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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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Numerous millirobots were developed in the past decade for autonomous swarm systems that aim to utilize large numbers of these units in space-constrained environments. However, the size limitation of these robots has often resulted in their reduced computational, sensing, and locomotion capabilities. mROBerTO (milli-ROBot-TOronto) was developed in response to such limitations. Despite its enhanced features, the reliable and repeatable locomotion of mROBerTO has still been of some concern due to lack of effective closed-loop motion control – as is the case with all other similar millirobots. In this letter, we present the next version of mROBerTO with a new locomotion mechanism that utilizes stepper motors, capable of micro-stepping down to 1/32 of a full step, to yield a millirobot with maneuvering capabilities superior to current similar-sized robots. mROBerTO 2.0 is novel in that it utilizes these stepper motors without relying on a separate processor for controlling them. This letter also presents a complementary new algorithm for efficiently converting desired trajectories into robot-motion commands. The proposed algorithm was developed to allow millirobots to execute complex trajectories reliably in an open-loop manner.

In this paper, we use simulated swarms of robots to further explore the aggregation dynamics generated by these simple individual mechanisms. Our objective is to study the introduction of "informed robots", and to study how many of these are needed to direct the aggregation process toward a pre-defined site among those available in the environment. Informed robots are members of a group that selectively avoid the site/s where no aggregate should emerge, and stop only on the experimenter predefined site/s for aggregation. We study the aggregation process with informed robots in three different scenarios: two that are morphologically symmetric, whereby the different types of aggregation site are equally represented in the environment; and an asymmetric scenario, whereby the target site has an area that is half the area of the sites that should be avoided. We first show what happens when no robot in the swarm is informed: in symmetric environments, the swarm is able to break the symmetry and aggregates on one of the two types of site at random, not necessarily on the target site, while in the asymmetric environment, the swarm tends to aggregate on the sites that are most represented in terms of area. As a further valuable contribution of this study, we provide analytical results by studying a system of Ordinary Differential Equations' (ODEs) that is an extension of a well known model. Using this model, we show how, for certain values of the parameters, the model can predict the dynamics observed with simulated robots in one of the two symmetric scenarios., Comment: Submitted Neural Computing and Applications

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.

International audience; While direct local communication is very important for the organization of robot swarms, so far it has mostly been used for relatively simple tasks such as signaling robots preferences or states. Inspired by the emergence of meaning found in natural languages, more complex communication skills could allow robot swarms to tackle novel situations in ways that may not be a priori obvious to the experimenter. This would pave the way for the design of robot swarms with higher autonomy and adaptivity. The state of the art regarding the emergence of communication for robot swarms has mostly focused on offline evolutionary approaches, which showed that signaling and communication can emerge spontaneously even when not explicitly promoted. However, these approaches do not lead to complex, language-like communication skills, and signals are tightly linked to environmental and/or sensory-motor states that are specific to the task for which communication was evolved. To move beyond current practice, we advocate an approach to emergent communication in robot swarms based on language games. Thanks to language games, previous studies showed that cultural self-organization-rather than biological evolution-can be responsible for the complexity and expressive power of language. We suggest that swarm robotics can be an ideal test-bed to advance research on the emergence of language-like communication. The latter can be key to provide robot swarms with additional skills to support self-organization and adaptivity, enabling the design of more complex collective behaviors.

This paper studies the target searching problem using swarms of unmanned aerial vehicles (UAVs) in unknown environments which information is unknown to the UAVs, other than features they detect through their sensors. Effective decision and control methods are required for UAVs that consider their limitations and characteristics when confronted with target searching problems. A cooperative target searching method is proposed for swarm UAVs based on an improved bean optimization algorithm (BOA) called Robot Bean Optimization Algorithm (RBOA). Compared with conventional BOAs used for optimal computation, RBOA has two main modifications for the cooperative control of swarm robots: 1) it accounts for the free motion space of individual UAVs using a Thiessen polygon; and 2) it adds a free space search mechanism to improve the efficiency of target searching. Based on the above improvements, and by integrating a multi-phase search mechanism and scheduling control strategy, a swarm UAV collaborative search simulation platform is built for experimental purposes. The results obtained from search simulations show that the RBOA can outperform adaptive robotic particle swarm optimization (A-RPSO) in target searches in complex and unknown environments, especially with fewer evolutionary generations and smaller numbers of robots. The RBOA, which is inspired by plant population evolutionary patterns, has fast and effective search capabilities, distributed collaborative interaction, and emergent swarm intelligence. It provides new ideas and support for research into the control of swarm UAVs and swarm robots.

In swarm robotics, self-organized aggregation refers to a collective process in which robots form a single aggregate in an arbitrarily chosen aggregation site among those available in the environment, or just in an arbitrarily chosen location. Instead of focusing exclusively on the formation of a single aggregate, in this study we discuss how to design a swarm of robots capable of generating a variety of final distributions of the robots to the available aggregation sites. We focus on an environment with two possible aggregation sites, A and B. Our study is based on the following working hypothesis: robots distribute on site A and B in quantities that reflect the relative proportion of robots in the swarm that selectively avoid A with respect to those that selectively avoid B. This is with an as minimal as possible proportion of robots in the swarm that selectively avoid one or the other site. We illustrate the individual mechanisms designed to implement the above mentioned working hypothesis, and we discuss the promising results of a set of simulations that systematically consider a variety of experimental conditions.

Cooperation between individuals plays a very important role when swarm robots search for targets. In this article, we present a novel approach that is based on the distributed particle swarm optimization (DPSO) algorithm to guide swarm robots to search for targets. Both the communication limit and the communication energy consumption (CEC) of the robots are considered. In the proposed approach, robot representatives are selected to represent all of the robots to transfer data to the base stations. The initial deployment and relocation approaches of the base stations are introduced to shorten the transmission distance of the data and to improve the search performance. In addition, a dynamic swarm division method is proposed to efficiently handle cases in which there is more than one target that must be searched for simultaneously. The effectiveness of the proposed approach is verified by some experiments. Simulation results have demonstrated that the proposed approach performs well against other comparative algorithms in various cases.

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.

In swarm robotic systems, task allocation is a challenging problem aiming to decompose complex tasks into a series of subtasks. We propose a self-organizing method to allocate a swarm of robots to perform a foraging task consisting of sequentially dependent subtasks. The method regulates the proportion of robots to meet the task demands for given tasks. Our proposed method is based on the response threshold model, mapping the intensity of task demands to the probability of responding to candidate tasks depending on the response threshold. Each robot is suitable for all tasks but some robots have higher probability of taking certain tasks and lower probability of taking others. In our task allocation method, each robot updates its response threshold depending on the associated task demand as well as the number of neighbouring robots performing the task. It relies neither on a centralized mechanism nor on information exchange amongst robots. Repetitive and continuous task allocations lead to the desired task distribution at a swarm level. We also analyzed the mathematical convergence of the task distribution among a swarm of robots. We demonstrate that the method is effective and robust for a foraging task under various conditions on the number of robots, the number of tasks and the size of the arena. Our simulation results may support the hypothesis that social insects use a task allocation method to handle the foraging task required for a colony’s survival.

Nowadays artificial intelligence and swarm robotics become wide spread and take their approach in civil tasks. The main purpose of the article is to show the influence of common knowledge about surroundings sharing in the robotic group navigation problem by implementing the data transferring within the group. Methodology provided in article reviews a set of tasks implementation of which improves the results of robotic group navigation. The main questions for the research are the problems of robotics vision, path planning, data storing and data exchange. Article describes the structure of real-time laser technical vision system as the main environment-sensing tool for robots. The vision system uses dynamic triangulation principle. Article provides examples of obtained data, distance-based methods for resolution and speed control. According to the data obtained by provided vision system were decided to use matrix-based approach for robots path planning, it inflows the tasks of surroundings discretization, and trajectory approximation. Two network structure types for data transferring are compared. Authors are proposing a methodology for dynamic network forming based on leader changing system. For the confirmation of theory were developed an application of robotic group modeling. Obtained results show that common knowledge sharing between robots in-group can significantly decrease individual trajectories length.

The ability to reliably detect faults is essential in many real-world tasks that robot swarms have the potential to perform. Most studies on fault detection in swarm robotics have been conducted exclusively in simulation, and they have focused on a single type of fault or a specific task. In a series of previous studies, we have developed a robust fault-detection approach in which robots in a swarm learn to distinguish between normal and faulty behaviors online. In this paper, we assess the performance of our fault-detection approach on a swarm of seven physical mobile robots. We experiment with three classic swarm robotics tasks and consider several types of faults in both sensors and actuators. Experimental results show that the robots are able to reliably detect the presence of hardware faults in one another even when the swarm behavior is changed during operation. This paper is thus an important step toward making robot swarms sufficiently reliable and dependable for real-world applications.

Due to the recent advances in robotics, large numbers of robots can be created that exhibit ‘swarm-like’ behaviour. These robots, typically small and low-cost with restricted sensing, often exhibit Brownian motion similar to micro-particles. The development of algorithms that create collective behaviour that is robust to external pressures has applications in outdoor exploration, search and rescue operations, and nanomedicine. Here, we outline how a swarm of minimal robots, exhibiting only Brownian motion and with limited sensing capabilities, can form trails using mechanisms inspired by diffusion-limited aggregation (DLA). We demonstrate how the trail is robust to obstacles and efficient at finding the closest target. We validate this algorithm both in simulation as well as in reality, using a swarm of up to 100 robots, and highlight the optimum requirements for trail formation.

Human intelligence plays a significant role in the operation of a multi-agent system. This study proposes a control framework that allows a human operator to collaboratively interact with a swarm robot to accomplish environmental exploration, detection, and coverage. A ri-limited Voronoi partition is proposed herein for improving the all-territory sensing range for coverage control. Subsequently, an interactive control framework and control algorithms are presented for an abstract task function that allows a human operator to control the movement of a swarm robot in a working environment. Environmental information is fed back to the master devices so that the human operator can realize the swarm robots coverage control situation. Stability and position tracking with static coverage control and input-to-state stability with dynamic coverage control of the human-swarm system are investigated. The efficiency and efficacy of the proposed system are validated via numerical examples and experiments.

Flocking is a social animals’ common behaviour observed in nature. It has a great potential for real-world applications such as exploration in agri-robotics using low-cost robotic solutions. In this paper, an extended model of a self-organised flocking mechanism using heterogeneous swarm system is proposed. The proposed model for swarm robotic systems is a combination of a collective motion mechanism with obstacle avoidance functions, which ensures a collision-free flocking trajectory for the followers. An optimal control model for the leader is also developed to steer the swarm to a desired goal location. Compared to the conventional methods, by using the proposed model, the swarm network has less requirement for power and storage. The feasibility of the proposed self-organised flocking algorithm is validated by realistic robotic simulation software.

In this paper we study a generalized case of best-of-n model, which considers three kind of agents: zealots, individuals who remain stubborn and do not change their opinion; informed agents, individuals that can change their opinion, are able to assess the quality of the different options; and uninformed agents, individuals that can change their opinion but are not able to assess the quality of the different opinions. We study the consensus in different regimes: we vary the quality of the options, the percentage of zealots and the percentage of informed versus uninformed agents. We also consider two decision mechanisms: the voter and majority rule. We study this problem using numerical simulations and mathematical models, and we validate our findings on physical kilobot experiments. We find that (1) if the number of zealots for the lowest quality option is not too high, the decision-making process is driven toward the highest quality option; (2) this effect can be improved increasing the number of informed agents that can counteract the effect of adverse zealots; (3) when the two options have very similar qualities, in order to keep high consensus to the best quality it is necessary to have higher proportions of informed agents.

Swarm intelligence is the study of natural biological systems with the ability to transform simple local interactions into complex global behaviours. Swarm robotics takes these principles and applies them to multi-robot systems with the aim of achieving the same level of complex behaviour which can result in more robust, scalable and flexible robotic solutions than singular robot systems. This research concerns how cooperative multi-robot systems can be utilised to solve real world challenges and outperform existing techniques. The majority of this research is focused around an emergency ship hull repair scenario where a ship has taken damage and sea water is flowing into the hull, decreasing the stability of the ship. A bespoke team of simulated robots using novel algorithms enable the robots to perform a coordinated ship hull inspection, allowing the robots to locate the damage faster than a similarly sized uncoordinated team of robots. Following this investigation, a method is presented by which the same team of robots can use self-assembly to form a structure, using their own bodies as material, to cover and repair the hole in the ship hull, halting the ingress of sea water. The results from a collaborative nature-inspired scenario are also presented in which a swarm of simple robots are tasked with foraging within an initially unexplored bounded arena. Many of the behaviours implemented in swarm robotics are inspired by biological swarms including their goals such as optimal distribution within environments. In this scenario, there are multiple items of varying quality which can be collected from different sources in the area to be returned to a central depot. The aim of this study is to imbue the robot swarm with a behaviour that will allow them to achieve the most optimal foraging strategy similar to those observed in more complex biological systems such as ants. The author’s main contribution to this study is the implementation of an obstacle avoidance behaviour which allows the swarm of robots to behave more similarly to systems of higher complexity.

Mobile robots are widely used in many applications, while various types of mobile robots and their control researches have been proposed in literature. Since swarm robots have higher flexibility and capacity for teamwork, this paper presents a grey estimator-based tracking controller for formation trajectory tracking of swarm robots. First, wheel-type mobile robots are used and modeled for the controller design. Then, a grey dynamic estimator is developed to estimate the environmental disturbance and model uncertainty for linear feedback compensation. As a result, the asymptotic trajectory tracking is assured, so that the application on the swarm robot formation is achieved for a multi-agent system. The computational complexity is slightly reduced by the design. Finally, in order to verify the reliability of swarm robot formation, several types of formation are maintained by the grey estimator-based feedback linearization controller.

Collaboration between robots provides solutions for transporting more complex and heavier loads. In this work, inspired by the ant colony foraging and transport, we put forward two collaborative models, Coupled-Carriers and Navigator-Carrier, for aerial cooperative transport. To achieve this, a linear quadratic regulator (LQR) is applied to optimize the performance. The results show the task of dual-drone transport of a bar load is successfully accomplished.

Swarm robotics is an approach in collective robotics inspired by the self-organized behavior of social animals. Through simple rules and local interactions, swarm robotics aims at designing robust, scalable, and flexible collective behaviors for the coordination of large numbers of robots. In this study, swarm behavior has been performed with limited communication and decentralized management in the simulation environment. We can list these swarm behaviors as follows; formation, coordinated navigation by protecting the formation, separation-reunion, passing through the passageway, leader selection, leader following. In our proposed method, each UAV is swarm determines the situation they are in according to its own position, the position of neighbor UAVs, the position of the goal, and if there is a strait, the position and size of the strait. UAVs calculate vectors that perform different tasks according to the situation they determine at the moment, and combine these vectors to decide their next move.

Local interactions and communication are key features in swarm robotics, but they are most often fixed at design time, limiting flexibility and causing a stiff and inefficient response to changing environments. Motivated by the need for higher adaptation abilities, we propose that information about emergent collective structures should percolate onto the individual behavior, modifying it in a way that determines suitable responses in the face of new working conditions and organizational challenges. Indeed, complex societies are driven by an evolving set of individual and social norms subject to cultural propagation, which contribute to determining the individual behaviors. We leverage ideas from the evolution of natural language – an undoubtedly efficient cultural trait – and exploit the resulting social dynamics to select and propagate microscopic behavioral parameters that adapt continuously to macroscopic conditions, which in turn affect the agents’ communication topography, and, therefore, feed back onto the social dynamics. This concept is demonstrated on a self-organized aggregation behavior, which is a building block for most swarm robotics behaviors and a striking example of how collective dynamics are sensitive to experimental parameters. By means of experiments with simulated and real robots, we show that the cultural evolution of aggregation rules outperforms conventional approaches in terms of adaptivity to multiple experimental settings.

Target searching and tracking using a swarm of robots is a classical problem in the domain of robotics. The cooperation among the swarm robots has got increasing attention lately due to the different versions of the problem and complex environment. In this paper, a centralized control algorithm is proposed which utilizes cooperation among the swarm of robots for searching and tracking targets which is the velocity inspired robotic fruit fly algorithm (VRFA). The particle velocity concept of the particle swarm optimization is added to the fruit fly algorithm to improve the parameters such as local extremum and low convergence. The simulation results of the proposed technique in different scenarios demonstrate the effectiveness of the algorithm and its ability to keep tracking the targets until the exit condition matched. At last, the simulation result is shown with different environments and parameter settings.