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Programming for swarm robots is challenging due to platform diversity and the gap between individual and swarm behaviors. To tackle this challenge, we propose a component-based software synthesis method from a high-level specification. To support heterogeneous robots and maximize code reuse, we adopt a component-based approach that classifies software components into three categories: 1) robot; 2) algorithm; and 3) consensus. We generate a task graph model for an individual robot from a high-level specification and use a software synthesizer to generate the target code from the task graph model. Through a proof-of-concept implementation with a group searching application, the viability of the proposed technique is demonstrated. [ABSTRACT FROM AUTHOR]

In this paper, a decentralized consensus algorithm for a robotic swarm is presented, which enable agents to escape collisions and avoid obstacles collectively. To achieve consensus, we have used neighboring clusters and extreme function for data dissemination and consensus in agents to avoid obstacles respectively. The main challenge is to make fast and accurate collective decision by improving data propagation in presence of Byzantine agents. To improve data dissemination in local interactions of agents in decentralized fashion, an iterative rumor-based data propagation model is proposed. Due to presence of Byzantine robots, the LCP and the W-MSR algorithm cannot achieve consensus for obstacle avoidance in artificial swarms. We establish the Expectation-based Extreme Value (EEV) algorithm using the local expectation and the extreme function to solve these problems. The experiments conducted in simulations demonstrate that the rumor spreading method has better results than the Peer-to-Peer method in randomly connected swarm signaling network (SSN) with complex environmental circumstance, the EEV algorithm is more effective than the LCP and the W-MSR for the swarm navigation and consensus in agent on large obstacles / environmental features. Furthermore, in presence of malicious / hacked agents in a swarm it is very difficult to reach consensus. The result show that proposed algorithm can handle the Byzantine agents effectively.

In order to solve the problem of data security and sharing efficiency caused by the complex and uncertain environment faced by swarm robotic systems in emergency scenarios, this paper designs a data security consensus algorithm based on blockchain technology. Aiming at the problems of high communication overhead, long consensus delay and low throughput when the traditional Practical Byzantine Fault Tolerance (PBFT) algorithm is applied to this scenario, a grouped practical Byzantine Fault Tolerance consensus algorithm based on K-means clustering is proposed. Firstly, the K-means clustering algorithm is used to group robotic nodes according to the location distribution of robotics in the emergency scenario. Secondly, a reputation mechanism is designed to dynamically evaluate the behaviour of robotic nodes in each group during the consensus process. Each consensus node is divided into master and slave chains according to its reputation score. The consensus task is firstly decomposed, and slave chains participate in the consensus in parallel. Finally, the master chain completes the global consensus, so as to reduce the communication times between nodes. The experimental results show that compared with the traditional PBFT, SG-PBFT and P-PBFT consensus algorithm, the proposed consensus algorithm effectively improves the system throughput, reduces the delay, reduces the communication overhead, and has a higher success rate of consensus.

The Adaptive Collision Avoidance Algorithm Based on the Estimated Collision Time (ACACT) is proposed in this paper, representing a novel approach designed for effective and efficient collision avoidance and path planning in highly dynamic environments, notably those with swarm Unmanned Aerial Vehicles (UAVs). The fundamental challenge in swarm UAV operations revolves around dynamic collision avoidance and nimble path planning. Addressing this, the ACACT algorithm exhibits the capability of predicting imminent collisions by estimating their likely occurrence times and then adeptly adjusting the UAVs’ trajectories in real-time. A significant facet of the algorithm is the employment of adaptive target velocity, updated in accordance with the predicted collision timelines. This ensures not only that UAVs can sidestep potential collisions but also that they can pursue more direct and efficient routes in comparison to conventional methodologies. Highlighting its superiority over existing techniques, the ACACT algorithm successfully resolves some long-standing issues linked with the Artificial Potential Field (APF) method, especially concerning unreachability and oscillation. This is accomplished by integrating a strategic contingency plan coupled with enhanced obstacle navigation, particularly in proximity to target locations. For a comprehensive evaluation of the algorithm’s prowess in collision avoidance and path planning, a novel metric named the Path Traveling Time Ratio (PTTR) is introduced. PTTR assesses both the traveling time taken for a vehicle to reach its target position and the duration it spends within collision-prone zones. This metric offers a more advanced evaluation method than merely comparing path lengths, collision counts, or traveling times. Through rigorous experimentation, it is observed that the ACACT algorithm enhances collision avoidance and path planning by an impressive margin of up to 20% compared to its traditional counterparts. Furthermore, a distinct advantage of the ACACT is its ability to uniformly tackle obstacles, irrespective of their speeds and independent of PID gain variations. It not only boosts the safety parameters but also amplifies operational efficiency, setting new benchmarks for UAVs to reach their target points with swiftness and security.

In robotic research and education, the cost in terms of money, expertise, and time required to instantiate and maintain robotic testbeds can prevent researchers and educators from including hardware based experimentation in their laboratories and classrooms. This results in robotic algorithms often being validated by low-fidelity simulation due to the complexity and computational demand required by high-fidelity simulators. Unfortunately, these simulation environments often neglect real world complexities, such as wheel slip, actuator dynamics, computation time, communication delays, and sensor noise. The Robotarium provides a solution to these problems by providing a state-of-the-art, multi-robot research facility to everyone around the world free of charge for academic and educational purposes. This paper discusses the remote usage of the testbed since its opening in 2017, details the testbeds design, and provides a brief tutorial on how to use it.

A brain-computer interface (BCI) is a system that allows a human operator to use only mental commands in controlling end effectors that interact with the world around them. Such a system consists of a measurement device to record the human user’s brain activity, which is then processed into commands that drive a system end effector. BCIs involve either invasive measurements which allow for high-complexity control but are generally infeasible, or noninvasive measurements which offer lower quality signals but are more practical to use. In general, BCI systems have not been developed that efficiently, robustly, and scalably perform high-complexity control while retaining the practicality of noninvasive measurements. Here we leverage recent results from feedback information theory to fill this gap by modeling BCIs as a communications system and deploying a human-implementable interaction algorithm for noninvasive control of a high-complexity robot swarm. We construct a scalable dictionary of robotic behaviors that can be searched simply and efficiently by a BCI user, as we demonstrate through a large-scale user study testing the feasibility of our interaction algorithm, a user test of the full BCI system on (virtual and real) robot swarms, and simulations that verify our results against theoretical models. Our results provide a proof of concept for how a large class of high-complexity effectors (even beyond robotics) can be effectively controlled by a BCI system with low-complexity and noisy inputs.

In many cases, a pack of robots holds an advantage over a single robot such when an oversized or over-weighted load is to be carried. In such cases, a single robot will not do. Nevertheless, this may not be an easy task for a pack of robots as well, especially when the load needs to be lifted off the ground making the cooperative task less tolerant of errors. The limited research on such a load can be attributed to the mechanical complexity of the problem. Notably, previous studies have not considered the spatial, decentralized, communication-free scenario. We, therefore, consider a robotic pack of six agents that assumes the task of spatially moving a load through a cluttered space. As it transports the load, the pack carefully avoids planar obstacles while maintaining its orientation. To do so, we model the whole system as a six Prismatic-Prismatic-Spherical-Spherical (6-PPSS) redundant mobile platform, having twelve degrees of freedom. This paper focuses on a decentralized control scheme where no mutual communication is needed. Each agent calculates its ego movements according to the height of its corresponding load-node; the surrounding obstacles, and; the goal’s relative position. To avoid numerical errors appearing in the vicinity of singular configurations, we calculate the platform’s forward kinematics in the model’s full configuration space. We then show how this rationale can be further extended to formulate a distributed control scheme for the motion planner. We demonstrate our algorithms in several simulated scenarios and in a set of real-world experiments using specially designed omnidirectional robot agents. We test the ability of the pack to maintain the load’s orientation just by measuring the load’s height at the holding node of each agent. Lastly, we measured the time required for the pack to assume a desired load orientation. Results indicated that even in the presence of a 10-degree tilt error, the load was able to be restabilized within a maximum of 15 seconds in simulated conditions and 20 seconds in real-life experiments.

Solving the robotic swarm coverage problem for an elliptical area has various applications for exploring novel environments. Solutions for this problem should cover a specified ellipse and seamlessly adapt to changing numbers of robots. Previous solutions used techniques such as formation control, vector fields, and neural networks. While these techniques were successful, they all lacked one or more of the three key tenants of swarm elliptical attraction: complete coverage of an ellipse with commandable parameters, simplicity for scaling in the number of robots, and adaptive sizing. Additionally, no previous work presented guidelines for ensuring that the swarms could successfully and safely converge to the commanded ellipse without collisions. In contrast, this work presents a novel swarm elliptical attraction behavior with all three key tenants with guidelines for ellipse and swarm parameter selection. First, a new Lyapunov stable elliptical attraction behavior for Reactive Particle Swarms is presented. The behavior commands robots to cover the entire ellipse area for a specific semimajor axis, eccentricity, and orientation. Additionally, dynamic interagent spacing naturally ensures coverage for different numbers of robots. Second, the work presents a novel adaptive sizing algorithm that varies the ellipse’s semimajor axis based on the swarm state. The adaptive sizing algorithm specifies the eccentricity and orientation using time-varying functions. Third, guidelines for selecting the number of robots, commanded ellipse area, obstacle avoidance distance, and robot communication range that allow for successful aggregation to the commanded ellipse are presented. All three of the results are verified using simulation and hardware-in-the-loop trials.

Swarm robots have always served as verification platforms and deployment tools for swarming models that usually take relative distances, bearing angles, velocity directions, or differentiation of neighbors as inputs to regulate individual motion. With natural decentralization and high scalability, existing swarm robotic platforms based on purely implicit cooperation exhibit excellent potential for evaluating and deploying swarming models in denied environments. However, most of the current implicit cooperation platforms are limited in their adaptability because they are hardly capable of providing the above four inputs without external assistance. To address this problem, this article presents optiSwarm, a swarm robotic platform, where each robot (Cubot) within this platform is based on a novel implicit cooperation system. Cubot can not only directly obtain relative distances, bearing angles, and differentiation of adjacent robots through vision-based local rules but also has a relative velocity direction perception mechanism purely on implicit communication. The robot’s perception is proven to be reliable through real-world tests. Through implementing three collective behaviors guided by diverse inputs, optiSwarm is employed and evaluated in typical scenarios demonstrating that it is capable of verifying models of collective behaviors. [ABSTRACT FROM AUTHOR]

Due to the lack of systematic empirical analyses and comparisons of ideas and methods, a clearly established state of the art is still missing in the optimization-based design of robot swarms. In this article, we propose an experimental protocol for the comparison of fully automatic design methods. This protocol is characterized by two notable elements: 1) a way to define benchmarks for the evaluation and comparison of design methods and 2) a sampling strategy that minimizes the variance when estimating their expected performance. To define generally applicable benchmarks, we introduce the notion of mission generator: a tool to generate missions that mimic those a design method will eventually have to solve. To minimize the variance of the performance estimation, we show that, under some common assumptions, one should adopt the sampling strategy that maximizes the number of missions considered—a formal proof is provided as the supplementary material. We illustrate the experimental protocol by comparing the performance of two offline fully automatic design methods that were presented in previous publications. [ABSTRACT FROM AUTHOR]

This article presents a decentralized formation control strategy for electromagnetic formation flight (EMFF). A primary control challenge of EMFF is the coupling that occurs between the electromagnetic fields generated by all satellites. Alternating magnetic field forces (AMFF) are used to address these coupling challenges. Each satellite’s electromagnetic actuation system is driven by a sum of amplitude-modulated sinusoids, where the amplitudes are optimally designed to prescribe the average intersatellite force between each satellite pair while minimizing control effort. The communication structure is a connected undirected graph, where each satellite relies on relative-position and relative-velocity feedback of neighboring satellites. The controller is designed based on an average dynamic model, which is used to provide necessary and sufficient conditions for formation control. [ABSTRACT FROM AUTHOR]

Swarm intelligence has been applied to replicate numerous natural processes and relatively simple species to achieve excellent performance in a variety of disciplines. An autonomous approach employing deep reinforcement learning is presented in this study for swarm navigation. In this approach, complex 3D environments with static and dynamic obstacles and resistive forces such as linear drag, angular drag, and gravity are modeled to track multiple dynamic targets. In this regard, a novel island policy optimization model is introduced to tackle multiple dynamic targets simultaneously and thus make the swarm more dynamic. Moreover, new reward functions for robust swarm formation and target tracking are devised to learn complex swarm behaviors. Since the number of agents is not fixed and has only the partial observance of the environment, swarm formation and navigation become challenging. In this regard, the proposed strategy consists of four main components to tackle the aforementioned challenges: 1) Island policy-based optimization framework with multiple targets tracking 2) Novel reward functions for multiple dynamic target tracking 3) Improved policy and critic-based framework for the dynamic swarm management 4) Memory. The dynamic swarm management phase translates basic sensory input to high-level commands and thus enhances swarm navigation and decentralized setup while maintaining the swarm’s size fluctuations. While in the island model, the swarm can split into individual sub-swarms according to the number of targets, thus allowing it to track multiple targets that are far apart. Also, when multiple targets come close to each other, these sub-swarms have the ability to rejoin and thus form a single swarm surrounding all the targets. Customized state-of-the-art policy-based deep reinforcement learning neuro-architectures are employed to achieve policy optimization. The results show that the proposed strategy enhances swarm navigation by achieving a high cumulative reward and a low policy loss. The simulations show that the proposed framework can efficiently track multiple static and dynamic targets in complex environments.

This paper presents a bimodal control strategy for transitioning between different topologies of flocking and dynamic structures. The work is motivated by applications where agents must pursue and close in on a target prior to carrying out more sophisticated tasks. Specifically, it builds on previous work in consensus-based flocking to define a new type of dynamic target pursuit and capturing method called dynamic enspherement. The problem is formulated in terms of the canonical Reynolds rules and overlapping planes of encirclement using quaternions of arbitrary orientation. A new method for transitioning between flocking and enspherement is presented, the smoothness of which is demonstrated mathematically. The proposed approach is validated in a series of simulations on swarms of particles with double-integrator dynamics.

This paper describes an efficacious virtual-region-based shape control scheme for exploring an unknown occluded dynamic environment, likely to have multiple targets, by a swarm of robots. The traditional leader-follower strategy has been intertwined with the shape control technique to achieve group-splitting of the robotic swarm in multi-target or multi-path scenarios. If multiple passages are detected by the parent swarm during the navigational process, several sub-swarms with proportionate agents will be created for progressing through these separate paths. The splitting philosophy is dependent on the fitment of the virtual elliptical regions in each of the paths found ahead, which will guide their respective sub-swarms to navigate further. This work, subsequently, conceives a realistic condition where two or more pathways merge to form a single lane, leading to a unique target. In such situations, the sub-swarms (operating in those paths) rejoin and proceed to the goal as a unified unit. Therefore, in this work, two different features of a swarm robotics framework, namely splitting and merging, have been studied. The scalability control plan ensures strict cohesiveness amongst the agents (inside the mother swarm or any sub-swarm) during the exploration steps. To substantiate the efficacy of the proposed technique, simulation results along with hardware experiment are duly furnished in this article. Note to Practitioners—In recent years, autonomous robots have played an increasingly significant role in civil applications. The search and rescue operation is one such example. In this specific field, swarm robotic systems have the potential to significantly improve efficiency with faster search and rescue of victims, initial assessment and mapping of the environment, real-time monitoring, and surveillance operations. Motivated from this specification, in this work, we offer a novel trajectory planning technique for a robotic swarm employing the region-based shape control scheme with the leader-follower approach for achieving the splitting and merging. The proposed solution can be used in any kind of environmental circumstance, such as single or multiple targets, single or multiple pathways, and so on, such as a normal rescue operation in which several victims may be found in different locations. Moreover, to undertake complete surveillance, it would be cost-effective to deploy one large swarm and then split it into sub-swarms based on the requirements. [ABSTRACT FROM AUTHOR]

This article presents a novel optimization method for multirobot formation in an obstacle environment. For this challenge, we proposed an iterative optimization approach in previous work, in which all robots as a whole obtain the optimal goal pattern, and then, move to the goal without collision to form the pattern. However, this approach results in high consumption in terms of time and the path that robots travel as the number of robots increases. To improve efficiency, we propose a grouping-based optimization method. First, a specific grouping strategy (the number of groups and the number of data points in each group are fixed) is utilized to group the multiple robots. And then in an obstacle environment, each group of robots completes its optimal pattern formation in parallel without collision through coordination within and between groups. The simulation results of the multiletter pattern formation validate the effectiveness of the method proposed in this article compared to the method without grouping. [ABSTRACT FROM AUTHOR]

Swarm foraging is a common test case application for multi-robot systems. In this paper RepAtt algorithm is used for improving coordination of a robot swarm by selectively broadcasting repulsion and attraction signals. This is a chemotaxis-inspired search behaviour where robots use the temporal gradients of these signals to navigate towards more advantageous areas. Hardware experiments were used to model and validate realistic, noisy sound communication and vision system. We then show through extensive simulation studies that RepAtt significantly improves swarm foraging time and robot efficiency under realistic communication and vision models. Note to Practitioners—This research developed a swarm foraging algorithm that takes into consideration the vision and communication sensing noise levels faced by robots in real world applications. The algorithm, known as RepAtt, was developed with the aim of emphasizing algorithmic simplicity and limiting the hardware requirements for the robots in the swarm. In this paper, we have focused on the problem of deploying swarm robots to forage litter in an environment such as a park. The communication model of the robots was based on the physics of sound, while their vision system was modelled using experiments with deep neural networks based object detectors. The results show that the RepAtt algorithm is robust to different distributions of targets (or litter) in the search space, exhibits good swarm efficiency with changes in swarm population and is robust to noise in its communication and vision systems. Apart from the RepAtt algorithm, other contributions made by this research include modelling of robot vision system to aid extensive study of the impact of communication and vision noise on swarm coordination. This will be relevant for extensive testing and validation before deployment to swarm robots hardware. The sound communication used in this research limits the kinds of environment the robots can be deployed in. Echoes within an enclosed environment and bandwidth limitation for communication frequency and public disturbance due to sound emitted by the robots can all contribute to this limitation. Thus, this research can be improved by investing in the development of a communication technology with similar physics. Other areas of improvement include adopting better obstacle avoidance algorithms and implementing suitable manipulators for handling litter objects. The algorithm can be extended to make it applicable for solving other problems such as search and rescue operations where foraging targets could be disaster survivors; demining and hazardous waste cleanup, where targets are the mines or waste material; and planetary exploration, where targets could be interesting features of the planets are the targets searched for by the robots. [ABSTRACT FROM AUTHOR]

The small size and wireless actuation of microrobots make them potential candidates for minimally invasive medicine. To advance microrobots to future clinical application, microrobotics researchers have investigated a number of key issues, in which swarm control is a primary challenge and is attracting increasing attention. As a single microrobot has limited volume and surface area, clinically relevant tasks, including in-vivo tracking, usually require simultaneous control of a large swarm of microrobots. Unlike macroscale robots, implementing on-board actuators and sensors for microrobots is challenging, which differentiates swarm microrobotics from other swarm robotics approaches. This article systematically summarizes the state of the art for this emerging field, including actuation systems with different power sources, swarm behaviors modeling and simulation, swarm control strategies, and targeted biomedical applications. Actuation principles of microrobot swarms are categorized in detail, and critical comparisons are made to provide guidance and insight for future swarm microrobotics researchers. Considering the unique features of swarm microrobotics compared to traditional swarm robotics, this article also emphasizes the modeling, simulation, and control of microrobot swarms. Furthermore, recent biomedical applications of microrobot swarms are summarized to illustrate specific application scenarios. Finally, we provide an assessment of the future directions of swarm microrobotics. [ABSTRACT FROM AUTHOR]

Astrobots are robotic artifacts whose swarms are used in astrophysical studies to generate the map of the observable universe. These swarms have to be coordinated with respect to various desired observations. Such coordination is so complicated that distributed swarm controllers cannot always coordinate enough astrobots to fulfill the minimum data desired to be obtained in the course of observations. Thus, a convergence verification is necessary to check the suitability of coordination before its execution. However, a formal verification method does not exist for this purpose. In this article, we instead use machine learning to predict the convergence of astrobots swarm. As the first solution to this problem, we propose a weighted $k$ -NN-based algorithm that requires the initial status of a swarm and its observational targets to predict its convergence. Our algorithm learns to predict based on the coordination data obtained from previous coordination of the desired swarm. This method first generates a convergence probability for each astrobot based on a distance metric. Then, these probabilities are transformed to either a complete or an incomplete categorical result. The method is applied to two typical swarms, including 116 and 487 astrobots. It turns out that the correct prediction of successful coordination may be up to 80% of overall predictions. Thus, these results witness the efficient accuracy of our predictive convergence analysis strategy. Note to Practitioners—Observatories involved in the generation of spectroscopic surveys always encounter limited resources to check the throughputs of their planned observations before their executions. The information yielded by an observation directly depends on the convergence rate of the observatory’s astrobots in that particular observation. Namely, if the astrobots’ convergence rate is below a minimum, then the observation has to be revoked and replanned. Thus, one may define another observation that fulfills the minimum-information requirement. There has been yet no analytical tool developed to verify the convergence rate of the coordination computed by the state-of-the-art trajectory planners of astrobots swarms. Thus, we propose to use a machine learning scheme to predict the desired convergence rate instead of involving in the infeasible process of finding its exact value. This method is a supervised method that requires the target-to-astrobot assignments table of an observation. The algorithm also needs a data set including previous coordination results of various observations of a particular swarm. The simulated scenarios manifest magnificent accuracies in the convergence predictions of some astrobots swarms corresponding to modern spectroscopic surveys, such as SDSS-V (including ~500 astrobots). Our strategy is based on the smallest subset of the astrobots’ features that have a pivotal role in convergence rates, say, the projected positions of targets on a hosting telescope’s focal plane. We argue that more explorations have to be considered to find other important features, such as the motion direction of each astrobot, which may even further improve the obtained prediction accuracies. [ABSTRACT FROM AUTHOR]

Autonomous vehicles have been highlighted as a major growth area for future transportation systems and the deployment of large numbers of these vehicles is expected when safety and legal challenges are overcome. To meet the necessary safety standards, effective collision avoidance technologies are required to ensure that the number of accidents are kept to a minimum. As large numbers of autonomous vehicles, operating together on roads, can be regarded as a swarm system, we propose a bio-inspired collision avoidance strategy using virtual pheromones; an approach that has evolved effectively in nature over many millions of years. Previous research using virtual pheromones showed the potential of pheromone-based systems to maneuver a swarm of robots. However, designing an individual controller to maximise the performance of the entire swarm is a major challenge. In this paper, we propose a novel deep reinforcement learning (DRL) based approach that is able to train a controller that introduces collision avoidance behaviour. To accelerate training, we propose a novel sampling strategy called Highlight Experience Replay and integrate it with a Deep Deterministic Policy Gradient algorithm with noise added to the weights and biases of the artificial neural network to improve exploration. To evaluate the performance of the proposed DRL-based controller, we applied it to navigation and collision avoidance tasks in three different traffic scenarios. The experimental results showed that the proposed DRL-based controller outperformed the manually-tuned controller in terms of stability, effectiveness, robustness and ease of tuning process. Furthermore, the proposed Highlight Experience Replay method outperformed than the popular Prioritized Experience Replay sampling strategy by taking 27% of training time average over three stages. [ABSTRACT FROM AUTHOR]

Coordination of robot swarms has received significant research interest over the last decade due to its wide real-world applications including precision agriculture, target surveillance, planetary exploration, etc. Many of these practical activities can be formulated as a formation tracking problem. This brief aims to design a robust control strategy for networked robot swarms subjected to nonlinear dynamics and unknown disturbances. Firstly, a robust adaptive formation coordination protocol is proposed for robot swarms, which utilizes only local information for tracking a dynamic target with uncertain maneuvers. A rigorous theoretical proof utilizing the Lyapunov stability approach is then provided to guarantee the control performance. Towards the end, real-time hardware experiments with wheeled mobile robots are conducted to validate the robustness and feasibility of the proposed formation coordination approach. [ABSTRACT FROM AUTHOR]

In this article, we propose a decentralized approach to formation control in groups of stochastic mobile robots. Different from existing work, we explicitly model the presence of stochastic perturbations affecting the dynamics and model the multivehicle system through stochastic differential equations. We design a decentralized formation control strategy where only a subset of informed agents is aware of the desired target locations and derive sufficient conditions for almost sure convergence to the desired formation pattern. Specifically, we illustrate how the achievement of the control goal is related to the intensity of the noise and to the topology of the communication graph among the robots. The proposed formation control strategy is tested through extensive numerical analyses and validated experimentally on ground robots. [ABSTRACT FROM AUTHOR]

Swarm robotics is an approach to collective robotics with which it is easily possible to complete the tasks that are difficult to do with single robot. The wireless multiple access network contributes significantly in wireless mobile swarm robotics for communication and networking, especially in disaster scenarios. Since the unpredictable impacts may fail the sensing robots or nodes, causing the loss of valuable data, a coding scheme called the Growth codes is designed to increase data persistence. In the wireless multiple access network, a key requirement of data collection in disaster scenarios is to maintain a high data intermediate recovery rate. In the later period of the Growth codes, the large amount of redundant data in the network affects the efficiency of data collection. In this paper, we design and analyze techniques to reduce the number of redundant data, and propose a sectional configured data collection strategy based on the Growth codes, called the SCGC protocol. Setting cache nodes around the sink to filter the redundant codewords, the sink can collect data faster, ensuring a high data intermediate recovery rate. We also design an information update strategy and some constraints to control node overhead. Through the simulations, we show that the proportion of redundant codewords can be reduced by 10-15%, while the negative impact of these valid codewords is not considered in the Growth codes. We also show that the SCGC protocol can improve the data recovery efficiency without affecting the stability of the network or shortening the network lifetime.

This paper addresses the challenge of swarm robots search for multiple targets simultaneously. Techniques are investigated gradually and a systematic scheme which is based on mechanical particle swarm optimization and artificial potential field is eventually developed. The innovative extension makes the bio-inspired particle swarm optimization first endowed with the robots' mechanical properties which reduces the control expense and is already beyond the conventional application scope of this algorithm. The scheme closely considers the practical applications of real robots thus uses the differences, for example, signal frequencies, between the targets for organizing corresponding sub-robot groups aiming at different targets. Those robot groups which move towards non-aimed targets are applied with penalties thus an unimodal objective function for each robot group is built. Meanwhile, the developed method contains the ability for obstacle avoidance based on the module-switching strategy according to their priorities. The methods for controlling the group size and make balance of the search convergence and diversity are investigated, too. Rich simulations and experiments with real robots have been performed to verify this work. Promising results show the effectiveness and robustness of the proposed search method.

Sheepdogs smartly herd a flock of sheep and guide them towards a goal. A single dog can herd a few hundred sheep in easy to navigate environments. Understanding the interaction space between the sheepdogs, sheep and the environment is important due to the possibility of transferring this knowledge to solve practical swarm robotics problems. This interaction space is a complex mesh of influencing factors. We scrutinize this interaction space to identify areas where the complexity of the herding problem changes from low (easy to solve) to high (harder to solve or becoming unsolvable) complexity. In particular, we study reactive models for shepherding, whereby agents respond directly to stimuli in the environments by fusing the set of force vectors influencing their behaviour. We present an enhanced shepherding model with higher success rate than its predecessor. We investigate four key factors that influence the complexity of the problem: the relative speed between the sheepdog and sheep, the spatial configuration of the sheep at the start of the task, the number of sheepdogs, and the density of obstacles in the environment. We discovered a phase transition in shepherding resulting from the interaction between the number of sheepdogs and obstacles. The phase transition occurs as the density of obstacles range from 0.2% for a single shepherding agent to 5% for 10 shepherding agents. During this phase transition, the problem changes from being an easy problem where the flock gets collected quickly, to a hard one where the overall herding task becomes utterly not achievable using reactive approaches.

We study models and algorithms for Programmable Matter (PM), that is matter with the ability to change its physical properties (e.g., shape or optical properties) in a programmable fashion. PM can be implemented by assembling a system of weak self-organizing computational elements, called particles, that can be programmed via distributed algorithms to collectively achieve some global task. Recent advances in the production of nanotechnologies have rendered such systems increasingly possible in practice, thus triggering research interests from many areas of computer science. The most established models for PM assume that particles: are modeled as finite state automata; are all identical, executing the same algorithm based on local observation of the surroundings; live and operate in the cells of a hexagonal grid; can move from one cell to another by repeatedly alternating between a contracted state (a particle occupies one cell) and an expanded state (a particle occupies two neighboring cells). Given these elementary features, it is rather hard to design distributed algorithms even for basic tasks and, in fact, all existing solutions to solve fundamental problems via PM have resorted to endowing PM systems with various capabilities to overcome such hardness, thus assuming quite unrealistic features. In this paper, we move toward more realistic computational models for PM. Specifically, we first introduce SILBOT, a new modeling approach that relaxes several assumptions used in previous ones. Second, we present a distributed algorithm to solve, in the SILBOT model, a foundational primitive for PM, namely Leader Election. This algorithm works in O(n) rounds for all initial configurations of n particles that are both connected (i.e. particles induce a connected graph) and compact (i.e. without holes, that is no empty cells surrounded by particles occur). As usual in asynchronous contexts, a round is intended as the time within which all particles have been activated at least once. Third, we show that, if the initial configuration admits holes, it is impossible to achieve leader election while preserving connectivity. Finally, by slightly empowering the robots, we design an algorithm to handle initial configurations admitting holes that in O(n2) rounds solves the leader election problem while obtaining also compaction.

Machine Learning (ML) for swarm motion prediction is a relatively unexplored area that could help sustain and monitor daily swarm robotics collective tasks. This paper focuses on a specific application of swarm robotics which is pattern formation, to demonstrate the ability of Ensemble Learning (EL) approaches to predict the motion speed of swarm robots. Specifically, the boosted trees (BST) and bagged trees (BT) algorithms are introduced to predict the motion speed of a swarm of miniature two-wheels differential driver mobile robots performing a circle-formation via the viscoelastic control model. This choice’s motivation is due to EL-based models’ ability to improve the performance of ML models by combining multiple learners versus single regressors. Both BST and BT algorithms’ performances are compared to ten commonly known prediction models based on Support Vector Regressors (SVRs) and Gaussian Process Regressors (GPRs) with different kernel functions. Using simulated measurements recorded every 0.1 second from the robots’ sensors, we demonstrate the effectiveness of the developed methods over conventional ML models (SVR and GPR) in a free/non-free obstacles environment. Results showed that the BST and BT regression models reached the highest prediction performance with fully and partially connected swarms and even when involving different swarm sizes. [ABSTRACT FROM AUTHOR]

Inspired by the group activities of natural swarms (e.g., a flock of birds, a colony of ants, etc.), a fleet of mobile robots can be collaboratively put into work to accomplish complex real-world tasks. Depending on the nature and complexity of a problem, a multi-robot system (MRS) may need to be decomposed into several subgroups. This paper proposes a unified group coordinated control scheme for networked MRSs having multiple targets. A ‘discontinuous’ cooperative control law is first developed for a networked MRS to achieve individual sub-formations surrounding the assigned targets. A ‘continuous’ cooperative control protocol is then proposed to overcome the chattering phenomenon often caused by a discontinuous control action during hardware implementation. The closed-loop stability of the overall networked MRS is guaranteed via the Lyapunov theory and boundary-layer techniques. Finally, two hardware experiments (target-enclosing and object transportation) involving real mobile robots have been carried out to demonstrate the usefulness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This article presents a geometrical region-based shape control methodology for navigating a cohesive swarm-robotic structure toward the goal even in a field occluded by unknown obstacles. In this control approach, initially, the robotic swarm is conceived to lie within a well-defined virtual circular region thus preserving a strict interagent cohesiveness among them. However, during the progression, for evading severely constricted obstacles, the virtual circle has been allowed to change its shape and in the process, varied elliptical shapes are made to evolve. In essence, for a collision-free solution, this shrinking aspect (from circle to ellipse) depends entirely on the number of agents in the swarm and at the same time also reliance on the sensed distance between two nearest obstacles through which the shrunken circle or the virtual ellipse will be able to pass. Consequently, shape switching is a dynamic as well as a stochastic process throughout the journey of the swarm. For achieving these objectives, a two-level hierarchical control strategy has been employed. Moreover, during aggregating toward the target, the actuation failure of any agent or agents may occur. In this perspective, the proposed control law has been updated adaptively throughout the route such that agent failure does not encumber the mission. Finally, the extensive simulation results along with the hardware experimentation are provided to demonstrate the efficacy of the proposed scheme. [ABSTRACT FROM AUTHOR]

Applying swarm intelligence to actual swarm robotic systems is a challenging task especially with adequately consideration of corresponding practical constraints. Under the restrictions of the field-of-view limited relative positioning, local sensing and communication, kinematic limitations as well as anti-collision issues, this paper presents a constrained particle swarm optimization (PSO) based collaborative searching method for robotic swarms. Besides, the proposed method follows the concept of evolution speed and a modified aggregation degree to determine the adaptive weights in the robotic PSO model. The modified aggregation degree is associated with the number of members in one's field-of-view. Unlike the traditional position update method, the proposed method updates the forward speed and angular velocity of the robot using the non-holonomic model to realize the motion control of each robot. The simulation results show that the proposed method has the potential for the practical implementation of collaborative searching tasks for robotic swarms in different types of environments.

Aggregation is a vital behavior when performing complex tasks in most of the swarm systems, such as swarm robotics systems. In this paper, three new aggregation methods, namely the distance-angular, the distance-cosine, and the distance-Minkowski k-nearest neighbor (k-NN) have been introduced. These aggregation methods are mainly built on well-known metrics: the cosine, angular, and Minkowski distance functions, which are used here to compute distances among robots' neighbors. Relying on these methods, each robot identifies its k-nearest neighborhood set that will interact with. Then, in order to achieve the aggregation, the interactions sensing capabilities among the set members are modeled using a virtual viscoelastic mesh. Analysis of the results obtained from the ARGoS simulator shows a significant improvement in the swarm aggregation performance compared to the conventional distance-weighted k-NN aggregation method. Also, the aggregation performance of the methods is reported to be robust to partially faulty robots and accurate under noisy sensors.

In quality-diversity algorithms, the behavioral diversity metric is a key design choice that determines the quality of the evolved archives. Although behavioral diversity is traditionally obtained by describing the observed resulting behavior of robot controllers evaluated in a single environment, it is often more easily induced by introducing environmental diversity, i.e., by manipulating the environments in which the controllers are evaluated. This article proposes quality-environment-diversity (QED), an algorithm that repeatedly generates a random environment according to a probability distribution over environmental features (e.g., number of obstacles, arena size and robot sensor and actuator characteristics), evaluates the controller in that environment, and then describes the controller in terms of the features of that environment, the environment descriptor. Our study compares QED to three baseline task-specific and generic behavioral descriptors, in 5 different robot swarm benchmark tasks. For each task, the quality of the evolved archives is assessed by their capability to provide high-performing compensatory behaviors following injection of 250 unique faults to the robots of the swarm. The evolved archives achieve a median 2- to 3-fold reduction in the impact of the faults on the performance of the swarm. A qualitative analysis of evolved archives is done by visualizing the relation between diversity of compensatory behaviors, here called useful behavioral diversity, and fault recovery metrics. The resulting signatures indicate that, due to the diversity of environments inducing useful behavioral diversity, archives evolved by QED provide robot swarm controllers that are capable of recovering from high-impact faults. [ABSTRACT FROM AUTHOR]

As technology advances, the places we have been able to explore have drastically increased. However, the advancements in the underwater realm have staggered behind both the exploration of surface and air domains. This is due in part to the challenges that arise when placing a robot in water. One current shift has seen the use of a swarm of robots that are cheaper and are of a lower quality, that work together to accomplish a common goal, as opposed to using a single expensive robot. Swarm robotics benefits from being more tolerant of catastrophic failure and can cover large areas in smaller time frames. However, unlike other advancements in technology, underwater swarm robotics have struggled to compete with its counterparts on the surface and in the air. This is mainly due to the problems with communication underwater, the hazardous environment, the cost and difficulties with construction of underwater robots. This article conducts a literature review into the current state of underwater swarm robotics; it covers the design of the underwater robots, the methods used by the individual robot to perceive their environment, how they can localize to said environment, the methods of communication available underwater, centralized and decentralized control, the basis of swarm algorithms, the behaviors that are exhibited when a swarm works collectively and how swarms have been applied underwater. [ABSTRACT FROM AUTHOR]

Detecting anomalies in a robot swarm play a core role in keeping the desired performance, and meeting requirements and specifications. This paper deals with the problem of detecting anomalies in a robot swarm. In this regards, an unsupervised monitoring approach based on principal component analysis and k-nearest neighbor is proposed. The principal component analysis model is employed to generate residuals for anomaly detection. Then, the residuals are examined by computing the proposed exponentially smoothed k-nearest neighbor statistic for the purpose of anomaly detection. Here, instead of using parametric thresholds derived based on the Gaussian distribution, a nonparametric decision threshold is computed using the kernel density estimation method. This provides more flexibility to the proposed detector by relaxing assumption on the distribution underlying the data. Tests on data from ARGoS simulator show efficient performance of the proposed mechanism in monitoring a robot swarm. [ABSTRACT FROM AUTHOR]

Low-power edge-intelligence is leading to spectacular advances in smart sensors, actuators, and human–machine interfaces. In particular, energy efficiency is driving key advances in robotics, where low-power computation is augmented with smart control and mechanical systems to enable small-sized and intelligent drones, unmanned aerial vehicles (UAVs), micro-sized cars, and so on with applications in surveillance, disaster relief, and reconnaissance. Furthermore, for a variety of tasks, swarms of robots are often used as opposed to the individual robots. This article presents an energy-efficient computing platform that can enable a sample class of algorithms for swarm robotics. We demonstrate that both physical-model-based algorithms as well as learning-based algorithms can be supported on the same computing platform. We also demonstrate that with changing swarm sizes, the number of bits required to compute also scales. We take advantage of this observation to propose a hybrid-digital-mixed-signal computing platform, whose energy efficiency scales with the resolution of the data path and hence the swarm size. Measurements on a 65-nm CMOS test-chip demonstrate a peak energy efficiency of 9.1 TOPS/W at a 3-b resolution, and it scales down to 1.1 TOPS/W at an 8-b resolution. [ABSTRACT FROM AUTHOR]

As the size of robots decreases in multirobot systems, collisions cease to be catastrophic events that need to be avoided at all costs. This implies that less conservative, coordinated control strategies can be employed, where collisions are not only tolerated, but can potentially be harnessed as an information source. In this paper, we follow this line of inquiry by employing collisions as a sensing modality that provides information about the robots’ surroundings. We envision a collection of robots moving around with no sensors other than binary, tactile sensors that can determine if a collision occurred, and let the robots use this information to determine their locations. We apply a probabilistic localization technique based on mean-field approximations that allows each robot to maintain and update a probability distribution over all possible locations. Simulations and real multirobot experiments illustrate the feasibility of the proposed approach. [ABSTRACT FROM AUTHOR]

We present a distributed control algorithm simultaneously solving both the stochastic target assignment and optimal motion control for large-scale swarms to achieve complex formation shapes. Our probabilistic swarm guidance using inhomogeneous Markov chains (PSG–IMC) algorithm adopts a Eulerian density-control framework, under which the physical space is partitioned into multiple bins and the swarm's density distribution over each bin is controlled in a probabilistic fashion to efficiently handle loss or the addition of agents. We assume that the number of agents is much larger than the number of bins and that each agent knows in which bin it is located, the desired formation shape, and the objective function and motion constraints. PSG–IMC determines the bin-to-bin transition probabilities of each agent using a time IMC. These time-varying Markov matrices are computed by each agent in real time using the feedback from the current swarm distribution, which is estimated in a distributed manner. The PSG–IMC algorithm minimizes the expected cost of transitions per time instant that are required to achieve and maintain the desired formation shape, even if agents are added to or removed from the swarm. PSG–IMC scales well with a large number of agents and complex formation shapes and can also be adapted for area exploration applications. We demonstrate the effectiveness of this proposed swarm guidance algorithm by using numerical simulations and hardware experiments with multiple quadrotors. [ABSTRACT FROM AUTHOR]

We consider the problem of distributing a large group of heterogeneous robots among a set of tasks that require specialized capabilities in order to be completed. We model the system of heterogeneous robots as a community of species, in which each species (robot type) is defined by the traits (capabilities) that it owns. In order to solve the distribution problem, we develop centralized as well as decentralized methods to efficiently control the heterogeneous swarm of robots. Our methods assume knowledge of the underlying task topology and are based on a continuous model of the system that defines transition rates to and from tasks, for each robot species. Our optimization of the transition rates is fully scalable with respect to the number of robots, number of species, and number of traits. Building on this result, we propose a real-time optimization method that enables an online adaptation of transition rates as a function of the state of the current robot distribution. We also show how the robot distribution can be approximated based on local information only, consequently enabling the development of a decentralized controller. We evaluate our methods by means of microscopic simulations and show how the performance of the latter is well predicted by the macroscopic equations. Importantly, our framework also includes a diversity metric that enables an evaluation of the impact of swarm heterogeneity on performance. The metric defines the notion of minspecies, i.e., the minimum set of species that are required to achieve a given goal. We show that two distinct goal functions lead to two specializations of minspecies, which we term as eigenspecies and coverspecies. Quantitative results show the relation between diversity and performance. [ABSTRACT FROM PUBLISHER]

One challenge of programming soon-to-be-common large robotic teams is the definition of programming primitives that generate reusable and predictable behaviors. A new language construct allows developers to categorize robots using custom conditions and assign tasks to the groups created. The web extra at https://youtu.be/ddHXIef3nDo offers a real-life demonstration of a robot swarm programmed using Buzz, an extension language with dynamic typing. [ABSTRACT FROM AUTHOR]