Search

We propose an approach to guide simple robots through arbitrary environments while engaged in a planar construction task. The intent is to mitigate spatial interference between robots which often occurs in distributed robots operating on the same set of objects. Information can also be encoded within the structure of the labyrinth to provide auxiliary guidance, such as the direction in which objects should be incrementally moved to reach their goals. We show results in a simulation environment and study the impact of the number of robots deployed. We also provide validation on physical robots. [ABSTRACT FROM AUTHOR]

Embodied evolution is an evolutionary robotics approach that implements an evolutionary algorithm over a population of robots and evolves while the robots perform their tasks. So far, most studies on embodied evolution utilize relatively simple neural networks as robot controllers. However, a simple structured controller might restrict robot behavior and lead to lower performance. This paper proposes an embodied evolution approach that uses echo state networks as robot controllers. The experiments are conducted using computer simulations, and the controllers are evolved in a two-target navigation task. The results show that the echo state network controllers outperform the conventional controllers. [ABSTRACT FROM AUTHOR]

The article presents a method for distributing tasks to agents of a heterogeneous UAV group in a cluster field of tasks, when the number of tasks exceeds the number of agents by 5–20 times. The proposed task distribution method based on a three-stage procedure for distributing agents of different specializations among task clusters, taking into account the agent value function. To evaluate the effectiveness, the method compared with the greedy task distribution algorithm, the collective plan improvement algorithm, and the consensus-based linking algorithm with local rescheduling. 2400 experiments were carried out with different group sizes and randomly generated task maps, the results of which revealed the high efficiency of the proposed method. According to the results of the study, a relationship found between the efficiency of the method depending on the concentration of the number of tasks per agent. With an increase in the specific number of tasks per agent, the task execution time improves and the indicator of the path traveled by agents worsens. With a ratio of 5–10 agents per 100 tasks, the method shows the best results in terms of the parameters of the path traveled by agents and task execution time.

The field of multi-robot systems (MRS) has recently been gaining increasing popularity among various research groups, practitioners, and a wide range of industries. Compared to single-robot systems, multi-robot systems are able to perform tasks more efficiently or accomplish objectives that are simply not feasible with a single unit. This makes such multi-robot systems ideal candidates for carrying out distributed tasks in large environments—e.g., performing object retrieval, mapping, or surveillance. However, the traditional approach to multi-robot systems using global planning and centralized operation is, in general, ill-suited for fulfilling tasks in unstructured and dynamic environments. Swarming multi-robot systems have been proposed to deal with such steep challenges, primarily owing to its adaptivity. These qualities are expressed by the system’s ability to learn or change its behavior in response to new and/or evolving operating conditions. Given its importance, in this perspective, we focus on the critical importance of adaptivity for effective multi-robot system swarming and use it as the basis for defining, and potentially quantifying, swarm intelligence. In addition, we highlight the importance of establishing a suite of benchmark tests to measure a swarm’s level of adaptivity. We believe that a focus on achieving increased levels of swarm intelligence through the focus on adaptivity will further be able to elevate the field of swarm robotics.

Bug Algorithms in robotics field play an important role in path planning. The main challenge in conventional bug algorithms is searching the cluttered environment. To solve this problem a method is introduced which uses the concept of swarm robotics that helps in finding path using coordination between robots in swarm. The challenge in this research article is to find a path which is heuristically optimal. A type of bug algorithm is introduced in which parent bug sends two of its child bugs. Each of them has capability of searching in different directions. After searching the path from both sides, parent bug follows the path which is heuristically optimal. Parent and child bugs are equipped with tactile sensors to follow the perimeter of an obstacle. Illustrative simulation results show two test cases in which different scenarios are presented. Results are compared with of bug0 algorithm that is visualized in configuration space as well as in workspace to find the heuristically optimal path.

This study investigates the incorporation of swarm robotics into the control mechanism of electric vehicles (EVs), introducing an innovative intelligent control framework that utilizes the concepts of decentralized decision-making. The research entails a methodical inquiry that encompasses the design of system architecture, the creation of a model for swarm robotics, the modeling of electric vehicle drive, the integration of swarm robotics with EV control, the development of algorithms for intelligent control, and the execution of real-world tests. The fleet of electric cars, propelled by a collective of independent robotic entities, displayed remarkable flexibility in adjusting to fluctuating surroundings. Findings demonstrated disparities in operating duration, distance traversed, mean speed, and energy expenditure during several iterations, highlighting the system’s adeptness in promptly reacting to instantaneous inputs. Significantly, the swarm-propelled electric cars successfully attained varied operating durations, showcasing the system’s adaptability in accommodating environmental dynamics. The swarm-driven system demonstrated its navigation effectiveness by effectively covering various distances, highlighting its versatility and extensive coverage capabilities. The system’s ability to effectively balance energy economy and performance is shown by the collective regulation of average velocity. The energy consumption study demonstrated the system’s efficacy in optimizing energy use, with certain experiments showing significant savings. Percentage change studies have yielded valuable insights into the comparative enhancements or difficulties seen in each indicator, so illustrating the influence of decentralized decision-making on operational results. This study is a valuable contribution to the ever-changing field of intelligent transportation systems, providing insight into the immense potential of swarm-driven electric cars to completely transform sustainable and adaptable transportation. The results highlight the remarkable flexibility and optimization skills of swarm robotics in the management of electric vehicles, paving the way for future advancements in the quest for intelligent, energyefficient, and dynamically responsive transportation solutions.

Swarm robotics epitomizes a frontier in cooperative control within multi-agent systems, where the emulation of biological swarms offers a paradigm shift in robotics. This paper delves into the mechanisms of decentralized decision-making and the emergent behaviors that arise from local interactions among autonomous robotic agents without the need for a central controller. It explores the synthesis of simple control rules that yield complex, adaptive, and scalable group behaviors, akin to those found in natural swarms. A critical examination of communication protocols elucidates how information-sharing among agents leads to the robust execution of collective tasks. The research further investigates the dynamics of role allocation, task partitioning, and redundancy, which are crucial for the resilience of swarm robotic systems. Through simulation and empirical analysis, the efficacy of swarm algorithms in various applications, including search and rescue, environmental monitoring, and collective construction, is demonstrated. The study's findings underscore the significance of bio-inspired algorithms and the potential of swarm robotic systems to adapt and thrive in unpredictable environments. The implications for the future of autonomous systems are profound, as swarm robotics paves the way for innovations in distributed artificial intelligence and robotic.

Prey-predator interactions play a pivotal role in elucidating the evolution and adaptation of various organism’s traits. Numerous approaches have been employed to study the dynamics of prey-predator interaction systems, with agent-based methodologies gaining popularity. However, existing agent-based models are limited in their ability to handle multi-modal interactions, which are believed to be crucial for understanding living organisms. Conversely, prevailing prey-predator integration studies often rely on mathematical models and computer simulations, neglecting real-world constraints and noise. These elusive attributes, challenging to model, can lead to emergent behaviors and embodied intelligence. To bridge these gaps, our study designs and implements a prey-predator interaction scenario that incorporates visual and olfactory sensory cues not only in computer simulations but also in a real multi-robot system. Observed emergent spatial-temporal dynamics demonstrate successful transitioning of investigating prey-predator interactions from virtual simulations to the tangible world. It highlights the potential of multi-robotics approaches for studying prey-predator interactions and lays the groundwork for future investigations involving multi-modal sensory processing while considering real-world constraints.

The area coverage problem solution is one of the vital research areas which can benefit from swarm robotics. The greatest challenge to the swarm robotics system is to complete the task of covering an area effectively. Many domains where area coverage is essential include exploration, surveillance, mapping, foraging, and several other applications. This paper introduces a survey of swarm robotics in area coverage research papers from 2015 to 2022 regarding the algorithms and methods used, hardware, and applications in this domain. Different types of algorithms and hardware were dealt with and analysed; according to the analysis, the characteristics and advantages of each of them were identified, and we determined their suitability for different applications in covering the area for many goals. This study demonstrates that naturally inspired algorithms have the most significant role in swarm robotics for area coverage compared to other techniques. In addition, modern hardware has more capabilities suitable for supporting swarm robotics to cover an area, even if the environment is complex and contains static or dynamic obstacles.

Mobile multi-robot systems are well suited for gas leak localization in challenging environments. They offer inherent advantages such as redundancy, scalability, and resilience to hazardous environments, all while enabling autonomous operation, which is key to efficient swarm exploration. To efficiently localize gas sources using concentration measurements, robots need to seek out informative sampling locations. For this, domain knowledge needs to be incorporated into their exploration strategy. We achieve this by means of partial differential equations incorporated into a probabilistic gas dispersion model that is used to generate a spatial uncertainty map of process parameters. Previously, we presented a potential-field-control approach for navigation based on this map. We build upon this work by considering a more realistic gas dispersion model, now taking into account the mechanism of advection, and dynamics of the gas concentration field. The proposed extension is evaluated through extensive simulations. We find that introducing fluctuations in the wind direction makes source localization a fundamentally harder problem to solve. Nevertheless, the proposed approach can recover the gas source distribution and compete with a systematic sampling strategy. The estimator we present in this work is able to robustly recover source candidates within only a few seconds. Larger swarms are able to reduce total uncertainty faster. Our findings emphasize the applicability and robustness of robotic swarm exploration in dynamic and challenging environments for tasks such as gas source localization.

Swarm robotics is a promising approach to control large groups of robots. However, designing the individual behavior of the robots so that a desired collective behavior emerges is still a major challenge. In recent years, many advances in the automatic design of control software for robot swarms have been made, thus making automatic design a promising tool to address this challenge. In this article, I highlight and discuss recent advances and trends in offline robot evolution, embodied evolution, and offline robot learning for swarm robotics. For each approach, I describe recent design methods of interest, and commonly encountered challenges. In addition to the review, I provide a perspective on recent trends and discuss how they might influence future research to help address the remaining challenges of designing robot swarms.

Collaborative robots represent an evolution in the field of swarm robotics that is pervasive in modern industrial undertakings from manufacturing to exploration. Though there has been much work on path planning for autonomous robots employing floor plans, energy-efficient navigation of autonomous robots in unknown environments is gaining traction. This work presents a novel methodology of low-overhead collaborative sensing, run-time mapping and localization, and navigation for robot swarms. The aim is to optimize energy consumption for the swarm as a whole rather than individual robots. An energy- and information-aware management algorithm is proposed to optimize the time and energy required for a swarm of autonomous robots to move from a launch area to the predefined destination. This is achieved by modifying the classical Partial Swarm SLAM technique, whereby sections of objects discovered by different members of the swarm are stitched together and broadcast to members of the swarm. Thus, a follower can find the shortest path to the destination while avoiding even far away obstacles in an efficient manner. The proposed algorithm reduces the energy consumption of the swarm as a whole due to the fact that the leading robots sense and discover respective optimal paths and share their discoveries with the followers. The simulation results show that the robots effectively re-optimized the previous solution while sharing necessary information within the swarm. Furthermore, the efficiency of the proposed scheme is shown via comparative results, i.e., reducing traveling distance by 13% for individual robots and up to 11% for the swarm as a whole in the performed experiments. [ABSTRACT FROM AUTHOR]

There has been growing interest in the use of multi-robot systems in various tasks and scenarios. The main attractiveness of such systems is their flexibility, robustness, and scalability. An often overlooked yet promising feature is system modularity, which offers the possibility of harnessing agent specialization, while also enabling system-level upgrades. However, altering the agents' capacities can change the exploration–exploitation balance required to maximize the system's performance. Here, we study the effect of a swarm's heterogeneity on its exploration–exploitation balance while tracking multiple fast-moving evasive targets under the cooperative multi-robot observation of multiple moving targets framework. To this end, we use a decentralized search and tracking strategy with adjustable levels of exploration and exploitation. By indirectly tuning the balance, we first confirm the presence of an optimal balance between these two key competing actions. Next, by substituting slower moving agents with faster ones, we show that the system exhibits a performance improvement without any modifications to the original strategy. In addition, owing to the additional amount of exploitation carried out by the faster agents, we demonstrate that a heterogeneous system's performance can be further improved by reducing an agent's level of connectivity, to favor the conduct of exploratory actions. Furthermore, in studying the influence of the density of swarming agents, we show that the addition of faster agents can counterbalance a reduction in the overall number of agents while maintaining the level of tracking performance. Finally, we explore the challenges of using differentiated strategies to take advantage of the heterogeneous nature of the swarm. [ABSTRACT FROM AUTHOR]

With the continuous advancement of robotics technology, the integration of robots into diverse human environments has become increasingly prevalent. However, the presence of robots in public spaces can often elicit discomfort or unease among individuals. To address this concern, the concept of concealing robots in various settings has emerged as an innovative approach to improve robot navigation and interaction while minimizing intrusion on human privacy. This paper explores the motivations, challenges, and potential benefits of hiding robots in different environments, particularly within the context of swarm robotics where multiple interconnected robots form a cohesive swarm. Equipped with onboard processing, communication, and sensing capabilities, these robots can autonomously interact with each other and adapt to the environment. The paper investigates the problem of maximizing the number of hidden orthogonal swarm robots, considering scenarios in which robots need to navigate and operate within polygonal environments. Specifically, it presents a 4-approximation algorithm for computing a maximum hidden robot set in such environments. The algorithm provides a practical solution for efficiently arranging robots, minimizing their visibility, and ensuring effective swarm operation. It also presents an O(n 2)-time 4-approximation algorithm to compute the maximum hidden robot set in simple polygons. The algorithm utilizes a working space of O(n), where n represents the complexity of the environment provided as input. Concealing robots in diverse environments offers several benefits. Firstly, it alleviates discomfort or unease among individuals, facilitating smoother integration of robots into public spaces. Additionally, concealing robots enhances their navigation capabilities by leveraging stealth techniques, allowing them to move seamlessly and unobtrusively within the environment. This approach also promotes improved human-robot interaction, as the reduced visibility of the robots can alleviate concerns and foster a more natural and comfortable interaction between humans and robots. Furthermore, it presents insights into future directions, including the development of more advanced stealth technologies, ethical frameworks for integrating hidden robots, and the potential impact on urban planning and infrastructure. In conclusion, hiding robots in diverse environments offers a promising approach to enhancing robot navigation, interaction, and privacy [ABSTRACT FROM AUTHOR]

Robots such as drones, ground rovers, underwater vehicles and industrial robots have increased in popularity in recent years. Many sectors have benefited from this by increasing productivity while also decreasing costs and certain risks to humans. These robots can be controlled individually but are more efficient in a large group, also known as a swarm. However, an increase in the quantity and complexity of robots creates the need for an adequate control system. Reinforcement learning, an artificial intelligence paradigm, is an increasingly popular approach to control a swarm of unmanned vehicles. The quantity of reviews in the field of reinforcement learning-based swarm robotics is limited. We propose reviewing the various applications, algorithms and simulators on the subject to fill this gap. First, we present the current applications on swarm robotics with a focus on reinforcement learning control systems. Subsequently, we define important reinforcement learning terminologies, followed by a review of the current state-of-the-art in the field of swarm robotics utilizing reinforcement learning. Additionally, we review the various simulators used to train, validate and simulate swarms of unmanned vehicles. We finalize our review by discussing our findings and the possible directions for future research. Overall, our review demonstrates the potential and state-of-the-art reinforcement learning-based control systems for swarm robotics. [ABSTRACT FROM AUTHOR]

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

Common knowledge, that is, a common understanding of environmental conditions, task objectives, coordination rules, etc., can greatly improve the collaborative efficiency of swarm robots. In many complex task scenarios, it is impossible to assume there is a central facility (e.g., a powerful robot or a back-end server that can communicate effectively with everyone) responsible for maintaining the collective's common knowledge. Instead, we must maintain it in a decentralized way. Blockchain has been proved to be an effective means of meeting this demand. It can even tolerate malicious or malfunctioning individuals to a certain extent, which is an important capability for swarm robots to operate in an open or hostile environment. However, current widely-accepted Blockchain techniques, such as Ethereum, use the proof-of-work mechanism as the basis of reaching consensus, which has to consume huge computing resources and is not suitable for swarm robots. In this paper, we present a fast and robust solution for maintaining common knowledge in swarm robots based on Hashgraph, a lightweight consensus technology being originally proposed for fully-connected, well-conditioned networks. We successfully improve its kernel mechanisms to adapt it to swarm robots with limited communication capabilities. And we novelly introduce the concept of Ranger Robot, a special kind of robot that can significantly accelerate the formation of consensus in sparsely-distributed or physically-partitioned robot swarms. Furthermore, we design a knowledge validation algorithm to enable the robot swarm to recognize attacks from a special kind of malicious robot called Byzantine robots. The results of a set of experiments based on both simulated and real robots show that our solution can greatly reduce computing overhead and accelerate the formation of consensus in comparison with solutions based on the original Hashgraph. [ABSTRACT FROM AUTHOR]

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

This paper demonstrates to generate a collective behavior of a multi-legged robotic swarm based on the evolutionary robotics approach. Most studies in swarm robotics are conducted using mobile robots driven by wheels. This paper focuses on generating collective behavior using a multi-legged robotic swarm. The evolutionary robotics approach is employed for designing a robot controller. The intuition-based constraint factors are incorporated into the fitness function to make the gait of robots similar to natural organisms. The experiment on a task of forming a line is conducted in computer simulations using the PyBullet physics engine. The robot controller is represented by a recurrent neural network with a single hidden layer. The experimental results show that proposed constraint factors successfully designed the robot's gait similar to natural organisms. The results also show that the evolutionary robotics approach successfully designed the robot controller for collective behavior of a multi-legged robotic swarm. [ABSTRACT FROM AUTHOR]

Swarm robotics requires a practical scheme to maintain operator supervision for the acceptability of systems' autonomy by humans. For this purpose, this paper proposes a distributed algorithm for continuous connectivity between robots and a base station to maintain the controllability and transparency of the swarm. This algorithm forms network topology among the swarm members and deploys repeaters to maintain the connection to the base station by a role switching scheme. Preliminary simulations have shown that the revised acute angle test reduced the cost of the network formation with the Gabriel graph topology. Through the simulated patrol missions, the proposed algorithm successfully maintained the continuous connectivity between the base station and the swarm members without significant inequality in the computational cost among swarm members. Furthermore, as the number of robots increases, the computational cost per robot does not increase significantly. These results indicate the distributed nature and scalability of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Establishing a large adaptive connected network for decentralized swarms is useful for their behavior to share information about the working environment. A hard-coded implementation is time-consuming to achieve. Therefore, we are motivated to explore the benefits of reinforcement learning (RL) to learn a suitable adaptive policy. We also explore the combined use of a scalar field, which was inspired by template pheromones in social insects. In this paper, we investigate using RL with low and high-resolution scalar fields to solve the largest covering network problem. Our results show that RL outperforms the hard-coded approach in the presence of the high-resolution scalar field. [ABSTRACT FROM AUTHOR]

Users and operators of swarms will, in the future, need to monitor the operations of swarms in a distributed way, without explicitly tracking every agent, and without the need for significant infrastructure or set up. Here we present a method for swarm self-monitoring that enables the aggregate display of information about swarm location by making use of physical transport of information and local communication. This method uses movement already exhibited by many swarms to collect self-reflective information in a fully distributed manner. We find that added swarm mobility can compensate for limited communication and that our self-monitoring swarm system scales well, with performance increasing with the size of the swarm in some cases. When developing systems such as this for real-world applications, individual agent memory will need to be taken into consideration, finding an effective means to spread swarm knowledge among robots while keeping information accessible to users. [ABSTRACT FROM AUTHOR]

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

The self-reconfigurable modular robotic system is a class of robots that can alter its configuration by rearranging the connectivity of their component modular units. The reconfiguration deformation planning problem is to find a sequence of reconfiguration actions to transform one reconfiguration into another. In this paper, a hybrid reconfiguration deformation planning algorithm for modular robots is presented to enable reconfiguration between initial and goal configurations. A hybrid algorithm is developed to decompose the configuration into subconfigurations with maximum commonality and implement distributed dynamic mapping of free vertices. The module mapping relationship between the initial and target configurations is then utilized to generate reconfiguration actions. Simulation and experiment results verify the effectiveness of the proposed algorithm.

In swarm-robotics foraging, the purpose of task allocation is to adjust the number of active foraging robots dynamically based on the task demands and changing environment. It is a difficult challenge to generate self-organized foraging behavior in which each robot can adapt to environmental changes. To complete the foraging task efficiently, this paper presents a novel self-organized task allocation strategy known as the dynamic response threshold model (DRTM). To adjust the behavior of the active foraging robots, the proposed DRTM newly introduces the traffic flow density, which can be used to evaluate the robot density. Firstly, the traffic flow density and the amount of obstacle avoidance are used to adjust the threshold which determines the tendency of a robot to respond to a stimulus in the environment. Then, each individual robot uses the threshold and external stimulus to calculate the foraging probability that determines whether or not to go foraging. This paper completes the simulation and physical experiments, respectively, and the performance of the proposed method is evaluated using three commonly used performance indexes: the average deviation of food, the energy efficiency, and the number of obstacle avoidance events. The experimental results show that the DRTM is superior to and more efficient than the adaptive response threshold model (ARTM) in all three indexes.