Swarm robotics focuses on designing and coordinating large groups of relatively simple robots to perform tasks in a decentralised and collective manner. The swarm provides a resilient and flexible solution for many applications. However, contemporary swarm robots have a significant power problem in that secondary (i.e. rechargeable) batteries are slow to charge and offer lifetimes of only a few years, increasing maintenance costs and pollution due to battery replacement. We imagine a different future, wherein battery-free robots powered by supercapacitors can be recharged in seconds, offer long-life autonomous operation and can rapidly pass charge between one another using trophallaxis. In pursuit of this vision, we contribute the CapBot, a battery-free swarm robot equipped with Mecanum wheels, a Cortex M4F application processor and Bluetooth Low Energy networking. The CapBot fully recharges in 16 s, offers 51 min of autonomous operation at top speed, and can transfer up to 50 % of its available charge to a peer via trophallaxis in under 20 s. The CapBot is fully open source and all software and hardware source is available online.

Simultaneously controlling multiple robot swarms is challenging for a single human operator. When involving multiple operators, however, they can each focus on controlling a specific robot swarm, which helps distribute the cognitive workload. They could also exchange some robots with each other in response to the requirements of the tasks they discover. This paper investigates the ability of multiple operators to dynamically share the control of robot swarms and the effects of different communication types on performance and human factors. A total of 52 participants completed an experiment in which they were randomly paired to form a team. In a$2\times 2$mixed factorial study, participants were split into two groups by communication type (direct vs. indirect). Both groups experienced different robot-sharing conditions (robot-sharing vs. no-robot-sharing). Results show that although the ability to share robots did not necessarily increase task scores, it allowed the operators to switch between working independently and collaboratively, reduced the total energy consumed by the swarm, and was considered useful by the participants.

This paper proposes a novel aggregation strategy for a network of mobile wheeled robots with constrained dynamics. The strategy assumes a centralized control architecture, which collects all the robot positions and generates the control signals sent to the robots in the network. To do this a control Lyapunov function (CLF) based approach is designed relying on a switched formulation of the robot models. Such a formulation is in fact made possible by constraining the robot motion only to rotation and roto-translation in the plane. Moreover, a collision avoidance objective is taken into account in the design of the CLF. The approach is analyzed, and simulations as well as experiments with six robots show its effectiveness and practical applicability.

Involving human operators to support swarms of robots can be beneficial to address increasingly complex scenarios. However, the shared control between multiple operators remains a challenge, especially where communication between the operators is not available. This paper studies the problem of forming a dynamic chain of robots connecting two operators moving within an environment. The robot chain enables operators to share information and robots among themselves. Based on supervisory control theory, we propose a distributed solution which formally guarantees that the deployed robot controllers match the modeled specifications. We validate the controllers through simulations with groups of up to 40 mobile robots in an environment with obstacles, demonstrating the feasibility of the approach.

We propose a novel concept for modular robots, termed modular fluidic propulsion (MFP), which promises to combine effective propulsion, a large reconfiguration space, and a scalable design. MFP robots are modular fluid networks. To propel, they route fluid through themselves. In this article, both hydraulic and pneumatic implementations are considered. The robots move towards a goal by way of a decentralized controller that runs independently on each module face, uses two bits of sensory information and requires neither run-time memory, nor communication. We prove that 2-D MFP robots reach the goal when of orthogonally convex shape, or reach a morphology-dependent distance from it when of arbitrary shape. We present a 2-D hydraulic MFP prototype and show, experimentally, that it succeeds in reaching the goal in at least 90% of trials, and that 71% less energy is expended when modules can communicate. Moreover, in simulations with 3-D hydraulic MFP robots, the decentralized controller performs almost as well as a state-of-the-art and centralized controller. Given the simplicity of the hardware requirements, the MFP concept could pave the way for modular robots to be used at sub-centimeter-scale, where effective modular propulsion systems have not been demonstrated.

Supervisory Control Theory (SCT) provides a formal framework for controlling discrete event systems. It has recently been used to generate correct-by-construction controllers for swarm robotics systems. Current SCT frameworks are limited, as they support only (private) events that are observable within the same robot. In this paper, we propose an extended SCT framework that incorporates (public) events that are shared among robots. The extended framework allows to model formally the interactions among the robots. It is evaluated using a case study, where a group of mobile robots need to synchronise their movements in space and time-a requirement that is specified at the formal level. We validate our approach through experiments with groups of e-puck robots.

Searching for a lost teammate is an important task for multirobot systems. We present a variant of rapidly-expanding random trees (RRT) for generating search paths based on a probabilistic belief of the target teammate's position. The belief is updated using a hidden Markov model built from knowledge of the target's planned or historic behavior. For any candidate search path, this belief is used to compute a discounted reward which is a weighted sum of the connection probability at each time step. The RRT search algorithm uses randomly sampled locations to generate candidate vertices and adds candidate vertices to a planning tree based on bounds on the discounted reward. Candidate vertices are along the shortest path from an existing vertex to the sampled location, biasing the search based on the topology of the environment. This method produces high quality search paths which are not constrained to a grid and can be computed fast enough to be used in real time. Compared with two other strategies, it found the target significantly faster in the most difficult 60% of situations and was similar in the easier 40% of situations.

We study the problem of controlling a swarm of anonymous, mobile robots to cooperatively cover an unknown two-dimensional space. The novelty of our proposed solution is that it is applicable to extremely simple robots that lack run-time computation or storage. The solution requires only a single bit of information per robot-whether or not another robot is present in its line of sight. Computer simulations show that our deterministic controller, which was obtained through off-line optimization, achieves around 71-76% coverage in a test scenario with no robot redundancy, which corresponds to a 26-39% reduction of the area that is not covered, when compared to an optimized random walk. A moderately lower level of performance was observed in 20 experimental trials with 25 physical e-puck robots. Moreover, we demonstrate that the same controller can be used in environments of different dimensions and even to navigate a maze. The controller provides a baseline against which one can quantify the performance improvements that more advanced and expensive techniques may offer. Moreover, due to its simplicity, it could potentially be implemented on swarms of sub-millimeter-sized robots. This would pave the way for new applications in micro-medicine.

Multirobot coverage is the problem of planning paths for several identical robots such that the combined regions traced out by the robots completely cover their environment. We consider the problem of multirobot coverage with the objective of minimizing the mission time, which depends on the number of turns taken by the robots. To solve this problem, we first partition the environment into ranks which are long thin rectangles the width of the robot's coverage tool. Our novel partitioning heuristic produces a set of ranks which minimizes the number of turns. Next, we solve a variant of the multiple travelling salesperson problem (m-TSP) on the set of ranks to minimize the robots' mission time. The resulting coverage plan is guaranteed to cover the entire environment. We present coverage plans for a robotic vacuum using real maps of 25 indoor environments and compare the solutions to paths planned without the objective of minimizing turns. Turn minimization reduced the number of turns by 6.7% and coverage time by 3.8% on average for teams of 1-5 robots.

Modular reconfigurable robots are touted for their flexibility, as their bodies can assume a wide range of shapes. A particular challenge is to make them move efficiently in 3D without compromising the scalability of the system. This paper proposes decentralized and fully reactive controllers for pose control of 3D modular reconfigurable robots. The robots operate in liquid environments, and move by routing fluid through themselves. Each module uses only two bits of sensory information per face. Additionally, the modules can use up to five bits of information that are exchanged via shared power lines. We prove that robots of convex shape are guaranteed to reach a goal object with a preferred orientation. Using computer simulations of Modular Hydraulic Propulsion robots, all controllers are assessed for different environments, system sizes and noise, and their performances compared against a centralized controller. Given the simplicity of the solutions, modules could be realized at scales below a millimeter-cube, where robots of high spatial resolution could perform accurate movements in 3D liquid environments.

As communication is important for cooperation, teams of mobile robots need a way to re-establish a wireless connection if they get separated. We develop a method for mobile robots to maintain a belief of each other's positions using locally available information. They can use their belief to plan paths with high probabilities of reconnection. This approach also works for subteams cooperatively searching for a robot or group of robots that they would like to reconnect with. The problem is formulated as a constrained optimization problem which is solved using a branch-and-bound approach. We present simulation results showing the effectiveness of this strategy at reconnecting teams of up to five robots and compare the results to two other strategies.

Recently, a new class of machine learning algorithms has emerged, where models and discriminators are generated in a competitive setting. The most prominent example is Generative Adversarial Networks (GANs). In this paper we examine how these algorithms relate to the Turing test, and derive what - from a Turing perspective - can be considered their defining features. Based on these features, we outline directions for generalizing GANs - resulting in the family of algorithms referred to as Turing Learning. One such direction is to allow the discriminators to interact with the processes from which the data samples are obtained, making them "interrogators", as in the Turing test. We validate this idea using two case studies. In the first case study, a computer infers the behavior of an agent while controlling its environment. In the second case study, a robot infers its own sensor configuration while controlling its movements. The results confirm that by allowing discriminators to interrogate, the accuracy of models is improved.

This paper introduces the concept of Modular Hydraulic Propulsion, in which a modular robot that operates in a fluid environment moves by routing the fluid through itself. The robot's modules represent sections of a hydraulics network. Each module can move fluid between any of its faces. The modules (network sections) can be rearranged into arbitrary topologies. We propose a decentralized motion controller, which does not require modules to communicate, compute, nor store information during run-time. We use 3-D simulations to compare the performance of this controller to that of a centralized controller with full knowledge of the task. We also detail the design and fabrication of six 2-D prototype modules, which float in a water tank. Results of systematic experiments show that the decentralized controller, despite its simplicity, reliably steers modular robots towards a light source. Modular Hydraulic Propulsion could offer new solutions to problems requiring reconfigurable systems to move precisely in 3-D, such as inspection of pipes, vascular systems or other confined spaces.

This paper presents OpenSwarm, a lightweight easy-to-use open-source operating system. To our knowledge, it is the first operating system designed for and deployed on miniature robots. OpenSwarm operates directly on a robot's microcontroller. It has a memory footprint of 1 kB RAM and 12 kB ROM. OpenSwarm enables a robot to execute multiple processes simultaneously. It provides a hybrid kernel that natively supports preemptive and cooperative scheduling, making it suitable for both computationally intensive and swiftly responsive robotics tasks. OpenSwarm provides hardware abstractions to rapidly develop and test platform-independent code. We show how OpenSwarm can be used to solve a canonical problem in swarm robotics—clustering a collection of dispersed objects. We report experiments, conducted with five e-puck mobile robots, that show that an OpenSwarm implementation performs as good as a hardware-near implementation. The primary goal of OpenSwarm is to make robots with severely constrained hardware more accessible, which may help such systems to be deployed in real-world applications.

Endotherms such as rats and mice huddle together to keep warm. The huddle is considered to be an example of a self-organising system, because complex properties of the collective group behaviour are thought to emerge spontaneously through simple interactions between individuals. Groups of rodent pups display two such emergent properties. First, huddling undergoes a 'phase transition', such that pups start to aggregate rapidly as the temperature of the environment falls below a critical temperature. Second, the huddle maintains a constant 'pup flow', where cooler pups at the periphery continually displace warmer pups at the centre. We set out to test whether these complex group behaviours can emerge spontaneously from local interactions between individuals. We designed a model using a minimal set of assumptions about how individual pups interact, by simply turning towards heat sources, and show in computer simulations that the model reproduces the first emergent property--the phase transition. However, this minimal model tends to produce an unnatural behaviour where several smaller aggregates emerge rather than one large huddle. We found that an extension of the minimal model to include heat exchange between pups allows the group to maintain one large huddle but eradicates the phase transition, whereas inclusion of an additional homeostatic term recovers the phase transition for large huddles. As an unanticipated consequence, the extended model also naturally gave rise to the second observed emergent property--a continuous pup flow. The model therefore serves as a minimal description of huddling as a self-organising system, and as an existence proof that group-level huddling dynamics emerge spontaneously through simple interactions between individuals. We derive a specific testable prediction: Increasing the capacity of the individual to generate or conserve heat will increase the range of ambient temperatures over which adaptive thermoregulatory huddling will emerge.

This paper proposes a strategy for transporting a large object to a goal using a large number of mobile robots that are significantly smaller than the object. The robots only push the object at positions where the direct line of sight to the goal is occluded by the object. This strategy is fully decentralized and requires neither explicit communication nor specific manipulation mechanisms. We prove that it can transport any convex object in a planar environment. We implement this strategy on the e-puck robotic platform and present systematic experiments with a group of 20 e-pucks transporting three objects of different shapes. The objects were successfully transported to the goal in 43 out of 45 trials. When using a mobile goal, teleoperated by a human, the object could be navigated through an environment with obstacles. We also tested the strategy in a 3-D environment using physics-based computer simulation. Due to its simplicity, the transport strategy is particularly suited for implementation on microscale robotic systems.

We propose a coevolutionary approach for learning the behavior of animals, or agents, in collective groups. The approach requires a replica that resembles the animal under investigation in terms of appearance and behavioral capabilities. It is able to identify the rules that govern the animals in an autonomous manner. A population of candidate models, to be executed on the replica, compete against a population of classifiers. The replica is mixed into the group of animals and all individuals are observed. The fitness of the classifiers depends solely on their ability to discriminate between the replica and the animals based on their motion over time. Conversely, the fitness of the models depends solely on their ability to 'trick' the classifiers into categorizing them as an animal. Our approach is metric-free in that it autonomously learns how to judge the resemblance of the models to the animals. It is shown in computer simulation that the system successfully learns the collective behaviors of aggregation and of object clustering. A quantitative analysis reveals that the evolved rules approximate those of the animals with a good precision.

The practical effectiveness of modular robotic systems depends heavily on the connection mechanisms used to join their separate entities, particularly for those systems capable of self-reconfiguration. This work presents HiGen, a high-speed genderless mechanical connection mechanism for the docking of robotic modules. HiGen connectors can join with one another in a manner that allows either side to disconnect in the event of failure. During connection electrical contacts are mated, supporting the concurrent use of local and global communication protocols, as well as power sharing techniques. Rapid actuation of the mechanism allows connections to be made and broken at a speed that is, to our knowledge, an order of magnitude faster than existing mechanical genderless approaches that feature single-sided disconnect, benefiting the reconfiguration time of modular robots. The HiGen connector is intended for future work in modular robotics, but could also see use in other areas of robotics for tool and payload attachment.

This paper proposes a method that allows a machine to infer the behavior of an animal in a fully automatic way. In principle, the machine does not need any prior information about the behavior. It is able to modify the environmental conditions and observe the animal; therefore it can learn about the animal through controlled interaction. Using a competitive coevolutionary approach, the machine concurrently evolves animats, that is, models to approximate the animal, as well as classifiers to discriminate between animal and animat. We present a proof-of-concept study conducted in computer simulation that shows the feasibility of the approach. Moreover, we show that the machine learns significantly better through interaction with the animal than through passive observation. We discuss the merits and limitations of the approach and outline potential future directions.

This paper proposes a strategy for transporting a tall, and potentially heavy, object to a goal using a large number of miniature mobile robots. The robots move the object by pushing it. The direction in which the object moves is controlled by the way in which the robots distribute themselves around its perimeter - if the robots dynamically reallocate themselves around the section of the object's perimeter that occludes their view of the goal, the object will eventually be transported to the goal. This strategy is fully distributed, and makes no use of communication between the robots. A controller based on this strategy was implemented on a swarm of 12 physical e-puck robots, and a systematic experiment with 30 randomized trials was performed. The object was successfully transported to the goal in all the trials. On average, the path traced by the object was about 8.4% longer than the shortest possible path.

We study a simple algorithm inspired by the Brazil nut effect for achieving segregation in a swarm of mobile robots. The algorithm lets each robot mimic a particle of a certain size and broadcast this information locally. The motion of each particle is controlled by three reactive behaviors: random walk, taxis, and repulsion by other particles. The segregation task requires the swarm to self-organize into a spatial arrangement in which the robots are ranked by particle size (e.g., annular structures or stripes). Using a physics-based computer simulation, we study the segregation performance of swarms of 50 mobile robots. The robots represent particles of three different sizes. We first analyze the problem of how to combine the basic behaviors so as to minimize the percentage of errors in rank. We then show that the system is very robust to noise on inter-robot perception and communication. For a noise level of 50%, the mean percentage of errors in rank is 1%. Moreover, we investigate a simplified version of the control algorithm, which does not rely on communication. Finally, we show that the mean percentage of errors in rank decreases exponentially as the particles' size ratio increases. As the error is bounded, one can achieve 100% error-free segregation. The reduction in error, however, comes at the expense of an increase in the required sensing/communication range.

Swarm robotics draws inspiration from decentralized self-organizing biological systems in general and from the collective behavior of social insects in particular. In social insect colonies, many tasks are performed by higher order group or team entities, whose task-solving capacities transcend those of the individual participants. In this paper, we investigate the emergence of such higher order entities. We report on an experimental study in which a team of physical robots performs a foraging task. The robots are "identical" in hardware and control. They make little use of memory and take actions purely on the basis of local information. Our study advances the current state of the art in swarm robotics with respect to the number of real-world robots engaging in teamwork (up to 12 robots in the most challenging experiment). To the best of our knowledge, in this paper we present the first self-organized system of robots that displays a dynamical hierarchy of teamwork (with cooperation also occurring among higher order entities). Our study shows that teamwork requires neither individual recognition nor differences between individuals. This result might also contribute to the ongoing debate on the role of these characteristics in the division of labor in social insects.

In this paper, we review half a century of research on the design of systems displaying (physical) self-assembly of macroscopic components. We report on the experience gained in the design of 21 such systems, exhibiting components ranging from passive mechanical parts to mobile robots. We present a taxonomy of the systems and discuss design principles and functions. Finally, we summarize the main achievements and indicate potential directions for future research.

Mobile robots are said to be capable of self- assembly when they can autonomously form physical connections with each other. Despite the recent proliferation of self- assembling systems, little work has been done on using self- assembly to add functional value to a robotic system, and even less on quantifying the contribution of self-assembly to system performance. In this study we demonstrate and quantify the performance benefits of i) acting as a physically larger self-assembled entity, ii) using self-assembly adaptively and iii) making the robots morphologically aware (the self-assembled robots leverage their new connected morphology in a task specific way). In our experiments, two real robots must navigate to a target over a-priori unknown terrain. In some cases the terrain can only be overcome by a self-assembled connected entity. In other cases, the robots can reach the target faster by navigating individually.

This paper addresses the cooperative transport of a heavy object by a group of mobile robots. We present a system in which group members lacking knowledge about the position of the transport target exploit physical interactions with other members of the group that have such knowledge. This is the first such system to achieve a performance superior to that of a passive caster. The system is fully decentralized and the information flow between the robots is limited to physical interactions. The robots have no knowledge about their relative positions. A comprehensive experimental study with up to six physical robots confirms the effectiveness, reliability, and robustness of the system. Finally, the system is examined in rough terrain conditions

We present a first attempt to accomplish a simple object manipulation task using the self-reconfigurable robotic system swarm-bot. The number of modular entities involved, their global shape or size and their internal structure are not pre-determined, but result from a self-organized process in which the modules autonomously grasp each other and/or an object. The modules are autonomous in perception, control, action, and power. We present quantitative results, obtained with six physical modules, that confirm the utility of self-assembling robots in a concrete task

This article illustrates the methods and results of two sets of experiments in which a group of mobile robots, calleds-bots, are required to physically connect to each other, that is, to self-assemble, to cope with environmental conditions that prevent them from carrying out their task individually. The first set of experiments is a pioneering study on the utility of self-assembling robots to address relatively complex scenarios, such as cooperative object transport. The results of our work suggest that the s-bots possess hardware characteristics which facilitate the design of control mechanisms for autonomous self-assembly. The control architecture we developed proved particularly successful in guiding the robots engaged in the cooperative transport task. However, the results also showed that some features of the robots' controllers had a disruptive effect on their performances. The second set of experiments is an attempt to enhance the adaptiveness of our multi-robot system. In particular, we aim to synthesise an integrated (i.e., not-modular) decision-making mechanism which allows the s-bot to autonomously decide whether or not environmental contingencies require self-assembly. The results show that it is possible to synthesize, by using evolutionary computation techniques, artificial neural networks that integrate both the mechanisms for sensory-motor coordination and for decision making required by the robots in the context of self-assembly.

In this paper, we discuss the self-assembling capabilities of the swarm-bot, a distributed robotics concept that lies at the intersection between collective and self-reconfigurable robotics. A swarm-bot is comprised of autonomous mobile robots called s-bots. S-bots can either act independently or self-assemble into a swarm-bot by using their grippers. We report on experiments in which we study the process that leads a group of s-bots to self-assemble. In particular, we present results of experiments in which we vary the number of s-bots (up to 16 physical robots), their starting configurations, and the properties of the terrain on which self-assembly takes place. In view of the very successful experimental results, swarm-bot qualifies as the current state of the art in autonomous self-assembly