Swarm Robotics Research Research Articles

Tracking the trajectory of a swarm of mobile robots with a computer vision system

Swarm robotics research uses a range of tools for evaluating the behaviors and metrics of robot collectives. One crucial tool involves the capability to track each robot’s position and orientation at various intervals, enabling the reconstruction of individual robot poses and trajectories. Comprehensive analysis of swarm behavior hinges on the study of the collective trajectories of each robot within the group. This paper demonstrates the implementation of a computer vision system, utilizing a webcam and Python scripts, to effectively track a mobile robot group within a swarm. This shows the feasibility of developing such research tools using commonplace computing equipment. The design and development of the vision system, including a detailed calibration procedure, robot identification methods, and practical examples, are also shown. Furthermore, it offers an exhaustive explanation of the robot tracking process. Experimental trials with three robots validate the system’s ability to extract images from video feeds and accurately identify each robot. Subsequently, after image processing, the system generates a dataset encompassing image numbers, robot IDs, x and y positions, and orientations.

An Open-Source UAV Platform for Swarm Robotics Research: Using Cooperative Sensor Fusion for Inter-Robot Tracking

An Open-Source UAV Platform for Swarm Robotics Research: Using Cooperative Sensor Fusion for Inter-Robot Tracking

HeRo 2.0: a low-cost robot for swarm robotics research

The current state of electronic component miniaturization coupled with the increasing efficiency in hardware and software allow the development of smaller and compact robotic systems. The convenience of using these small, simple, yet capable robots has gathered the research community’s attention towards practical applications of swarm robotics. This paper presents the design of a novel platform for swarm robotics applications that is low cost, easy to assemble using off-the-shelf components, and deeply integrated with the most used robotic framework available today: ROS (Robot Operating System). The robotic platform is entirely open, composed of a 3D printed body and open-source software. We describe its architecture, present its main features, and evaluate its functionalities executing experiments using a couple of robots. Results demonstrate that the proposed mobile robot is capable of performing different swarm tasks, given its small size and reduced cost, being suitable for swarm robotics research and education.

SimoBot: An Underactuated Miniature Robot Driven by a Single Motor

A simple-structured, low-cost miniature robot is highly desired for swarm robotics research. In this article, we designed an underactuated miniature robot using only one motor, named as SimoBot, to save space, cost, weight, and energy consumption. Since it has only one motor but needs to travel on the 2-D ground, the design, moving mechanism, and control are all challenging. We conceived a movement strategy based on the interplaying between the centrifugal force and swinging motion of the robot to generate translation and rotation on the ground and finally follow various arbitrary paths. This robot has only five simple components—a vibration motor, a button cell, a microcontroller board, four-pin legs (one of which is shorter than the others), and sensors. This article built the kinematics and kinetics models and analyzed and optimized the parameters. The prototype weighs only 4.76 g, costs 4.7 dollars, and is 20 mm in diameter and 18 mm in height. It can run as fast as 40 mm/s. Its cost of transportation is only 55, smaller than most miniature robots with onboard power and insects. By modulating voltage polarities on the motor and the periods, we demonstrated the controllability of SimoBot by the movement along a straight line, a circle, an “S” curve, and more arbitrary trajectories.

State-transfer modeling collective behavior of multi-ball Bernoulli system based on local interaction forces.

Collective behavior observed in nature has been actively employed in swarm robotics. In order to better respond to external cues, the agents in such systems organize themselves in an ordered structure based on simple local rules. The central assumption, in swarm robotics, is that all agents in the system collaborate to fulfill a common goal. In nature, however, many multi-agent systems exhibit a more complex collective behavior involving a certain level of competition. One representative example of complex collective behavior is a multi-ball Bernoulli-ball system. In this paper, by extracting local force among the Bernoulli balls, we approximated the state-transfer model mapping interaction forces to observed behaviors. The results show that the collective Bernoulli-ball system spent 41% of its time on competitive behaviors, in which up to 84% of the interaction state is unorganized. The rest 59% of the time is spent on collaborative behavior. We believe that the novel proposed model opens new avenues in swarm robotics research.

Recent Advances in Swarm Robotics Coordination: Communication and Memory Challenges

Swarm robotics research has been present for some decades, providing nature-inspired algorithms in swarms of robots [...]

Transporting Robotic Swarms Via Mean-Field Feedback Control

With the rapid development of AI and robotics, transporting a large swarm of networked robots has foreseeable applications in the near future. Existing research in swarm robotics has mainly followed a bottom-up philosophy with predefined local coordination and control rules. However, it is arduous to verify the global requirements and analyze their performance. This motivates us to pursue a top-down approach, and develop a provable control strategy for deploying a robotic swarm to achieve a desired global configuration. Specifically, we use mean-field partial differential equations (PDEs) to model the swarm and control its mean-field density (i.e., probability density) over a bounded spatial domain using mean-field feedback. The presented control law uses density estimates as feedback signals and generates corresponding velocity fields that, by acting locally on individual robots, guide their global distribution to a target profile. The design of the velocity field is therefore centralized, but the implementation of the controller can be fully distributed -- individual robots sense the velocity field and derive their own velocity control signals accordingly. The key contribution lies in applying the concept of input-to-state stability (ISS) to show that the perturbed closed-loop system (a nonlinear and time-varying PDE) is locally ISS with respect to density estimation errors. The effectiveness of the proposed control laws is verified using agent-based simulations.

Swarm Robotics: Simulators, Platforms and Applications Review

This paper presents an updated and broad review of swarm robotics research papers regarding software, hardware, simulators and applications. The evolution from its concept to its real-life implementation is presented. Swarm robotics analysis is focused on four aspects: conceptualization, simulators, real-life robotics for swarm use, and applications. For simulators and robots, a detailed comparison between existing resources is made. A summary of the most used swarm robotics applications and behaviors is included.

Phormica: Photochromic Pheromone Release and Detection System for Stigmergic Coordination in Robot Swarms.

Stigmergy is a form of indirect communication and coordination in which agents modify the environment to pass information to their peers. In nature, animals use stigmergy by, for example, releasing pheromone that conveys information to other members of their species. A few systems in swarm robotics research have replicated this process by introducing the concept of artificial pheromone. In this paper, we present Phormica, a system to conduct experiments in swarm robotics that enables a swarm of e-puck robots to release and detect artificial pheromone. Phormica emulates pheromone-based stigmergy thanks to the ability of robots to project UV light on the ground, which has been previously covered with a photochromic material. As a proof of concept, we test Phormica on three collective missions in which robots act collectively guided by the artificial pheromone they release and detect. Experimental results indicate that a robot swarm can effectively self-organize and act collectively by using stigmergic coordination based on the artificial pheromone provided by Phormica.

Automatic Design of Collective Behaviors for Robots that Can Display and Perceive Colors

Research in swarm robotics has shown that automatic design is an effective approach to realize robot swarms. In automatic design methods, the collective behavior of a swarm is obtained by automatically configuring and fine-tuning the control software of individual robots. In this paper, we present TuttiFrutti: an automatic design method for robot swarms that belongs to AutoMoDe—a family of methods that produce control software by assembling preexisting software modules via optimization. The peculiarity of TuttiFrutti is that it designs control software for e-puck robots that can display and perceive colors using their RGB LEDs and omnidirectional camera. Studies with AutoMoDe have been so far restricted by the limited capabilities of the e-pucks. By enabling the use of colors, we significantly enlarge the variety of collective behaviors they can produce. We assess TuttiFrutti with swarms of e-pucks that perform missions in which they should react to colored light. Results show that TuttiFrutti designs collective behaviors in which the robots identify the colored light displayed in the environment and act accordingly. The control software designed by TuttiFrutti endowed the swarms of e-pucks with the ability to use color-based information for handling events, communicating, and navigating.

MicroUSV: A low-cost platform for indoor marine swarm robotics research.

This article describes an open source Unmanned Surface Vehicle (USV) designed to operate in indoor laboratory environments. The microUSV is small (23cm long) and inexpensive (approximately $320 per unit for 10 vessels): an ideal hardware platform for algorithm validation and marine swarm robotics research. The primary design goal was to minimize the vehicle's size and cost while providing a stable and maneuverable platform with onboard autonomy. To that end the vehicle is built using 3D printed and off-the-shelf hobbyist electronic components and uses an overhead camera system to simulate sensor data to minimize the number of onboard sensors required. This article describes the context, design, and assembly procedures for a microUSV and demonstrates the platform's base-level functionality in the form of a waypoint following controller implementation for both single and multi-robot configurations.

A swarm design paradigm unifying swarm behaviors using minimalistic communication

Numerous nature inspired algorithms have been suggested to enable robotic swarms, mobile sensor networks and other multi-agent systems to exhibit various self-organized behaviors. Swarm intelligence and swarm robotics research have been underway for a few decades and have produced many such algorithms based on natural self-organizing systems. While a large body of research exists for variations and modifications in swarm intelligence algorithms, there have been few attempts to unify the underlying agent level design of these widely varying behaviors. In this work, a design paradigm for a swarm of agents is presented which can exhibit a wide range of collective behaviors at swarm level while using minimalistic single-bit communication at the agent level. The communication in the proposed paradigm is based on waves of ‘ping’-signals inspired by strategies for communication and self organization of slime mold (Dictyostelium discoideum) and fireflies (lampyridae). The unification of common collective behaviors through this Wave Oriented Swarm Paradigm (WOSP) enables the control of swarms with minimalistic communication and yet allowing the emergence of diverse complex behaviors. It is demonstrated both in simulation and using real robotic experiments that even a single-bit communication channel between agents suffices for the design of a substantial set of behaviors. Ultimately, the reader will be enabled to combine different behaviours based on the paradigm to develop a control scheme for individual swarms.

Swarm robotics: Robustness, scalability, and self-X features in industrial applications

Abstract Applying principles of swarm intelligence to the control of autonomous systems in industry can advance our ability to manage complexity in prominent and high-cost sectors—such as transportation, logistics, and construction. In swarm robotics, the exclusive use of decentralized control relying on local communication and information provides the key advantage first of scalability, and second of robustness against failure points. These are directly useful in certain applied tasks that can be studied in laboratory environments, such as self-assembly and self-organized construction. In this article, we give a brief introduction to swarm robotics for a broad audience, with the intention of targeting future industrial applications. We then present a summary of four examples of our recently published research results with simple models. First, we present our approach to self-reconfiguration, which uses collective adjustment of swarm density in a dynamic setting. Second, we describe our robot experiments for self-organized material deployment in structured and semi-structured environments, applicable to braided composites. Third, we present our machine learning approach for self-assembly, motivated as a simple model developing foundational methods, which generates self-organizing robot behaviors to form emergent patterns. Fourth, we describe our experiments implementing a bioinspired model in a robot swarm, where we show self-healing of damage as the robots collectively locate a resource. Overall, the four examples we present concern robustness, scalability, and self-X features, which we propose as potentially relevant to future research in swarm robotics applied to industry sectors. We summarize these approaches as an introduction to our recent research, targeting the broad audience of this journal.

Evolutionary-learning framework: improving automatic swarm robotics design

PurposeThe purpose of this paper is to review the current state of proceedings in the research area of automatic swarm design and discusses possible solutions to advance swarm robotics research.Design/methodology/approachFirst, this paper begins by reviewing the current state of proceedings in the field of automatic swarm design to provide a basic understanding of the field. This should lead to the identification of which issues need to be resolved in order to move forward swarm robotics research. Then, some possible solutions to the challenges are discussed to identify future directions and how the proposed idea of incorporating learning mechanism could benefit swarm robotics design. Lastly, a novel evolutionary-learning framework for swarms based on epigenetic function is proposed with a discussion of its merits and suggestions for future research directions.FindingsThe discussion shows that main challenge which is needed to be resolved is the presence of dynamic environment which is mainly caused by agent-to-agent and agent-to-environment interactions. A possible solution to tackle the challenge is by incorporating learning capability to the swarm to tackle dynamic environment.Originality/valueThis paper gives a new perspective on how to improve automatic swarm design in order to move forward swarm robotics research. Along with the discussion, this paper also proposes a novel framework to incorporate learning mechanism into evolutionary swarm using epigenetic function.

Perpetual Robot Swarm: Long-Term Autonomy of Mobile Robots Using On-the-fly Inductive Charging

Swarm robotics studies the intelligent collective behaviour emerging from long-term interactions of large number of simple robots. However, maintaining a large number of robots operational for long time periods requires significant battery capacity, which is an issue for small robots. Therefore, re-charging systems such as automated battery-swapping stations have been implemented. These systems require that the robots interrupt, albeit shortly, their activity, which influences the swarm behaviour. In this paper, a low-cost on-the-fly wireless charging system, composed of several charging cells, is proposed for use in swarm robotic research studies. To determine the system’s ability to support perpetual swarm operation, a probabilistic model that takes into account the swarm size, robot behaviour and charging area configuration, is outlined. Based on the model, a prototype system with 12 charging cells and a small mobile robot, Mona, was developed. A series of long-term experiments with different arenas and behavioural configurations indicated the model’s accuracy and demonstrated the system’s ability to support perpetual operation of multi-robotic system.

Pheeno, A Versatile Swarm Robotic Research and Education Platform

Swarms of low-cost autonomous robots can potentially be used to collectively perform tasks over very large domains and time scales. Novel robots for swarm applications are currently being developed as a result of recent advances in sensing, actuation, processing, power, and manufacturing. These platforms can be used by researchers to conduct experiments with robot collectives and by educators to include robotic hardware in their curricula. However, existing low-cost robots are specialized and can lack desired sensing, navigation, control, and manipulation capabilities. This letter presents a new mobile robot platform, Pheeno, that is affordable, versatile, and suitable for multirobot research, education, and outreach activities. Users can modify Pheeno for their applications by designing custom modules that attach to its core module. We describe the design of the Pheeno core and a three degree-of-freedom gripper module, which enables unprecedented manipulation capabilities for a robot of Pheeno's size and cost. We experimentally demonstrate Pheeno's ability to fuse measurements from its onboard odometry for global position estimation and use its camera for object identification in real time. We also show that groups of two and three Pheenos can act on commands from a central controller and consistently transport a payload in a desired direction.

The TAM: abstracting complex tasks in swarm robotics research

Research in swarm robotics focuses mostly on how robots interact and cooperate to perform tasks, rather than on the details of task execution. As a consequence, researchers often consider abstract tasks in their experimental work. For example, foraging is often studied without physically handling objects: the retrieval of an object from a source to a destination is abstracted into a trip between the two locations—no object is physically transported. Despite being commonly used, so far task abstraction has only been implemented in an ad hoc fashion. In this paper, we propose a new approach to abstracting complex tasks in swarm robotics research. This approach is based on a physical device called the “task abstraction module” (TAM) that abstracts single-robot tasks to be performed by an e-puck robot. A complex multi-robot task can be abstracted using a group of TAMs by first modeling the task as the set of its constituent single-robot subtasks and then abstracting each subtask with a TAM. We present a collection of tools for modeling complex tasks, and a framework for controlling a group of TAMs such that the behavior of the group implements the model of the task. The TAM enables research on cooperative behaviors and complex tasks with simple, cost-effective robots such as the e-puck—research that would be difficult and costly to conduct using specialized robots or ad hoc task abstraction. We demonstrate how to abstract a complex task with multiple TAMs in an example scenario involving a swarm of e-puck robots.

Colias: An Autonomous Micro Robot for Swarm Robotic Applications

Robotic swarms that take inspiration from nature are becoming a fascinating topic for multi-robot researchers. The aim is to control a large number of simple robots in order to solve common complex tasks. Due to the hardware complexities and cost of robot platforms, current research in swarm robotics is mostly performed by simulation software. The simulation of large numbers of these robots in robotic swarm applications is extremely complex and often inaccurate due to the poor modelling of external conditions. In this paper, we present the design of a low-cost, open-platform, autonomous micro-robot (Colias) for robotic swarm applications. Colias employs a circular platform with a diameter of 4 cm. It has a maximum speed of 35 cm/s which enables it to be used in swarm scenarios very quickly over large arenas. Long-range infrared modules with an adjustable output power allow the robot to communicate with its direct neighbours at a range of 0.5 cm to 2 m. Colias has been designed as a complete platform with supporting software development tools for robotics education and research. It has been tested in both individual and swarm scenarios, and the observed results demonstrate its feasibility for use as a micro-sized mobile robot and as a low-cost platform for robot swarm applications.

Modelling Oil-Spill Detection with Swarm Drones

Nowadays, swarm robotics research is having a great increase due to the benefits derived from its use, such as robustness, parallelism, and flexibility. Unlike distributed robotic systems, swarm robotics emphasizes a large number of robots, and promotes scalability. Among the multiple applications of such systems we could find are exploring unstructured environments, resource monitoring, or distributed sensing. Two of these applications, monitoring, and perimeter/area detection of a given resource, have several ecological uses. One of them is the detection and monitoring of pollutants to delimit their perimeter and area accurately. Maritime activity has been increasing gradually in recent years. Many ships carry products such as oil that can adversely affect the environment. Such products can produce high levels of pollution in case of being spilled into sea. In this paper we will present a distributed system which monitors, covers, and surrounds a resource by using a swarm of homogeneous low cost drones. These drones only use their local sensory information and do not require any direct communication between them. Taking into account the properties of this kind of oil spills we will present a microscopic model for a swarm of drones, capable of monitoring these spills properly. Furthermore, we will analyse the proper macroscopic operation of the swarm. The analytical and experimental results presented here show the proper evolution of our system.

Swarmanoid: A Novel Concept for the Study of Heterogeneous Robotic Swarms

Swarm robotics systems are characterized by decentralized control, limited communication between robots, use of local information, and emergence of global behavior. Such systems have shown their potential for flexibility and robustness [1]-[3]. However, existing swarm robotics systems are by and large still limited to displaying simple proof-of-concept behaviors under laboratory conditions. It is our contention that one of the factors holding back swarm robotics research is the almost universal insistence on homogeneous system components. We believe that swarm robotics designers must embrace heterogeneity if they ever want swarm robotics systems to approach the complexity required of real-world systems.