Swarm Robotic Systems Research Articles

Exploration of a Simple Navigation Method for Swarm Robots Pioneered by Heterogeneity

In recent years, research has been conducted on swarm robot systems in which multiple autonomous mobile robots cooperate to perform tasks. Swarm robot systems are expected to perform high functionality as a group by cooperating with each other, in spite of the limited capabilities of the individual robots. This paper explores a method of simplifying swarm robot controllers as much as possible for swarm robot navigation. If we can achieve autonomous navigation of swarm robots to a target area with minimal resource consumption, they only need to implement the task execution function in that area. This leads to lower costs for swarm robot design and more efficient system architecture design. To address the above aims, this study focuses on heterogeneity. Specifically, we introduce a navigator robot that indirectly guides swarm robots named the worker robots. Heterogeneity in this paper refers to the worker robots and the navigator robots. We design the interaction between the navigator robot and the worker robots to provide a system that guides the worker robots to the destination.

Evolutionary Design of Cooperative Transport Behavior for a Heterogeneous Robotic Swarm

Swarm robotics system (SRS) is a type of artifact that employs multiple robots to work together in a coordinated way, inspired by the self-organizing behavior of social insects such as ants and bees. SRSs are known for their robustness, flexibility, and scalability. This study focuses on evolutionary robotics (ER) which uses artificial neural networks (ANNs) as controllers to operate autonomous robots. In traditional ER research, SRSs were often composed of teams of homogeneous robots, each of which is controlled by a single ANN. In contrast, this study focuses on the implementation of ER in a heterogeneous SRS. To evaluate our approach, we present the concept of employing multiple controllers for sub-teams in a swarm. Heterogeneity was achieved using different controllers for the same physical bodies. We simulated a cooperative transport task, in which the performance of heterogeneity was superior because the two ANN controllers were able to express a variety of behaviors as an entire swarm. Additionally, this study investigated how well the three types of parental selection methods of the heterogeneous approach, can help to optimize the performance of the swarm.

Automated pattern generation for swarm robots using constrained multi-objective genetic programming

Swarm robotic systems (SRSs), which are widely used in many fields, such as search and rescue, usually comprise a number of robots with relatively simple mechanisms collaborating to accomplish complex tasks. A challenging task for SRSs is to design local interaction rules for self-organization of robots that can generate adaptive patterns to entrap moving targets. Biologically inspired approaches such as gene regulatory network (GRN) models provide a promising solution to this problem. However, the design of GRN models for generating entrapping patterns relies on the expertise of designers. As a result, the design of the GRN models is often a laborious and tedious trial-and-error process. In this study, we propose a modular design automation framework for GRN models that can generate entrapping patterns. The framework employs basic network motifs to construct GRN models automatically without requiring expertise. To this end, a constrained multi-objective genetic programming is utilized to simultaneously optimize the structures and parameters of the GRN models. A multi-criteria decision-making approach is adopted to choose the preferred GRN model for generating the entrapping pattern. Comprehensive simulation results demonstrate that the proposed framework can obtain novel GRN models with simpler structures than those designed by human experts yet better performance in complex and dynamic environments. Proof-of-concept experiments using e-puck robots confirmed the feasibility and effectiveness of the proposed GRN models.

Entropy by Neighbor Distance as a New Measure for Characterizing Spatiotemporal Orders in Microscopic Collective Systems.

Collective systems self-organize to form globally ordered spatiotemporal patterns. Finding appropriate measures to characterize the order in these patterns will contribute to our understanding of the principles of self-organization in all collective systems. Here we examine a new measure based on the entropy of the neighbor distance distributions in the characterization of collective patterns. We study three types of systems: a simulated self-propelled boid system, two active colloidal systems, and one centimeter-scale robotic swarm system. In all these systems, the new measure proves sensitive in revealing active phase transitions and in distinguishing steady states. We envision that the entropy by neighbor distance could be useful for characterizing biological swarms such as bird flocks and for designing robotic swarms.

Exploring the criticality hypothesis using programmable swarm robots with Vicsek-like interactions.

A widely mentioned but not experimentally confirmed view (known as the 'criticality hypothesis') argues that biological swarm systems gain optimal responsiveness to perturbations and information processing capabilities by operating near the critical state where an ordered-to-disordered state transition occurs. However, various factors can induce the ordered-disordered transition, and the explicit relationship between these factors and the criticality is still unclear. Here, we present an experimental validation for the criticality hypothesis by employing real programmable swarm-robotic systems (up to 50 robots) governed by Vicsek-like interactions, subject to time-varying stimulus-response and hazard avoidance. We find that (i) not all ordered-disordered motion transitions correspond to the functional advantages for groups; (ii) collective response of groups is maximized near the critical state induced by alignment weight or scale rather than noise and other non-alignment factors; and (iii) those non-alignment factors act to highlight the functional advantages of alignment-induced criticality. These results suggest that the adjustability of velocity or directional coupling between individuals plays an essential role in the acquisition of maximizing collective response by criticality. Our results contribute to understanding the adjustment strategies of animal interactions from a perspective of criticality and provide insights into the design and control of swarm robotics.

A Concurrent Mission-Planning Methodology for Robotic Swarms Using Collaborative Motion-Control Strategies.

Swarm robotic systems comprising members with limited onboard localization capabilities rely on employing collaborative motion-control strategies to successfully carry out multi-task missions. Such strategies impose constraints on the trajectories of the swarm and require the swarm to be divided into worker robots that accomplish the tasks at hand, and support robots that facilitate the movement of the worker robots. The consideration of the constraints imposed by these strategies is essential for optimal mission-planning. Existing works have focused on swarms that use leader-based collaborative motion-control strategies for mission execution and are divided into worker and support robots prior to mission-planning. These works optimize the plan of the worker robots and, then, use a rule-based approach to select the plan of the support robots for movement facilitation - resulting in a sub-optimal plan for the swarm. Herein, we present a mission-planning methodology that concurrently optimizes the plan of the worker and support robots by dividing the mission-planning problem into five stages: division-of-labor, task-allocation of worker robots, worker robot path-planning, movement-concurrency, and movement-allocation. The proposed methodology concurrently searches for the optimal value of the variables of all stages. The proposed methodology is novel as it (1) incorporates the division-of-labor of the swarm into worker and support robots into the mission-planning problem, (2) plans the paths of the swarm robots to allow for concurrent facilitation of multiple independent worker robot group movements, and (3) is applicable to any collaborative swarm motion-control strategy that utilizes support robots. A unique pre-implementation estimator, for determining the possible improvement in mission execution performance that can achieved through the proposed methodology was also developed to allow the user to justify the additional computational resources required by it. The estimator uses a machine learning model and estimates this improvement based on the parameters of the mission at hand. Extensive simulated experiments showed that the proposed concurrent methodology improves the mission execution performance of the swarm by almost 40% compared to the competing sequential methodology that optimizes the plan of the worker robots first and, then, the plan of the support robots. The developed pre-implementation estimator was shown to achieve an estimation error of less than 5%.

Static group-bipartite consensus in networked robot systems with integral action

With the increasing complexity of modern industry, the traditional single-target control of swarm robots can no longer meet application requirements. Hence, this article addresses compound task control for swarm robot systems, where the control aim and the dynamics of the robot are modeled by static group-bipartite consensus and Euler–Lagrange systems, respectively. After introducing the concept of static group-bipartite consensus in networked Euler–Lagrange systems, a distributed group-bipartite consensus control protocol with integral action is designed, and the criterion that ensures that static group-bipartite consensus is reached is presented. The most remarkable feature of the proposed algorithm is that it can accurately calculate the final state of system convergence. Finally, simulation examples are presented to verify the theoretical results.

Method for decomposition a global task into local tasks through the process of collective decision making in swarm robotic systems

Objective. The purpose of the work is to increase the efficiency of the process of task allocation through preliminary decomposition and prioritization of tasks based on the process of collective decision-making (CDM) in swarm robotic systems (SRS).Method. Task decomposition implemented using a hybrid decision-making strategy, the majority principle of changing opinions using distributed registry technology for disseminating opinions among SRS agents and aggregating accumulated knowledge about the functioning environment. An element of scientific novelty is the proposed SRS procedure, which provides the possibility of assessing the priority of tasks, which in turn improves the efficiency of the SRS agents.Result. Software implementation of the proposed method made in the C++ programming language. For the experiments, the scenario of collective perception by SRS agents in a specialized simulation environment ARGoS was used.Conclusion. The method implemented using the solutions proposed in the work turned out to be more efficient than the method based on the greedy algorithm. The proposed solutions can be used not only in SRS, but also in any other robotic systems with decentralized control, designed to monitor and control environmental parameters.

From Warfighting Needs to Robot Actuation: A Complete Rapid Integration Swarming Solution

Swarm robotics systems have the potential to transform warfighting in urban environments but until now have not seen large-scale field testing. We present the Rapid Integration Swarming Ecosystem (rise), a platform for future multi-agent research and deployment. rise enables rapid integration of third-party swarm tactics and behaviors, which was demonstrated using both physical and simulated swarms. Our physical testbed is composed of more than 250 networked heterogeneous agents and has been extensively tested in mock warfare scenarios at five urban combat training ranges. rise implements live, virtual, constructive simulation capabilities to allow the use of both virtual and physical agents simultaneously, while our “fluid fidelity” simulation enables adaptive scaling between low and high fidelity simulation levels based on dynamic runtime requirements. Both virtual and physical agents are controlled with a unified gesture-based interface that enables a greater than 150:1 agent-to-operator ratio. Through this interface, we enable efficient swarm-based mission execution. rise translates mission needs to robot actuation with rapid tactic integration, a reliable testbed, and efficient operation.

Proposal and Implementation of an Autonomous Decentralized Rescue Architecture for Anomaly Response in Swarm Robot Systems

Proposal and Implementation of an Autonomous Decentralized Rescue Architecture for Anomaly Response in Swarm Robot Systems

Data-driven Search for the Graph Structure Improving Control Performance in Cooperative Control of Human-vehicle Network

Recently, robot swarm systems have been developed and cooperative control of human-robotic network based on passivity of a human operator has been proposed. However, it is difficult to guarantee control performance of human-robotic network because it depends on the property of human. In this paper, we propose a data-driven method to search for the graph structure of the vehicle swarm improving control performance in cooperative control of human-vehicle network, assuming that humans are unknown linear systems. We use the idea of FRIT and define a cost function which means the error between the output of the ideal transfer function and the vehicle swarm. Then, we propose a method to calculate the cost function with an one-shot input output data. In this way, we can search for the graph structure of the vehicle swarm that is easy for humans to control. Finally, we demonstrate the effectiveness of the proposed method through numerical simulations.

Improved PBFT Algorithm Based on K-Means Clustering for Emergency Scenario Swarm Robotic Systems

Improved PBFT Algorithm Based on K-Means Clustering for Emergency Scenario Swarm Robotic Systems

Color‐Coded Light Signals Based Communication for Swarming Behaviors in Mobile Robotic System

Swarm robotics deals with the design, development, and control of multirobot system. The individual agents of the swarm robotics system are simple, with limited capabilities in order to enhance easy scalability of the system. The current researches in swarm robotics are limited due to the complexity of their control and communication system. This paper presents minimobile robots that uses color‐coded light signals for communication over short distances and can also communicate over WiFi mesh for global information sharing. Color‐coded light‐based communication enables the robots to control the directionality and range of communication signals, thus making the communication highly efficient comparing to other conventional communication techniques. The proposed robot design helps the agents to communicate with each other and thereby a swarming behavior emerges from their interaction.

A fellow-following-principle based group model and its application to fish school analysis

Group models based on simple rules are viewed as a bridge to clarifying animal group movements. The more similar a model to real-world observations, the closer it is to the essence of such movements. Inspired by the fish school, this study suggests a principle called fellow-following for group movements. More specifically, a simple-rules-based model was proposed and extended into a set of concrete rules, and two- and three-dimensional group models were established. The model results are intuitively similar to the fish school, and when the group size increases, the milling phase of both the model and fish school tends from unstable to stable. Further, we proposed a novel order parameter and a similarity measurement framework for group structures. The proposed model indicates the intuition similarity, consistency of dynamic characteristics, and static structure similarity with fish schools, which suggests that the principle of fellow-following may reveal the essence of fish school movements. Our work suggests a different approach for the self-organized formation of a swarm robotic system based on local information.

Concept of Operations as a Boundary Object for Knowledge Sharing in the Design of Robotic Swarms

Designing a swarm of autonomous robots for commercial, military, or other purposes is a challenging engineering and human factors design effort. The challenges argue in favor of practices and tools for better integration of different engineering disciplines and for the advancement of communication between stakeholders with different interests. The Concept of Operations (ConOps) approach is widely used in Systems Engineering for this purpose. A ConOps is a high-level description of how the elements of a system and entities in its environment interact in order to achieve their stated goals. This paper will present the development of a ConOps for a swarm of autonomous robotic vehicles in the military domain to demonstrate how autonomic robotic swarms can be deployed in different military branches in the future. The proposed ConOps can be considered as a boundary object in the design, validation, or procurement of an autonomous robotic swarm system. We also propose that the ConOps should be maintained throughout the system life-cycle as an overview description and definition of overall goals and policies.

Exploration of Multiple Unknown Areas by Swarm of Robots Utilizing Virtual-Region-Based Splitting and Merging Technique

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

A Dynamic Game Framework for Rational and Persistent Robot Deception With an Application to Deceptive Pursuit-Evasion

This article studies rational and persistent deception among intelligent robots to enhance security and operational efficiency. We present an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> -player <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -stage game with an asymmetric information structure where each robot’s private information is modeled as a random variable or its type. The deception is persistent as each robot’s private type remains unknown to other robots for all stages. The deception is rational as robots aim to achieve their deception goals at minimum cost. Each robot forms a dynamic belief of others’ types based on intrinsic or extrinsic information. Perfect Bayesian Nash equilibrium (PBNE) is a natural solution concept for dynamic games of incomplete information. Due to its requirements of sequential rationality and belief consistency, PBNE provides a reliable prediction of players’ actions, beliefs, and expected cumulative costs over the entire <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> stages. The contribution of this work is fourfold. First, we identify the PBNE computation as a nonlinear stochastic control problem and characterize the structures of players’ actions and costs under PBNE. We further derive a set of extended Riccati equations with cognitive coupling under the linear-quadratic (LQ) setting and extrinsic belief dynamics. Second, we develop a receding-horizon algorithm with low temporal and spatial complexity to compute PBNE under intrinsic belief dynamics. Third, we investigate a deceptive pursuit-evasion game as a case study and use numerical experiments to corroborate the results. Finally, we propose metrics, such as deceivability, reachability, and the price of deception (PoD), to evaluate the strategy design and the system performance under deception. Note to Practitioners—Recent advances in automation and adaptive control in multi-agent systems enable robots to use deception to accomplish their objectives. Deception involves intentional information hiding to compromise the security and operational efficiency of the robotic systems. This work proposes a dynamic game framework to quantify the impact of deception, understand the robots’ behaviors and intentions, and design cost-efficient strategies under the deception that persists over stages. Existing research studies on robot deception have relied on experiments while this work aims to lay a theoretical foundation of deception with quantitative metrics, such as deceivability and the PoD. The proposed model has wide applications, including cooperative robots, pursuit and evasion, and human–robot teaming. The pursuit-evasion games are used as case studies to show how the deceiver can amplify the deception by belief manipulation and how the deceived robots can reduce the negative impact of deception by enhanced maneuverability and Bayesian learning. The future work would focus on designing cooperative deception among swarm robotics and robotic systems that are robust to or further benefit from the deception.

Congestion control algorithms for robotic swarms with a common target based on the throughput of the target area

When a large number of robots try to reach a common area, congestions happen, causing severe delays. To minimise congestion in a robotic swarm system, traffic control algorithms must be employed in a decentralised manner. Based on strategies aimed to maximise the throughput of the common target area, we developed two novel algorithms for robots using artificial potential fields for obstacle avoidance and navigation. One algorithm is inspired by creating a queue to get to the target area (Single Queue Former — SQF), while the other makes the robots touch the boundary of the circular area by using vector fields (Touch and Run Vector Fields — TRVF). We performed simulation experiments to show that the proposed algorithms are bounded by the throughput of their inspired theoretical strategies and compare the two novel algorithms with state-of-art algorithms for the same problem (PCC, EE and PCC–EE). The SQF algorithm significantly outperforms all other algorithms for a large number of robots or when the circular target region radius is small. TRVF, on the other hand, is better than SQF only for a limited number of robots and outperforms only PCC for numerous robots. However, it allows us to analyse the potential impacts on the throughput when transferring an idea from a theoretical strategy to a concrete algorithm that considers changing linear speeds and distances between robots.

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.