Swarm Robotic Systems Research Articles

Research on Data Security Communication Scheme of Heterogeneous Swarm Robotics System in Emergency Scenarios.

In emergency scenarios where the on-site information is completely lacking or the original environmental state has been completely changed, autonomous and mobile swarm robotics are used to quickly build a rescue support system to ensure the safety of follow-up rescuers and improve rescue efficiency. To address the data security problem caused by the complex and changeable topology of the heterogeneous swarm robotics network in the process of building the rescue support system, this paper introduced a decentralized data security communication scheme for heterogeneous swarm robotics. First, we built a decentralized network topology model by using base robot, communication robotics, and business robotics, and it can ensure the stability of the system. Moreover, based on the decentralized network topology model, we designed a storage model using the master–slave blockchain method. The master chain is composed of base robot and communication robotics, which mainly store the digests of robot data in multiple slave chains to reach the global data consensus of the system. The slave chains are composed of business robotics and communication robotics, which mainly store all data on the slave chains to reach the local data consensus of the system. The whole data storage system adopts the Delegated Proof of Stake consensus mechanism to elect proxy nodes to participate in the data consensus tasks in the system and to ensure the data consistency of each robot node in the decentralized network. Additionally, a prototype of the heterogeneous swarm robotics system based on the master–slave chains is constructed to verify the effectiveness of the proposed model. The experimental results show that the scheme effectively solves the data security problem caused by the unstable communication link of the heterogeneous swarm robotics system.

Wave-Type Interaction within a Robotic Swarm System for Decentralized Estimation of Global Geometric States

For a robotic swarm system composed of autonomous mobile robots, controlling and using asymmetric global geometric states promotes the task performance of the swarm. This paper presents a systematic method for estimating asymmetric global geometric states over a swarm system. To overcome the limitations of local observation or communication ability, we propose a wave-type interaction among neighboring robots. We assume that each robot has a scalar state variable called a phase, which is manipulated through interactions. Through the analysis of eigenvalues of a graph Laplacian matrix corresponding to a local communication network of robots, we show that a robot can estimate global states, such as the size of an entire swarm, by frequency analysis of its phase. We also analyzed the stability of the wave-type interaction based on von-Neumann stability. We verified the proposed method by computer simulations, in which robots in a swarm detected the deformation in the shape of the swarm when the swarm was passing through a narrow area. The result will contribute to building a control system for swarms that can manipulate their shape or characteristics to adapt themselves based on tasks or environmental requirements.

Robot Swarms as Hybrid Systems: Modelling and Verification

A swarm robotic system consists of a team of robots performing cooperative tasks without any centralized coordination. In principle, swarms enable flexible and scalable solutions; however, designing individual control algorithms that can guarantee a required global behavior is difficult. Formal methods have been suggested by several researchers as a mean to increase confidence in the behavior of the swarm. In this work, we propose to model swarms as hybrid systems and use reachability analysis to verify their properties. We discuss challenges and report on the experience gained from applying hybrid formalisms to the verification of a swarm robotic system.

DEVELOPMENT AND RESEARCH OF DISTRIBUTED CONTROL ALGORITHMS FOR SWARM INTELLIGENCE SYSTEMS

The subject of this paper is the development and research of distributed algorithms for organizing collective behavior in swarm robotic systems in order to solve various applied problems with these systems. Using the example of solving the problem of collective cleaning of a given area, several swarm algorithms based on classical swarm models are constructed and studied: random walk model, Reynolds model, bacterial search algorithm, stochastic gradient method. The results of numerical experiments comparing the efficiency of the proposed methods are presented

Collective fission behavior in swarming systems with density-based interaction

In this paper we revisit the self-organized collective behavior of swarms with density field interaction that is inspired from the model of smoothed particle hydrodynamics. For a homogeneous swarming system a novel collective fission behavior is seen to emerge where equal to or more than two sub-clusters spontaneously come from a single connected cluster. The focus of this study is on the analysis of collective fission behavior in trigger conditions, function manners and dynamic characteristics. For the first time, we are able to predict the stability of a single connected cluster, the trigger conditions of collective fission behavior, the number of sub-clusters and the group size of each sub-cluster. The analysis results also show that the collective fission behavior is irreversible by changing the control parameters and the group size. However, it is reversible when the sensing range is enlarged to encompass all sub-clusters. These characteristics are evaluated in simulations as well as on the real swarm robotic system. The study of collective fission behavior provides an elegant insight for how self-organized segregation emerges in biological swarms and artificial systems.

Formation optimization of blockchain-assisted swarm robotics systems against failures based on energy balance

The blockchain-assisted swarm robotics system, as an emerging distributed multi-robot system, has been widely discussed and studied for its outstanding advantages in secure communication, data logging and consensus achievement. Due to faults, formation changes, and obstructions, the network topology and communication rate change in the process of multi-robot collaboration, which inevitably affect the stability and operation efficiency of the swarm robotics system. However, few studies have quantitatively assessed the degradation of system stability performance after failure and how to respond to failure of team members and optimize the system for failure. In this paper, the concept of “energy” is introduced into the quantitative evaluation of system failure resistance, and an evaluation model based on communication distance, communication quality and communication volume is proposed, which can evaluate the stability of swarm robotics systems at different moments and any member in real-time collaboration. As the stability improves, the “energy” increases, and vice versa. Additionally, the “energy” balance is applied to the optimization process of multi-robot formation, and the optimization equation based on the efficiency of swarm robots is proposed. The optimal formation efficiency equation of the system at a certain moment is obtained by jointly solving the energy optimization equation and the system. The simulation results show that the proposed formation optimization method can improve the stability of the swarm robotics system at any time and in all directions, and maximize the operational efficiency of the queue.

An Approach to Swarm Modular Systems with Robustness to Dynamic Situation Changes

Self-assembly is an important factor for swarms to provide robustness to the system. For swarm robot systems, robots have been considered to realize self-assembly like ants and to adapt to their environment. These robots, however, have been modeled based on living organisms, and there are few studies that have further developed self-assembly in artificial swarm robot systems. This paper proposes a connected swarm system for artificial self-assembly. Firstly, we develop the system in which the robot can switch its driving mode from active to passive, and vice versa.

Access control system to information resources of swarm robotic systems in the presence of intruded malicious agents

A distributed approach to the creation of robotic swarm systems (SRS) will allow solving a wide range of problems in the fields of environmental protection, medicine, cleaning, surveillance, and many others. On the other hand, this approach allows an attacker to intrude malicious agents whose actions aimed at reducing the quality of the entire system. In this regard, the issue of research, design, and testing of these systems from the point of view of information security becomes relevant. Based on this, the purpose of this study is to develop an access control system (ACS) to SRS information resources in the presence of intruded malicious agents to improve the efficiency of SRS functioning when performing spatially distributed tasks. Within the framework of solving the task set, an approach to the synthesis of ACS is proposed, which makes it possible to ensure the continuous detection of malicious agents at all stages of the life cycle of the SRS operation. An element of the scientific novelty of the presented solution is the algorithm for synthesizing an ensemble of heterogeneous classifiers, the main idea of which is a combination of stacking and boosting methods for preparing a training sample and training a meta-classifier. Distinctive features of the proposed algorithm are the ability to use many different classifiers as basic classifiers, as well as the ability to reduce the size of the training set of the meta-classifier by using boosting when selecting samples in the training set. The proposed approach to the synthesis of ACS to SRS information resources using the presented algorithm improves the accuracy of detecting malicious agents both in the process of task distribution and in the process of task execution. The presented solution is implemented in the form of software in the Python programming language, which can be used in modeling decentralized control systems of SRS. The article refers to the scientific specialty 2.3.6 “Methods and systems of information protection, information security”.

Complex network–based pinning control of drone swarm

Controlling the bulk motion of a large-scale drone swarm is a challenging and critical issue in modern swarm robotic systems. We introduce a framework of pinning control based on the self-organized flocking model with a set of selected driver drones, in which different selection strategies for driver drones are considered from the network perspective. Results show that we can control the bulk motion of a drone swarm by only controlling a set of driver drones and, furthermore, a well-selected set of driver drones can accomplish the task with better efficiency and robustness.

Performance Analysis of Fault Tolerance Algorithm for Pattern Formation of Swarm Agents

Swarm robotics is a growing field of swarm intelligence inspired by natural swarms for providing a solution for the collaboration among swarm agents to achieve a common goal. One of the challenges in designing a swarm robotic system with desired collective behaviour is to understand the effect of individual behaviour on group performance. This paper considers a system composed of autonomous swarm agents, each one executing its algorithm. We present a generic fault-tolerant pattern formation algorithm, in which different agents from different positions gather and then form a desired geometric pattern specifically a uniform circle and maintain it in the presence of faulty agents. We present an analysis based on simulations and experimental results of the proposed algorithm on swarm forming the pattern, in which failures are deliberately induced to check their fault tolerance capability and pattern formation capability. Our findings show that the formed geometric pattern can be restored and maintained depending on the different parameters in the presence of faulty agents.

Introducing shell formation and a thermodynamics-inspired concept for swarm robotic systems

In this article, a new formation for swarm robotic systems is introduced. This formation, which is made up of a portion of swarm members and encircles the whole swarm, is called the shell formation. In this regard, an effective algorithm for developing the shell formation in swarm robotic systems is established. The interaction mechanism among swarm agents is based on the method of artificial potential fields and the local rule of the nearest neighbor. Subsequently, inspired by the thermodynamic science and based on the introduced shell formation, the thermodynamic concept of pressure is generalized to swarm robotic systems. Finally, the efficacy of the introduced shell formation in solving the problem of passing through a narrow passage is studied via numerical simulations.

Optimal Transport-Based Coverage Control for Swarm Robot Systems: Generalization of the Voronoi Tessellation-Based Method

Swarm robot systems, which consist of many cooperating mobile robots, have attracted attention for their environmental adaptability and fault tolerance advantages. One of the most important tasks for such systems is coverage control, in which robots autonomously deploy to approximate a given spatial distribution. In this study, we formulate a coverage control paradigm using the concept of optimal transport and propose a novel control technique, which we have termed the optimal transport-based coverage control (OTCC) method. The proposed OTCC, derived via the gradient flow of the cost function in the Kantorovich dual problem, is shown to covers a widely used existing control method as a special case. We also perform a Lyapunov stability analysis of the controlled system, and provide numerical calculations to show that the OTCC reproduces target distributions with better performance than the existing control method.

Design Considerations for Deep-Ocean Scientific Robotic Vehicles

Abstract Oceans cover 72% of the Earth's surface, house immense living and non-living resources, and play a key role in regulating the planet's climate. Robotic vehicles are essential for exploring vast deep-ocean resources, spatiotemporal monitoring of oceans to understand the patterns of climate change, monitoring marine pollution, providing defense, and identifying assets lost in the oceans. The article discusses key design considerations for realizing safe, reliable, and efficient deep-ocean unmanned and manned robotic vehicles capable of operating in challenging environments characterized by high hydrostatic pressure, low temperature, salinity, darkness, dynamic medium, and soft seabed conditions. Strategic technologies to enable cost-effective and increased spatiotemporal monitoring including homing and docking stations, autonomous intervention vehicles, swarm robotic systems, and bio-inspired vehicle designs are discussed.

Constructing a cohesive pattern for collective navigation based on a swarm of robotics.

Swarm robotics carries out complex tasks beyond the power of simple individual robots. Limited capabilities of sensing and communication by simple mobile robots have been essential inspirations for aggregation tasks. Aggregation is crucial behavior when performing complex tasks in swarm robotics systems. Many difficulties are facing the aggregation algorithm. These difficulties are as such: this algorithm has to work under the restrictions of no information about positions, no central control, and only local information interaction among robots. This paper proposed a new aggregation algorithm. This algorithm combined with the wave algorithm to achieve collective navigation and the recruitment strategy. In this work, the aggregation algorithm consists of two main phases: the searching phase, and the surrounding phase. The execution time of the proposed algorithm was analyzed. The experimental results showed that the aggregation time in the proposed algorithm was significantly reduced by 41% compared to other algorithms in the literature. Moreover, we analyzed our results using a one-way analysis of variance. Also, our results showed that the increasing swarm size significantly improved the performance of the group.

Self-Organised Collision-Free Flocking Mechanism in Heterogeneous Robot Swarms

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.

Torch: Strategy evolution in swarm robots using heterogeneous–homogeneous coevolution method

Because swarm robots have been applied widely in various fields, the evolution capability of their strategy have become of primary interest; therefore, the evolution method of swarm robots’ strategy has attracted attention in both industry and academia, especially for complex applications owing to their varied task scenes. Large amounts of researches have been conducted to realize strategy evolution in swarm robotics systems. However, there are few studies on the strategy evolution sufficiently examining the simultaneous improvement of evolutionary and strategy performance, which are two key demands of swarm robots. Besides, the strategy that evolved under the global information is difficult to fully adapt to the distributed task scenarios. To address these issues, this study presents a heterogeneous–homogeneous swarm coevolution method known as TORCH to improve the evolution capability of swarm robots. The method uses a swarm coevolution mechanism to accelerate the evolution. For the first time, we employ a behaviour expression tree in TORCH which expands the strategy search space of the evolved strategies. TORCH makes the swarm robots’ strategies evolve under local information conditions; hence, the evolutionary strategies are more adaptable to the distributed task scenarios. Extensive experiments have been conducted to verify the proposed TORCH, including a comparison with three methods based on the homogeneous swarm evolution method and parameter expression. The results demonstrate the superiority of the TORCH in terms of evolutionary efficiency improvement and strategy performance enhancement.

Quasi-omnidirectional transmitter for underwater wireless optical communication systems using a prismatic array of three high-power blue LED modules.

In this study, a quasi-omnidirectional underwater wireless optical communication (UWOC) system is implemented with a prismatic array consisting of three uniformly distributed high-power LED modules as the transmitter. Over a 10-m underwater channel in a 50-m standard swimming pool, a data rate of 22 Mbps is achieved without adopting any digital signal processing algorithm. With zero forcing (ZF) based frequency domain equalization (FDE) and a maximum ratio combining (MRC) algorithm, the maximum net data rates achieved are 69.65 Mbps, 39.8Mbps and 29.85 Mbps over 10-m, 30-m, and 40-m underwater channels, respectively. In the proposed UWOC system, the receiver could successfully capture optical signals at different directions from the transmitter and the bit error rates (BERs) measured in different directions show small fluctuations. The proposed system could meet the demands of high-speed data transmission among units in a swarm-robot system and last meter user access in an underwater optical cellular network system.

An inchworm-inspired motion strategy for robotic swarms

AbstractEffective motion planning and localization are necessary tasks for swarm robotic systems to maintain a desired formation while maneuvering. Herein, we present an inchworm-inspired strategy that addresses both these tasks concurrently using anchor robots. The proposed strategy is novel as, by dynamically and optimally selecting the anchor robots, it allows the swarm to maximize its localization performance while also considering secondary objectives, such as the swarm’s speed. A complementary novel method for swarm localization, that fuses inter-robot proximity measurements and motion commands, is also presented. Numerous simulated and physical experiments are included to illustrate our contributions.

SDP-Based Robust Formation-Containment Coordination of Swarm Robotic Systems with Input Saturation

There are many potential applications of swarm robotic systems in real-world scenarios. In this paper, formation-containment controller design for single-integrator and double-integrator swarm robotic systems with input saturation is investigated. The swarm system contains two types of robots—leaders and followers. A novel control protocol and an implementation algorithm are proposed that enable the leaders to achieve the desired formation via semidefinite programming (SDP) techniques. The followers then converge into the convex hull formed by the leaders simultaneously. In contrast to conventional consensus-based formation control methods, the relative formation reference signal is not required in the real-time data transmission, which provides greater feasibility for implementation on hardware platforms. The effectiveness of the proposed formation-containment control algorithm is demonstrated with both numerical simulations and experiments using real robots that utilize the miniature mobile robot, Mona.

A Self-Organizing Area Coverage Method for Swarm Robots Based on Gradient and Grouping

The openness of the environment brings great challenges to the swarm robotic system to cover the task area quickly and effectively. In this paper, a coverage method based on gradient and grouping (GGC) is proposed. What is novel about our proposed solution is that it is suitable for extremely simple robots that lack computing or storage power. Through the change of the robot gradient, the swarm robot system with very simple functions can effectively self-organize to cover the unknown task area. By grouping the swarm robots, each group can cover the task area in parallel, which greatly improves the coverage speed. We verified our proposed method through experimental simulation and found that the gradient and grouping-based method in this paper was superior to other methods in terms of coverage, coverage completion time, and other aspects. Simultaneously, the robustness of the proposed method is analyzed and admirable experimental results are obtained. Because the applicable robot is very simple, the method in this paper can be applied to the submillimeter swarm robot system, which will lay the foundation for micro medicine.