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An innovative and flexible approach is introduced to address the challenge of self-organizing a group of mobile robots into cubic-spline based patterns without any requirement of control points. Besides the self-organization of mobile robots, the approach incorporates a potential field-based control for obstacle/collision avoidance. This will offer more flexibility to swarm robots to efficiently dealing with many practical situations including smoothly avoiding obstacles during movement, or exploring and covering areas with complex curved patterns. Essentially, this challenge is approached by proposing a formation control model basing on a Smoothed Particle Hydrodynamic estimation technique, which uses special cubic-spline kernel functions applied here to interpolate the density of each robot in the swarm. The moving information is used to weight the distances to the robot’s neighbours available in its field of view. Then an artificial physics mesh is finally built among each robot and its three available neighbours having the smallest weighted distances. Significant results toward emerging cubic-spline patterns are shown with a swarm of foot-bot mobile robots simulated in the ARGoS platform. Analysis results with different metrics are also conducted to assess the performance of the model with different swarm sizes and in the presence of sensory noise as well in the presence of partially faulty robots.

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\footnote{\z{A coordination sample of the astrobots may be found in \url{http://y2u.be/MpXWvpz4h00.}}} are 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 a coordination before its execution. However, a formal verification method does not exist for this purpose. In this paper, we instead use machine learning to predict the convergence of astrobots swarm. \z{As the first solution to this problem}, we propose a weighted $k$-NN-based algorithm which \rr{requires the initial status of a swarm as well as 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.

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.

Recent works in aerial robotics show that the self-organized and cohesive flight of swarms can emerge from the exchange of purely local information between neighboring agents. However, most of the current swarm models are not capable of flight in densely cluttered environments. Predictive models have the potential to incorporate safe collision avoidance capabilities and give the agents the ability to anticipate and synchronize their trajectories in real-time. Here, we propose a distributed predictive swarm model that generates self-organized, safe, and cohesive trajectories by solving an optimization problem in real-time. In simulation, we show that our method is scalable to large numbers of agents and suitable for deployment in different environments, specifically a forest and a funnel-like environment. Furthermore, our results show that the agents are capable of collision-free flight with noisy sensor measurements for a noise level of up to 70% of the magnitude of the agent safety distance. Real-world experiments with a swarm of up to 16 quadrotors in an indoor artificial environment validate our method. Supplementary Materials can be found at https://doi.org/10.5281/zenodo.5245214 .

In swarm decision making, hand-crafting agents’ rules that use local information to achieve desirable swarm-level behaviours is a non-trivial design problem. Instead of relying entirely on swarm experts for designing these local rules, machine learning (ML) algorithms can be utilised for learning some of the local rules by mapping an agent’s perception to an appropriate action. To facilitate this process, we propose the use of Machine Education (ME) as a systematic approach for designing a curriculum for teaching the agents the required skills to autonomously select appropriate behaviours. We study the use of ME in the context of decision-making in best-of-n problems. The proposed approach draws on swarm robotics expertise for identifying agents’ capabilities and limitations, the skills required for generating the desirable behaviours, and the corresponding performance measures. In addition, ME utilises ML expertise for the selection and development of the ML algorithms suitable for each skill. The results of the experimental evaluations demonstrate the superior efficacy of the ME-based approach over the state-of-the-art approaches with respect to speed and accuracy. In addition, our approach shows considerable robustness to changes in swarm size and to changes in sensing and communication noise. Our findings promote the use of ME for teaching swarm members the required skills for achieving complex swarm tasks.

Multi-option collective decision-making is a challenging task in the context of swarm intelligence. In this paper, we extend the problem of collective perception from simple binary decision-making of choosing the color in majority to estimating the most likely fill ratio from a series of discrete fill ratio hypotheses. We have applied direct comparison (DC) and direct modulation of voter-based decisions (DMVD) to this scenario to observe their performances in a discrete collective estimation problem. We have also compared their performances against an Individual Exploration baseline. Additionally, we propose a novel collective decision-making strategy called distributed Bayesian belief sharing (DBBS) and apply it to the above discrete collective estimation problem. In the experiments, we explore the performances of considered collective decision-making algorithms in various parameter settings to determine the trade-off among accuracy, speed, message transfer and reliability in the decision-making process. Our results show that both DC and DMVD outperform the Individual Exploration baseline, but both algorithms exhibit different trade-offs with respect to accuracy and decision speed. On the other hand, DBBS exceeds the performances of all other considered algorithms in all four metrics, at the cost of higher communication complexity.

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.

The quantum self-organization algorithm model of wise knowledge base design for intelligent fuzzy controllers with required robust level considered. Background of the model is a new model of quantum inference based on quantum genetic algorithm. Quantum genetic algorithm applied on line for the quantum correlation’s type searching between unknown solutions in quantum superposition of imperfect knowledge bases of intelligent controllers designed on soft computing. Disturbance conditions of analytical information-thermodynamic trade-off interrelations between main control quality measures (as new design laws) discussed in Part I. The smart control design with guaranteed achievement of these tradeoff interrelations is main goal for quantum self-organization algorithm of imperfect KB. Sophisticated synergetic quantum information effect in Part I (autonomous robot in unpredicted control situations) and II (swarm robots with imperfect KB exchanging between “master - slaves”) introduced: a new robust smart controller on line designed from responses on unpredicted control situations of any imperfect KB applying quantum hidden information extracted from quantum correlation. Within the toolkit of classical intelligent control, the achievement of the similar synergetic information effect is impossible. Benchmarks of intelligent cognitive robotic control applications considered.

This paper introduces the emerging field of astrobotics, that is, a recently-established branch of robotics to be of service to astrophysics and observational astronomy. We first describe a modern requirement of dark matter studies, i.e., the generation of the map of the observable universe, using astrobots. Astrobots differ from conventional two-degree-of-freedom robotic manipulators in two respects. First, the dense formation of astrobots give rise to the extremely overlapping dynamics of neighboring astrobots which make them severely subject to collisions. Second, the structure of astrobots and their mechanical specifications are specialized due to the embedded optical fibers passed through them. We focus on the coordination problem of astrobots whose solutions shall be collision-free, fast execution, and complete in terms of the astrobots' convergence rates. We also illustrate the significant impact of astrobots assignments to observational targets on the quality of coordination solutions To present the current state of the field, we elaborate the open problems including next-generation astrophysical projects including 20,000 astrobots, and other fields, such as space debris tracking, in which astrobots may be potentially used

As swarm robots begin to share the same space with people, it is critical to design legible swarm robot motion that clearly and rapidly communicates the intent of the robots to nearby users. To address this, we apply concepts from intent-expressive robotics, swarm intelligence, and vision science. Specifically, we leverage the trajectory, collective behavior, and density of swarm robots to generate motion that implicitly guides people’s attention toward the goal of the robots. Through online evaluations, we compared different types of intent-expressive motions both in terms of legibility as well as glanceability, a measure we introduce to gauge an observer’s ability to predict robots’ intent pre-attentively. The results show that the collective behavior-based motion has the best legibility performance overall, whereas, for glanceability, trajectory-based legible motion is most effective. These results suggest that the optimal solution may involve a combination of these legibility cues based on the scenario and the desired properties of the motion.

The aggregation behavior shown by swarm robots to establish coordination among each other is a basic behavior that is used in swarm robotics. This study proposes an aggregation method based on flocking for self-organizing aggregation behavior in swarm robotics. In the proposed method, a decision-making structure that determines robot movements for the swarm robots to show aggregation behavior is utilized. Each swarm robot can aggregate by decision-making only by itself without needing a control unit by using the proposed aggregation method. In the study, with swarm robots that have basic features, the aggregation method is applied in the simulation environment for different arena sizes, different numbers of robots and different detection distances. The performance of the proposed aggregation method is statistically examined for different arena sizes, different detection limits and different numbers of robots. Additionally, the proposed method is compared to a method in the literature in terms of aggregation completion time. According to the results, the proposed method realizes the aggregation behavior in a shorter time than the other method in all systematic simulations.

Human–swarm interaction is a frontier in the realms of swarm robotics and human-factors engineering. However, no holistic theory has been explicitly formulated that can inform how humans and robot swarms should interact through an interface while considering real-world demands, the relative capabilities of the components, as well as the desired joint-system behaviours. In this article, we apply a holistic perspective that we refer to as joint human–swarm loops, that is, a cybernetic system made of human, swarm and interface. We argue that a solution for human–swarm interaction should make the joint human–swarm loop an intelligent system that balances between centralized and decentralized control. The swarm-amplified human is suggested as a possible design that combines perspectives from swarm robotics, human-factors engineering and theoretical neuroscience to produce such a joint human–swarm loop. Essentially, it states that the robot swarm should be integrated into the human’s low-level nervous system function. This requires modelling both the robot swarm and the biological nervous system as self-organizing systems. We discuss multiple design implications that follow from the swarm-amplified human, including a computational experiment that shows how the robot swarm itself can be a self-organizing interface based on minimal computational logic.

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.

Decentralised autonomous systems rely on distributed learning to make decisions and to collaborate in pursuit of a shared objective. For example, in swarm robotics the best-of-n problem is a well-known collective decision-making problem in which agents attempt to learn the best option out of n possible alternatives based on local feedback from the environment. This typically involves gathering information about all n alternatives while then systematically discarding information about all but the best option. However, for applications such as search and rescue in which learning the ranking of options is useful or crucial, best-of-n decision-making can be wasteful and costly. Instead, we investigate a more general distributed learning process in which agents learn a preference ordering over all of the n options. More specifically, we introduce a distributed rank learning algorithm based on three-valued logic. We then use agent-based simulation experiments to demonstrate the effectiveness of this model. In this context, we show that a population of agents are able to learn a total ordering over the n options and furthermore the learning process is robust to evidential noise. To demonstrate the practicality of our model, we restrict the communication bandwidth between the agents and show that this model is also robust to limited communications whilst outperforming a comparable probabilistic model under the same communication conditions.

The ability to perform well in the presence of noise is an important consideration when evaluating the effectiveness of a collective decision-making framework. Any system deployed for real-world applications will have to perform well in complex and uncertain environments, and a component of this is the limited reliability and accuracy of evidence sources. In particular, in swarm robotics there is an emphasis on small and inexpensive robots which are often equipped with low-cost sensors more prone to suffer from noisy readings. This paper presents an exploratory investigation into the robustness of a negative updating approach to the best-of-n problem which utilises negative feedback from direct pairwise comparison of options and opinion pooling. A site selection task is conducted with a small-scale swarm of five e-puck robots choosing between $$n=7$$ n = 7 options in a semi-virtual environment with varying levels of sensor noise. Simulation experiments are then used to investigate the scalability of the approach. We now vary the swarm size and observe the behaviour as the number of options n increases for different error levels with different pooling regimes. Preliminary results suggest that the approach is robust to noise in the form of noisy sensor readings for even small populations by supporting self-correction within the population.

Physarum polycephalum, a unicellular and multiheaded slime mould, can form highly efficient networks connecting separated food sources during the process of foraging. These adaptive networks exhibit a unique characteristic in that they are optimized without the control of a central consciousness. Inspired by this phenomenon, we present an efficient exploration and navigation strategy for a swarm of robots, which exploits cooperation and self-organisation to overcome the limited abilities of the individual robots. The task faced by the robots consists in the exploration of an unknown environment in order to find a path between two distant target areas. For the proposed algorithm (EAIPP), we experimentally present robustness tests and obstacle tests conducted to analyse the performance of our algorithm and compare the proposed algorithm with other swarm robot foraging algorithms that also focus on the path formation task. This work has certain significance for the research of swarm robots and Physarum polycephalum. For the research of swarm robotics, our algorithm not only can lead multirobot as a whole to overcome the limitations of very simple individual agents but also can offer better performance in terms of search efficiency and success rate. For the research of Physarum polycephalum, this work is the first one combining swarm robots and Physarum polycephalum. It also reveals the potential of the Physarum polycephalum foraging principle in multirobot systems.

In the era of autonomous robots, multi-targets search methods inspired researchers to develop adapted algorithms to robot constraints, and with the rising of Swarm Intelligence (SI) approaches, Swarm Robotics (SRs) became a very popular topic. In this paper, the problem of searching for an exponentially increasing number of targets in a complex and unknown environment is addressed. Our main objective is to propose a Robotic target search strategy based on the EHO (Elephants Herding Optimization) algorithm, namely Robotic-EHO (REHO). The main additions were the collision-free path planning strategy, the velocity limitation, and the extension to the multi-target version in discrete environments. The proposed method has been the subject of many experiments, emulating the search of infected individuals by COVID-19 in a context of containment within complex and unknown random environments, as well as in the real case study of USA. The particularity of these environments is their increasing targets' number and the dynamic Containment Rate (CR) that we propose. The experimental results show that REHO reacts much better in high Containment Rate, early start search mission, and where the robots' speed is higher than the virus spread speed.

This paper studies swarms of autonomous musical robots and its contributions are twofold. First, I introduce Dr. Squiggles, a simple rhythmic musical robot, which serves as a general platform for s...

In quality-diversity algorithms, the behavioural diversity metric is a key design choice that determines the quality of the evolved archives. Although behavioural diversity is traditionally obtained by describing the observed resulting behaviour 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 paper proposes Quality-Environment-Diversity, 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 Quality-Environment-Diversity to three baseline task-specific and generic behavioural 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 behaviours 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 visualising the relation between diversity of compensatory behaviours, here called useful behavioural diversity, and fault recovery metrics. The resulting signatures indicate that, due to the diversity of environments inducing useful behavioural diversity, archives evolved by QED provide robot swarm controllers that are capable of recovering from high-impact faults.

The motion control problem of UAVs flock moving through obstacle field towards a target point in quasi-two-dimensional space is considered. An approach is suggested, according to which the grid with cells of the selected size is imposed on the mission area. At the same time, the obstacle is interpolated by the set of convex polygons constructed on the nodes of this grid. The algorithm for determining the desired speed of objects in a group based on swarm robotics principles is proposed. The results of the simulation modeling are presented.

Aggregation, a widely observed behavior in social insects, is the gathering of individuals on any location or on a cue. The former being called the self-organized aggregation, and the latter being called the cue-based aggregation. One of the fascinating examples of cue-based aggregation is the thermotactic behavior of young honeybees. Young honeybees aggregate on optimal temperature zones in the hive using a simple set of behaviors. The state-of-the-art cue-based aggregation method BEECLUST was derived based on these behaviors. The BEECLUST method is a very simple, yet a very capable method that has favorable characteristics such as robustness to noise and simplicity to apply. However, the BEECLUST method does not perform well in low robot densities. In this article, inspired by the navigation techniques used by ants and bees, a self-adaptive landmark-based aggregation method is proposed. In this method, robots use landmarks in the environment to locate the cue once they “learn” the relative position of the cue with respect to the landmark. With the introduction of an error threshold parameter, the method also becomes adaptive to changes in the environment. Through systematic experiments in kinematic and realistic simulators with different parameters, robot densities, and cue sizes, it was observed that using the information of the environment makes the proposed method to show better performance than the BEECLUST in all the settings, including low robot density, high noise, and dynamic conditions.

The objective of area exploration is to traverse the area effectively and random walk methods are the most commonly used searching strategy for swarm robots. Existing research mainly compares the effectiveness of various random walk methods through experimental verification, which has relatively large limitations. In order to make the application of the random walk methods more convenient, this paper quantitatively analyzes the searching efficiency (SE) of random walk methods. Firstly, the formula of the mean square displacement (MSD) of the robot position is given, and it is shown that the mean and the variance of the random step length are the factors that affect the SE. In addition, in order to produce the suitable step length, a truncated random walk method is constructed to make the generated step lengths follow a given distribution and the step lengths are within a specified range, thereby improving the SE. Thirdly, the correlations between the step length threshold (SLT), the area of the region, and the number of robots are provided based on MSD and truncated random walk method. When the area of region and the number of robots are fixed, there exists a SLT. When the expectation of the step length is greater than SLT, the swarm robots can achieve the highest SE. The area exploration task of swarm robots are carried out in simulation experiments and the coverage ratio is used to evaluate the SE of each random walk method. The experimental results show that when the area and the number of robots are given, there exist an optimal step length, which can enable the robots to achieve the optimal search.

Swarm robotic systems are heavily inspired by observations of social insects. This often leads to robustness being viewed as an inherent property of them. However, this has been shown to not always be the case. Because of this, fault detection and diagnosis in swarm robotic systems is of the utmost importance for ensuring the continued operation and success of the swarm. This paper provides an overview of recent work in the field of exogenous fault detection and diagnosis in swarm robotics, focusing on the four areas where research is concentrated: immune system, data modelling, and blockchain-based fault detection methods and local-sensing based fault diagnosis methods. Each of these areas have significant advantages and disadvantages which are explored in detail. Though the work presented here represents a significant advancement in the field, there are still large areas that require further research. Specifically, further research is required in testing these methods on real robotic swarms, fault diagnosis methods, and integrating fault detection, diagnosis and recovery methods in order to create robust swarms that can be used for non-trivial tasks.

Nowadays most of the hazardous work such as rescue, mining, collection of terrain samples, toxic waste cleanup done by the people. Due to this high-risk work, more people will suffer from health issues even some people will lose a life. To save people from this kind of hazardous work, robots can be replaced. The single robot will not accomplish the task within a short period. For this single robot, the initial capital cost and maintenance cost is too high. Due to this cost and time issues, the number of small robots is used instead of a single robot. In this study, the foraging behaviour of the number of small robots is to be analyzed. Foraging is the task to find items and bring back the item into the nest. In such a situation, collision and congestion are possible. To avoid collision and congestion, different searching strategies are developed. All the strategies are to be simulated using player stage simulation software. From this simulation, responses to be recorded and optimized using the design of experiments. Finally, the best significant strategy is to be identified through the time comparison analysis with the help of the task completion time of all the strategies.

An Artificial Moment Method (AMM) is proposed for conflict resolution of swarm robots with path planning tasks, where some robots are in narrow passages and close to their targets while many other robots need to pass through the passages. In the AMM, an algorithm based on key companion candidates and their weight times is presented first for key companions of the robots close to their targets. As such, other robots, even with different motion directions, can bypass the robots in a more reasonable manner. Then, two new algorithms are presented for attractive points and attractive angles of robots or to modify the obtained attractive angles. The existing artificial moment motion controller is also improved. Consequently, the negative effects of robots on each other are decreased, and conflicts between robots can be resolved more easily. Simulations indicate that compared with existing AMMs, the proposed one can yield better solutions in complex situations.

This paper presents development of a circuit solution design for a bidirectional wireless energy transfer system, based on a resonant self-oscillator. The operation principle of the developed circuit solution in receiving and transmitting mode is described and the elementary circuit diagram is presented together with design ratios. Coil parameters for the resonant circuit are calculated, optimal number of turns in coils is presented, based upon the specified limit value of permissible current. The dependencies of system efficiency from transmitted power, maximum transmitted power, and energy transmission distance are obtained. The developed design, which includes the step-up DC-DC converter, allows to obtain the voltage on the output of the receiving system, equal to or higher than the voltage of the power source of the transmitting system. The specific feature of the proposed system is that it does not require a dedicated control system for operation in resonant mode and changing direction of power transfer. Resonance in transmitting and receiving coils can be maintained, even when their mutual layout is changed, due to utilization of identical resonant circuits and a self-oscillator. Application of the proposed solution is relevant for energy transfer among autonomous robots with limited positioning accuracy, as well as for energy transfer from power supply to robot or in reverse direction.

Swarm robotics material handling is a new research area inspired by biological systems such as ant or bee colonies. It comprises a system consisting of two or more small robots with simple control mechanisms capable of achieving complex shared behaviors on the swarm level such as aggregation, pattern formation and collective transportation etc. Within the scope of our knowledge presently there are no swarm robotics material handling applications for real-life problems. In multitude knowledge, self-gathering and self-reconfigurability are among the most significant and troublesome attributes as they can add extra capacities and usefulness to swarm robots material handling. In this paper, we survey the existing works on swarm robotics material handling and their applications on the conservation of solar energy.

Background A large number of robots have put forward the new requirements for humanrobot interaction. One of the problems in human-swarm robot interaction is how to naturally achieve an efficient and accurate interaction between humans and swarm robot systems. To address this, this paper proposes a new type of human-swarm natural interaction system. Methods Through the cooperation between three-dimensional (3D) gesture interaction channel and natural language instruction channel, a natural and efficient interaction between a human and swarm robots is achieved. Results First, A 3D lasso technology realizes a batch-picking interaction of swarm robots through oriented bounding boxes. Second, control instruction labels for swarm-oriented robots are defined. The instruction label is integrated with the 3D gesture and natural language through instruction label filling. Finally, the understanding of natural language instructions is realized through a text classifier based on the maximum entropy model. A head-mounted augmented reality display device is used as a visual feedback channel. Conclusions The experiments on selecting robots verify the feasibility and availability of the system.

Particle Swarm Optimization (PSO) is an excellent population-based optimization algorithm. Meanwhile, because of its inspiration source and the velocity update feature, it is also widely used in the collaborative searching tasks for swarm robotics. One of the PSO-based models for robotic swarm searching tasks is Robotic PSO (RPSO). It adds additional items for obstacle avoidance into standard PSO and has been applied to many single-target search tasks. However, due to PSO’s global optimization characteristics, it is easy to converge to a specific position in the search space and lose the ability to explore further. When faced with the problem of multitarget searching, it may become inefficient or even invalid. This paper proposes an Exploration Enhanced Robotic PSO (E2RPSO) method for multitarget searching problems for robotic swarms. The proposed method modifies the third item in the RPSO as an additional attraction term. This item not only enables the robot to avoid collisions but also guides the swarm to search unexplored regions as much as possible. This operation increases the swarm’s task-specific (top-down) diversity, making the system cover a broader search area and avoid falling into local optimums. Besides, the aggregation degree and evolution speed factors are also included in determining the inertia weight of the proposed method, which adjusts the swarm’s internal (bottom-up) diversity dynamically. The comparison results show that this method can balance the relationship between exploration and exploitation well, which has the potential to be applied to multitarget searching scenarios.

Collective behaviour in nature provides a source of inspiration to engineer artificial collective adaptive systems, due to their mechanisms favouring adaptation to environmental changes and enabling complex emergent behaviour to arise from a relatively simple behaviour of individual entities. As part of our ongoing research, we study the social amoeba Dictyostelium discoideum to derive agent-based models and mechanisms that we can then exploit in artificial systems, in particular in swarm robotics. In this paper, we present a selection of agent-based models of the aggregation phase of D. discoideum, their corresponding biological illustrations and how we used them as an inspiration for transposing this behaviour into swarms of Kilobots. We focus on the stream-breaking phenomenon occurring during the aggregation phase of the life cycle of D. discoideum. Results show that the breakup of aggregation streams depends on cell density, motility, motive force and the concentration of cAMP and CF. The breakup also comes with the appearance of late centres. Our computational results show similar behaviour to our biological experiments, using Ax2(ka) strain. For swarm robotics experiments, we focus on signalling and aggregation towards a centre.

This paper presents a stability analysis of swarm robots, a group of multiple robots. In particular, we focus on robot swarms with heterogeneous abilities, in which each robot has a different sensing range and physical limitations, including maximum velocity and acceleration. In addition, each robot has a unique sensing region with a limited angle field of view. We previously proposed a decentralized navigation method for such heterogeneous swarm robots consisting of one leader and multiple followers. With the decentralized navigation method, a single leader can navigate for followers while maintaining connectivity and satisfying the physical limitations unique to each robot; i.e., each follower has a target robot and follows it without violating its physical limitations. In this paper, we focus on a stability analysis of such swarm robots. When the leader moves at a constant velocity, we mathematically prove that the shape and orientations of all robots eventually converge to the equilibrium state. For this, we must first prove that the equilibrium state exists. Then, we show the convergence of the state to its equilibrium. Finally, we carry out experiments and numerical simulations to confirm the stability analysis, i.e., the convergence of the swarm robots to the equilibrium states.

The development of flexible swarm robotics systems capable of adapting to the task and environmental changes is a serious challenge. The main motivations of Swarm robotics are decentralized control, stability, adaptivity, and flexibility. Usually, ad-hoc approaches are employed to design a controller capable of meeting the problem specifications. However, these methods cannot be easily verified, and in some cases, it is not even shown that they meet the specifications. Moreover, the controller source code has to be developed separately, primarily when formal methods are employed; As a result, it cannot be guaranteed that the implementation matches the design. This paper proposes a new method - probabilistic timed supervisory control (ptSCT) - to formally design a controller from systems specifications. The proposed ptSCT has several advantages: 1) the automatic generation of the controller source code utilizable in ARGoS platform, 2) formal designing capability using the implemented software tool, 3) set of powerful design components like probabilistic decisions and time constraints, and 4) the reusability of formally designed modules among different scenarios and multiple robotic platforms. Two case studies are considered to investigate various aspects of the proposed system. Firstly, the synchronization case study is implemented for a comparison between SCT and ptSCT in terms of design capabilities and memory consumption. Secondly, the foraging case study as a complex and medium-sized problem is modeled using ptSCT step by step. More than 2400 experiments with a varying number of obstacles, targets, and robots are executed in ARGoS platform in order to show the performance of the automatically generated source code.

When searching for multiple targets in an unknown complex environment, swarm robots should firstly form a number of subswarms autonomously through a task division model and then each subswarm searches for a target in parallel. Based on the probability response principle and multitarget division strategy, a closed-loop regulation strategy is proposed, which includes target type of member, target response intensity evaluation, and distance to the corresponding individuals. Besides, it is necessary to make robots avoid other robots and convex obstacles with various shapes in the unknown complex environment. By decomposing the multitarget search behavior of swarm robots, a simplified virtual-force model (SVF-Model) is developed for individual robots, and a control method is designed for swarm robots searching for multiple targets (SRSMT-SVF). The simulation results indicate that the proposed method keeps the robot with a good performance of collision avoidance, effectively reducing the collision conflicts among the robots, environment, and individuals.

Optimization-based design is an effective and promising approach to realizing collective behaviours for robot swarms. Unfortunately, the domain literature often remains vague about the exact role played by the human designer, if any. It is our contention that two cases should be disentangled: semi-automatic design, in which a human designer operates and steers an optimization process (for example, by fine-tuning the parameters of the optimization algorithm); and (fully) automatic design, in which the optimization process does not involve, need or allow any human intervention. In this Perspective, we briefly review the relevant literature; illustrate the hypotheses, characteristics and core challenges of semi-automatic and automatic design; and sketch out the context in which they could be ideally applied. Developing swarm robots for a specific application is a time consuming process and can be alleviated by automated optimization of the behaviour. Birattari and colleagues discuss that there are two fundamentally different design approaches; a semi-autonomous one, which allows for situation specific tuning from human engineers and one that needs to be entirely autonomous.

Achieving efficient and reliable self-organization in groups of autonomous robots is a fundamental challenge in swarm robotics. Even simple states of collective motion, such as group translation or rotation, require nontrivial algorithms, sensors, and actuators to be achieved in real-world scenarios. We study here the capabilities and limitations in controlling experimental robot swarms of a decentralized control algorithm that only requires information on the positions of neighboring agents, and not on their headings. Using swarms of e-Puck robots, we implement this algorithm in experiments and show its ability to converge to self-organized collective translation or rotation, starting from a state with random orientations. Through a simple analytical calculation, we also unveil an essential limitation of the algorithm that produces small persistent oscillations of the aligned state, related to its marginal stability. By comparing predictions and measurements, we compute the experimental noise distributions of the linear and angular robot speeds, showing that they are well described by Gaussian functions. We then implement simulations that model this noise by adding Gaussian random variables with the experimentally measured standard deviations. These simulations are performed for multiple parameter combinations and compared to experiments, showing that they provide good predictions for the expected speed and robustness of the self-organizing dynamics.

Segregative behaviors, in which individuals with common characteristics are placed together and set apart from other groups, are commonly found in nature. In swarm robotics, these behaviors can be important in different tasks that require a heterogeneous group of robots to be divided in homogeneous sets according to their physical (sensors, actuators) or logical (algorithms) capabilities. In this paper, we propose a controller that can spatially segregate a swarm of robots in two specific ways: clusters and concentric rings. By segregation, we mean that the swarm is partitioned in groups, with similar robots belonging to a same group, and these groups are spatially separated from each other. We achieve this by adapting and extending the differential potential concept, an abstraction of the mechanism by which cells achieve segregation. We present stability analysis and perform simulated experiments in 2D and 3D spaces in order to show the robustness of the proposed controller. Experiments with a limited number of real robots are also presented as a proof of concept. Results show that our approach allows a swarm of heterogeneous robots to segregate in a stable, compact, and collision-free fashion.

Ultra-Wideband (UWB) technology has great potential for the cooperation and navigation among near ground mobile robots in GPS-denied environments. In this paper, an efficient two-segment UWB radio channel model is proposed with considering the multi-path condition in very near-ground environments and different surface roughness. We conducted field measurements to collect channel information, with both transmitter and receiver antennas placed at different heights above the ground: 0cm–20cm. Signal frequency was chosen at 4.3GHz with bandwidth of 1GHz. Three ground coverings were tested in common scenarios: brick, grass and robber fields. The proposed model has enhanced accuracy achieved by careful assessment of dominant propagation mechanisms in each segment, such as diffraction loss due to obstruction of the first Fresnel zone and higher-order waves produced by ground roughness. It is realized that antenna height and distance are the most influential geometric parameters to affect the path loss model. Once the antenna height is known, there exists a breakpoint distance in UWB propagation, which separates two segmentation using the different path-loss mechanism. Different surface types can cause different signal attenuation. Monte Carlo simulations are used to investigate the effects of antenna height, distance, ground surface type on mobile robots swarm communication to find out the antenna height is also a dominant factor on connectivity and the average number of neighbors. Within a certain range, the higher the antenna height and the closer the communication distance, the better the communication performance will usually be. Cramer-Rao lower bound(CRLB) of path loss estimator based on the proposed model is derived to show the relationship between CRLB with height and distance.

Advances in robotics have paved the way for a novel approach of organizing large numbers of robots, otherwise referred to as multi-robots. Multi-robots can either be homogenous or heterogeneous. Nevertheless, a group of autonomous and relatively homogenous robots that interacts with one another as well as with their environment is referred to as swarm robots. Swarm robots are biologically inspired by natural swarms as found in animal societies such as birds and fishes as well as social insects such as honey bees, wasps, termites and ants. Hence, they exhibit certain properties which are similar to those found in these creatures such as aggregation, self-organization, foraging as well as flocking. Swarm robots work together to achieve a desired goal, which is usually too complex for a single robot to accomplish. They are typically characterized by simplicity of individuals, fault tolerance, autonomy, parallelism, high reliability, scalability as well as robustness. They can be used for mining, military, medical and agricultural activities. They can also be used for search and rescue missions, toxic waste cleanup, and for piling sandbags along coastlines in preparation for floods or hurricane. Nevertheless, swarm robots are plagued with the stigma of widespread, interference, uncertainty, safety and lack of reliable communication. Furthermore, studies in swarm robotics are practically limited to virtual reality simulations. Hence, the principles of swarm robotics are rarely applied to real-life problems. It is against this background that this study systematically explores swarm robots. This study reviewed eighty literatures relating to swarm robots. These literatures were obtained from journal articles, technical reports, books, and conference proceedings. The selection of these literatures was based on their relevance to the research problem. This study revealed that the application of swarm robots to real life problems would promote the development of systems that are robust, fault tolerant and scalable.

This paper discusses the various task allocation algorithms that have been researched, analyzed, and used in swarm robotics. The main reason for switching over to swarm robotics from ordinary mobile robots is because of its ability to perform complex tasks co-operatively with other bots rather than individually. Furthermore, they can be scaled to perform any kind of tasks. To carry out tasks like foraging, surveying and other such tasks that require swarm intelligence, task allocation plays an important role. It is the crux of the entire system and plays a huge role in the success of the implementation of swarm robotics. Few algorithms that address this task allocation have been briefly discussed here.

Background: With the curiosity of exploring the underwater world, science has devised various technologies and machines that can help them in performing activities like exploring, navigating and plunging into the unknown world of oceanography. Underwater Robot or vehicle can be claimed as an outcome of extensive research done by the scientists who aimed at discovering the unknown mysterious world of ocean and how it can benefit humanity. Swarm robotics is an entirely new section of robotics that has been developed based on swarm intelligence. Considering the fact, swarm robotics being still in nuptial stage, researchers have provided immense contribution with an aim to develop this technology. The objective of the paper is to present a comprehensive review covering the various technical and conceptual aspects of underwater swarm robotic system. Methods: A systematic review on state-of-the-art has been performed where contributions of various researchers was considered. The study emphasis on the concepts, technical background, architecture and communication medium along with its applicability in various fields that also include various issues and challenges faced while attaining them. Results: The incorporation of swarm intelligence in underwater robotics provides a new angle altogether into the working pattern of underwater robotic system. Conclusion: The article is a systematic presentation of swarm robot technologies, their mechanisms, conceived and designed communication medium with respect to adaptability of the vehicle to the versatile nature of water. The paper delineates the various conceptual and technical details and its beneficence to humanity.

In this paper, we study human trust and its computational models in supervisory control of swarm robots with varied levels of autonomy (LOA) in a target foraging task. We implement three LOAs: manual, mixed-initiative (MI), and fully autonomous LOA. While the swarm in the MI LOA is controlled by a human operator and an autonomous search algorithm collaboratively, the swarms in the manual and autonomous LOAs are fully directed by the human and the search algorithm, respectively. From user studies, we find that humans tend to make their decisions based on physical characteristics of the swarm rather than its performance since the task performance of swarms is not clearly perceivable by humans. Based on the analysis, we formulate trust as a Markov decision process whose state space includes the factors affecting trust. We develop variations of the trust model for different LOAs. We employ an inverse reinforcement learning algorithm to learn behaviors of the operator from demonstrations where the learned behaviors are used to predict human trust. Compared to an existing model, our models reduce the prediction error by at most 39.6%, 36.5%, and 28.8% in the manual, MI, and auto-LOA, respectively.

The subtractive manufacturing of machining and the additive manufacturing of 3D printing rely on special processing equipment, and the produced parts have inherent flaws such as single function and non-reusability. Combining with the development of self-assembly in swarm robotics, a programmable forming method with flexible task is previously proposed. However, self-assembly of hollow shapes which have a hole inside has proved to be difficult to achieve, because robots in the hollow shape have less communication and reference to make decisions, while individuals make decisions purely through local interactions. In this paper, we propose a novel distributed self-assembly approach for hollow shape, which employs the stratified mechanism and uses a chain forming approach. The novel approach mainly includes the methods of state update, follow-up motion, rule extension, and trapped planning, respectively, solving the message blocking, motion separation, and robot trapped problems when the motion-chains fill up the hollow shape. We evaluate the feasibility and flexibility of this approach in simulation, and demonstrate the self-assembly algorithm on the hardware robotic platform designed in our lab. The formation of the hollow shape expands the range in which self-assembly can be formed, making it suitable for various types of parts.

Lately, robot swarm has widely employed in many applications like search and rescue missions, fire forest detection and navigation in hazard environments. Each robot in a swarm is supposed to move without collision and avoid obstacles while performing the assigned job. Therefore, a formation control is required to achieve the robot swarm three tasks. In this article, we introduce a decentralized formation control algorithm based on the potential field method for robot swarm. Our formation control algorithm is proposed to achieve the three tasks: avoid obstacles in the environment, keep a fixed distance among robots to maintain a formation and perform an assigned task. An artificial neural network is engaged in the online optimization of the parameters of the potential force. Then, real-time experiments are conducted to confirm the reliability and applicability of our proposed decentralized formation control algorithm. The real-time experiment results prove that the proposed decentralized formation control algorithm enables the swarm to avoid obstacles and maintain formation while performing a certain task. The swarm manages to reach a certain goal and tracks a given trajectory. Moreover, the proposed decentralized formation control algorithm enables the swarm to escape from local minima, to pass through two narrow placed obstacles without oscillation near them. From a comparison between the proposed decentralized formation control algorithm and the traditional PFM, we obtained that NN-swarm successes to reach its goal with average accuracy 0.14 m compared to 0.22 m for the T-swarm. The NN-swarm also keeps a fixed distance between robots with a higher swarming error reaches 34.83%, while the T-swarm reaches 23.59%. Also, the NN-swarm is more accurate in tracking a trajectory with a higher tracking error reaches 0.0086 m compared to min. error of T-swarm equals to 0.01 m. Besides, the NN-swarm maintains formation much longer than T-swarm while tracking trajectory reaches 94.31% while the T-swarm reaches 81.07% from the execution time, in environments with different numbers of obstacles.

Robotic swarms and mobile sensor networks are used for environmental monitoring in various domains and areas of operation. Especially in otherwise inaccessible environments, decentralized robotic swarms can be advantageous due to their high spatial resolution of measurements and resilience to failure of individuals in the swarm. However, such robotic swarms might need to be able to compensate misplacement during deployment or adapt to dynamical changes in the environment. Reaching a collective decision in a swarm with limited communication abilities without a central entity serving as decision-maker can be a challenging task. Here, we present the CIMAX algorithm for collective decision-making for maximizing the information gathered by the swarm as a whole. Agents negotiate based on their individual sensor readings and ultimately make a decision for collectively moving in a particular direction so that the swarm as a whole increases the amount of relevant measurements and thus accessible information. We use both simulation and real robotic experiments for presenting, testing, and validating our algorithm. CIMAX is designed to be used in underwater swarm robots for troubleshooting an oxygen depletion phenomenon known as “anoxia.”

In recent years, research into self-organized swarm robotics has received much attention. Within this research, development of a general control model that functions under different conditions has been a hot topic. One significant approach is to use a control model with tunable parameters determined through simulation. In this article, we propose a rule-based swarm robotics control model with tunable parameters. To improve the performance, a modified non-dominated sorting genetic algorithm (NSGA II) is embedded into this control model. Multiple scenarios including convex obstacles, square obstacles, and tunnels are used to test the performances of the proposed control model. Comparison is made with three widely applied population-based multi-objective optimization algorithms. Experimental results show that our control model has good performance and is robust under different scenarios.