Research Advance in Swarm Robotics

The research progress of swarm robotics is reviewed in details. The swarm robotics inspired from nature is a combination of swarm intelligence and robotics, which shows a great potential in several aspects. First of all, the cooperation of nature swarm and swarm intelligence are briefly introduced, and the special features of the swarm robotics are summarized compared to a single robot and other multi-individual systems. Then the modeling methods for swarm robotics are described by a list of several widely used swarm robotics entity projects and simulation platforms. Finally, as a main part of this paper, the current research on the swarm robotic algorithms are presented in detail, including cooperative control mechanisms in swarm robotics for flocking, navigating and searching applications.

Swarm robotics platform for intelligent interaction

Background This paper introduces a versatile edutainment platform based on a swarm robotics system that can support multiple interaction methods.We aim to create a re-usable open-ended tangible tool for a variety of educational and entertainment scenarios by utilizing the unique advantages of swarm robots such as flexible mobility,mutual perception,and free control of robot number.Methods Compared with the tangible user interface,the swarm user interface(SUI)possesses more flexible locomotion and more controllable widgets.However,research on SUI is still limited to system construction,and the upper interaction modes along with vivid applications have not been sufficiently studied.Results This study illustrates possible interaction modes for swarm robotics and feasible application scenarios based on these fundamental interaction modes.We also discuss the implementation of swarm robotics(including software and hardware),then design several simple experiments to verify the location accuracy of the swarm robotics system.

A Novel Analytical Model of Brain Tumor Based on Swarm Robotics

A tumor is referred to as“intracranial hard neoplasm”if it grows near the brain or central spinal vessel(neoplasm).In certain cases,it is possible that the responsible cells are neurons situated deep inside the brain’s structure.This article discusses a strategy for halting the progression of brain tumor.A precise and accurate analytical model of brain tumors is the foundation of this strategy.It is based on an algorithm known as kill chain interior point(KCIP),which is the result of a merger of kill chain and interior point algorithms,as well as a precise and accurate analytical model of brain tumors.The inability to obtain a clear picture of tumor cell activity is the biggest challenge in this endeavor.Based on the motion of swarm robots,which are considered a subset of artificial intelligence,this article proposes a new notion of this kind of behavior,which may be used in various situations.The KCIP algorithm that follows is used in the analytical model to limit the development of certain cell types.According to the findings,it seems that different KCIP speed ratios are beneficial in preventing the development of brain tumors.It is hoped that this study will help researchers better understand the behavior of brain tumors,so as to develop a new drug that is effective in eliminating the tumor cells.

Dynamic Frontier-Led Swarming:Multi-Robot Repeated Coverage in Dynamic Environments

A common assumption of coverage path planning research is a static environment.Such environments require only a single visit to each area to achieve coverage.However,some real-world environments are characterised by the presence of unexpected,dynamic obstacles.They require areas to be revisited periodically to maintain an accurate coverage map,as well as reactive obstacle avoidance.This paper proposes a novel swarmbased control algorithm for multi-robot exploration and repeated coverage in environments with unknown,dynamic obstacles.The algorithm combines two elements:frontier-led swarming for driving exploration by a group of robots,and pheromone-based stigmergy for controlling repeated coverage while avoiding obstacles.We tested the performance of our approach on heterogeneous and homogeneous groups of mobile robots in different environments.We measure both repeated coverage performance and obstacle avoidance ability.Through a series of comparison experiments,we demonstrate that our proposed strategy has superior performance to recently presented multi-robot repeated coverage methodologies.

Orderly hysteresis in field-driven robot swarm active matter

Boundary effect and time-reversal symmetry are hot topics in active matter. We present a biology-inspired robotenvironment-interaction active matter system with the field-drive motion and the rules of resource search, resource consumption, and resource recovery. In an environmental compression–expansion cycle, the swarm emerges a series of boundary-dependent phase transitions, and the whole evolution process is time-reversal symmetry-breaking;we call this phenomenon “orderly hysteresis”. We present the influence of the environmental recovery rate on the dynamic collective behavior of the swarm.

SUNDER:Self-organized grouping and entrapping method for swarms in multitarget environments

For swarm robots moving in a harsh or uncharted outdoor environment without GPS guidance and global communication,algorithms that rely on global-based information are infeasible.Typically,traditional gene regulatory networks(GRNs)that achieve superior performance in forming trapping pattern towards targets require accurate global positional information to guide swarm robots.This article presents a gene regulatory network with Self-organized grouping and entrapping method for swarms(SUNDER-GRN)to achieve adequate trapping performance with a large-scale swarm in a confined multitarget environment with access to only local information.A hierarchical self-organized grouping method(HSG)is proposed to structure subswarms in a distributed way.In addition,a modified distributed controller,with a relative coordinate system that is established to relieve the need for global information,is leveraged to facilitate subswarms entrapment toward different targets,thus improving the global multi-target entrapping performance.The results demonstrate the superiority of SUNDERGRN in the performance of structuring subswarms and entrapping 10 targets with 200 robots in an environment confined by obstacles and with only local information accessible.

Bio-inspired environmental adaptability of swarm active matter

How biologically active matters survive adaptively in complex and changeable environments is a common concern of scientists.Genetics,evolution and natural selection are vital factors in the process of biological evolution and are also the key to survival in harsh environments.However,it is challenging to intuitively and accurately reproduce such longterm adaptive survival processes in the laboratory.Although simulation experiments are intuitive and efficient,they lack fidelity.Therefore,we propose to use swarm robots to study the adaptive process of active matter swarms in complex and changeable environments.Based on a self-built virtual environmental platform and a robot swarm that can interact with the environment,we introduce the concept of genes into the robot system,giving each robot unique digital genes,and design robot breeding methods and rules for gene mutations.Our previous work[Proc.Natl.Acad.Sci.USA 119 e2120019119(2022)]has demonstrated the effectiveness of this system.In this work,by analyzing the relationship between the genetic traits of the population and the characteristics of environmental resources,and comparing different experimental conditions,we verified in both robot experiments and corresponding simulation experiments that agents with genetic inheritance can survive for a long time under the action of natural selection in periodically changing environments.We also confirmed that in the robot system,both breeding and mutation are essential factors.These findings can help answer the practical scientific question of how individuals and swarms can successfully adapt to complex,dynamic,and unpredictable actual environments.

Disorder-to-order transition induced by spontaneous cooling regulation in robotic active matter

In classical matter systems, typical phase-transition phenomena usually stem from changes in state variables, such as temperature and pressure, induced by external regulations such as heat transfer and volume adjustment. However, in active matter systems, the self-propulsion nature of active particles endows the systems with the ability to induce unique collectivestate transitions by spontaneously regulating individual properties to alter the overall states. Based on an innovative robot-swarm experimental system, we demonstrate a field-driven active matter model capable of modulating individual motion behaviors through interaction with a recoverable environmental resource field by the resource perception and consumption.In the simulated model, by gradually reducing the individual resource-conversion coefficient over time, this robotic active matter can spontaneously decrease the overall level of motion, thereby actively achieving a regulation behavior like the cooling-down control. Through simulation calculations, we discover that the spatial structures of this robotic active matter convert from disorder to order during this process, with the resulting ordered structures exhibiting a high self-adaptability on the geometry of the environmental boundaries.

Cognitive Supervisor for an Autonomous Swarm of Robots

As a sequel to our recent work [1], in which a control framework was developed for large-scale joint swarms of unmanned ground (UGV) and aerial (UAV) vehicles, the present paper proposes cognitive and meta-cognitive supervisor models for this kind of distributed robotic system. The cognitive supervisor model is a formalization of the recently Nobel-awarded research in brain science on mammalian and human path integration and navigation, performed by the hippocampus. This is formalized here as an adaptive Hamiltonian path integral, and efficiently simulated for implementation on robotic vehicles as a pair of coupled nonlinear Schr?dinger equations. The meta-cognitive supervisor model is a modal logic of actions and plans that hinges on a weak causality relation that specifies when atoms may change their values without specifying that they must change. This relatively simple logic is decidable yet sufficiently expressive to support the level of inference needed in our application. The atoms and action primitives of the logic framework also provide a straight-forward way of connecting the meta-cognitive supervisor with the cognitive supervisor, with other modules, and to the meta-cognitive supervisors of other robotic platforms in the swarm.

An introductory review of swarm technology for spacecraft on-orbit servicing

This review paper presents a comprehensive evaluation and forward-looking perspective on the underexplored topic of servicing target objects using spacecraft swarms.Such targets can be known or unknown,cooperative or uncooperative,and pose significant challenges in modern space operations due to their inherent complexity and unpredictability.Successfully servicing space objects is vital for active debris removal and broader on-orbit servicing tasks such as satellite maintenance,repair,refueling,orbital assembly,and construction.Significant effort has been invested in the literature to explore the servicing of targets using a single spacecraft.Given its advantages and benefits,this paper expands the discussion to encompass a swarm approach to the problem.This review covers various single-spacecraft approaches and presents a critical examination of the existing,although limited,body of work dedicated to servicing orbital objects using multiple spacecraft.The focus is also broadened to include some influential studies concerning the characterization,capture,and manipulation of physical objects by general multiagent systems,a subject with significant parallels to the core interest of this manuscript.Furthermore,this article also delves into the realm of simultaneous localization and mapping,highlighting its application within close-proximity operations in space,especially when dealing with unknown uncooperative targets.Special attention is paid to the benefits that this field can receive from distributed multiagent architectures.Finally,an exploration of the promising field of swarm robotics is presented,with an emphasis on its potential to revolutionize the servicing of orbital target objects.Concurrently,a survey of general research directly engaging swarms in the orbital context is conducted.This review aims to bridge the knowledge gap and stimulate further research in the underexplored domain of servicing space targets with spacecraft swarms.

Multi-Objective Rule System Based Control Model with Tunable Parameters for Swarm Robotic Control in Confined Environment

Enhancing the adaptability of Unmanned Aerial Vehicle(UAV)swarm control models to cope with different complex working scenarios is an important issue in this research field.To achieve this goal,control model with tunable parameters is a widely adopted approach.In this article,an improved UAV swarm control model with tunable parameters namely Multi-Objective O-Flocking(MO O-Flocking)is proposed.The MO O-Flocking model is a combination of a multi rule control system and a virtual-physical-law based control model with tunable parameters.To achieve multi-objective parameter tuning,a multi-objective parameter tuning method namely Improved Strength Pareto Evolutionary Algorithm 2(ISPEA2)is designed.Simulation experiment scenarios include six target orientation scenarios with different kinds of objectives.Experimental results show that both the ISPEA2 algorithm and MO O-Flocking control model have good performance in their experiment scenarios.

Quanta-A platform for rapid control and monitoring of heterogeneous rbots

Rapid prototyping,real-time control and monitoring of various events in robots are crucial requirements for research in the fields of modular and swarm robotics.A large quantities of resources(time,man power,infrastructure,etc.)are often invested in programming,interfacing the sensors,debugging the response to algorithms during prototyping and operational phases of a robot development cycle.The cost of developing an optimal infrastructure to efficiently address such control and monitoring requirements increases significantly in the presence of mobile robots.Though numerous solutions have been developed for minimizing the resources spent on hardware prototyping and algorithm validation in both static and mobile scenarios,it can be observed that researchers have either chosen methodologies that conflict with the power and infrastructure constraints of the research field or generated constrained solutions whose applications are restricted to the field itself.This paper develops a solution for addressing the challenges in controlling heterogeneous mobile robots.A platform named Quanta-a cost effective,energy efficient and high-speed wireless infrastructure is prototyped as a part of the research in the field of modular robotics.Quanta is capable of controlling and monitoring various events in/using a robot with the help of a light-weight communication protocol independent of the robot hardware architecture(s).

Emerging robot swarm traffic

Purpose-This paper aims to discuss traffic patterns generated by swarms of robots while commuting to and from a base station.Design/methodology/approach-The paper adopts a mathematical evaluation and robot swarm simulation.The swarm approach is bottom-up:designing individual agents the authors are looking for emerging group behaviour patterns.Examples of group behaviour patterns are human-driven motorized traffic which is rigidly structured in two lanes,while army ants develop a three-lane pattern in their traffic.The authors copy army ant characteristics onto their robots and investigate whether the three lane traffic pattern may emerge.They follow a three-step approach.The authors first investigate the mathematics and geometry of cases occurring when applying the artificial potential field method to three“perfect”robots.Any traffic pattern(two,three or more lanes)appears to be possible.Next,they use the mathematical cases to study the impact of limited visibility by defining models of sensor designs.In the final step the authors implement ant inspired sensor models and a trail following mechanism on the robots in the swarm and explore which traffic patterns do emerge in open space as well as in bounded roads.Findings-The study finds that traffic lanes emerge in the swarm traffic;however the number of lanes is dependent on the initial situation and environmental conditions.Intrinsically the applied robot models do not determine a specific number of traffic lanes.Originality/value-The paper presents a method for studying and simulating robot swarms.

Artificial moment method for swarm robot formation control

The purpose of this paper is to develop a general control method for swarm robot formation control. Firstly, an attraction-segment leader-follower formation graph is presented for formation representations. The model of swarm robot systems is described. According to the results and two kinds of artificial moments defined as leader-attraction moment and follower-attraction moment, a novel artificial moment method is proposed for swarm robot formation control. The principle of the method is introduced and the motion controller of robots is designed. Finally, the stability of the formation control system is proved. The simulations show that both the formation representation graph and the formation control method are valid and feasible.

Self-assembly control and experiments in swarm modular robots

This paper presents a self-assembly control strategy for the swarm modular robots. Simulated and physical experiments are conducted based on the Sambot platform, which is a novel self-assembly modular robot having the characteristics of both the chain-type and the mobile self-reconfigurable robots. Multiple Sambots can autonomously move and connect with one another through self-assembly to form robotic organisms. The configuration connection state table is used to describe the configuration of the robotic structure. A directional self-assembly control model is proposed to perform the self-assembly experiments. The self-assembly process begins with one Sambot as the seed, and then the Docking Sambots use a behavior-based controller to achieve connection with the seed Sambot. The controller is independent of the target configuration. The seed and connected Sambots execute a configuration comparison algorithm to control the growth of the robotic structure. Furthermore, the simul- taneous self-assembly of multiple Sambots is discussed. For multiple configurations, self-assembly experiments are conducted in simulation platform and physical platform of Sambot. The experimental results verify the effectiveness and scalability of the self-assembly algorithms.

Decentralized runtime enforcement for robotic swarms

Robotic swarms are usually designed in a bottom-up way,which can make robotic swarms vulnerable to environmental impact.It is particularly true for the widely used control mode of robotic swarms,where it is often the case that neither the correctness of the swarming tasks at the macro level nor the safety of the interaction among agents at the micro level can be guaranteed.To ensure that the behaviors are safe at runtime,it is necessary to take into account the property guard approaches for robotic swarms in uncertain environments.Runtime enforcement is an approach which can guarantee the given properties in system execution and has no scalability issue.Although some runtime enforcement methods have been studied and applied in different domains,they cannot effectively solve the problem of property enforcement on robotic swarm tasks at present.In this paper,an enforcement method is proposed on swarms which should satisfy multi-level properties in uncertain environments.We introduce a macromicro property enforcing framework with the notion of agent shields and a discrete-time enforcing mechanism called D-time enforcing.To realize this method,a domain specification language and the corresponding enforcer synthesis algorithms are developed.We then apply the approach to enforce the properties of the simulated robotic swarm in the robotflocksim platform.We evaluate and show the effectiveness of the method with experiments on specific unmanned aerial vehicle swarm tasks.

Biologically inspired collective construction with visual landmarks

We describe our research in using environmental visual landmarks as the basis for completing simple robot construction tasks.Inspired by honeybee visual navigation behavior,a visual template mechanism is proposed in which a natural landmark serves as a visual reference or template for distance determination as well as for navigation during collective construction.To validate our proposed mechanism,a wall construction problem is investigated and a minimalist solution is given.Experimental results show that,using the mechanism of a visual template,a collective robotic system can successfully build the desired structure in a decentralized fashion using only local sensing and no direct communication.In addition,a particular variable,which defines tolerance for alignment of the structure,is found to impact the system performance.By decreasing the value of the variable,system performance is improved at the expense of a longer construction time.The visual template mechanism is appealing in that it can use a reference point or salient object in a natural environment that is new or unexplored and it could be adapted to facilitate more complicated building tasks.

Bioinspired Centimeter-scale Sensor Free Obstacle-passing Robots with a Wireless Control System

Obstacle avoidance is of great importance for mobile robots since it provides protection for the robots’safety and ensures their routine operations.Sensors are proven to play an important role in robots obstacle avoidance,and they are useful as well.However,more sensors indicating additional space,larger weight load and more energy consumption.Reducing unnecessary sensors is conducive to the development of mobile robots and remains promising.Here we demonstrate Sensor Free Obstacle-Passing Robots(SFOPRs)inspired by flies using the Obstacle-passing strategy instead of Obstacle avoidance.The ability to autonomously adjust its direction after hitting obstacles and the ability to continuously hit obstacles are 2 key problems that need to be solved to build this robot.Owing to arc-shaped head design and undulating motion behaviors,the robots can autonomously adjust their direction to the outline of obstacles,such as a 90°corner,dispersive irregular obstacles,and even an"S"type channel without the assistance of any sensor.Besides,the caterpillar-like movement enables robots to continuously hit obstacles.Furthermore,collaborative awareness and mutual aid can be realized among two or more prototype robots,indicating simple yet functional units for future swarm robots.This study could provide a new strategy to pursue sensor-free obstacle-passing robots for future swarm robot applications.