmetric disturbing or excitation, causes formation of localized travelling excitation wave-fragments. When two or more wave-fragments collide, they annihilate or merge into new wave-fragment. The wave-fragments can be thought as elementary units of information and their interaction sites as a logical gates. Size and life span of a wave-fragment depends on a level of medium’s illumination. By encapsulating BZ reaction into a lipid vesicle, we can manipulate the BZ-derived logical gates. By interpreting wave-fragments as values of Boolean variables, we design a collision-based polymorphic logical gate in BZ vesicles. The gate implements operation xnor for low illumination, and it acts as nor gate for high illumination.