New Book: Engineering Swarms of Cyber-Physical Systems

We are excited to announce the release of our new book, Engineering Swarms of Cyber-Physical Systems, published by CRC Press in 2025. Authored by Melanie Schranz, Wilfried Elmenreich, and Farshad Arvin, this book is a vital resource for researchers, engineers, and students interested in swarm intelligence and cyber-physical systems (CPS).

This comprehensive guide covers the entire design cycle for applying swarm intelligence in CPS, including modeling, design, simulation, and deployment. Key features include:

• Real-World Examples: Applications in robotics, manufacturing, and search and rescue.
• Hands-On Approach: Programming examples that facilitate quick implementation of concepts.
• Diverse Methodologies: Insights into classical and machine-learning design methods for swarm applications.
• Simulation Insights: A chapter dedicated to simulation requirements and models.

 

Why We Wrote This Book

After years of research, we felt the need for a comprehensive resource that combines theoretical insights with practical applications. Our goal is to inspire creativity and provide the tools necessary for readers to embark on their own projects.

We invite you to explore Engineering Swarms of Cyber-Physical Systems yourself. We hope this book serves as a valuable resource for your research and engineering endeavors!

 

Melanie Schranz, Wilfried Elmenreich, Farshad Arvin. Engineering Swarms of Cyber-Physical Systems. CRC Press 2025. ISBN 978-1-032-04715-7. 

Discovering New Ways to Navigate: A Swarm Intelligence-Based Robotic Search Algorithm Integrated with Game Theory

Robotics has come a long way in the last few decades, and we continue to see innovations in the field as researchers seek to improve robotic search algorithms. Recently, researchers have proposed a decentralize and asynchronous swarm robotic search algorithm integrated with game theory to better disperse robots in the environment while crossing obstacles and solving mazes. This prevents early convergence and improves the efficiency of the searches.

In the proposed algorithm, individual robots, while searching, play a sequential game at each iteration, and based on that, choose their velocity update rule. This strategic game works well in search environments with different levels of complexity and especially improves search efficiency further in complex environments. In the target problem, since the environment is unknown, it is not possible to preplan a path to the target. And there is no difference between static and dynamic obstacles, as the robots cannot distinguish them. Thus, in the proposed method, passing and avoiding obstacles are synchronized with the target searching.

Example of a maze-like complex environment and its mapping to a fitness function

The simulation results showed that, following the proposed algorithm, robots disperse well in search environments, and therefore search speed increases by up to 24% and attended path length to target lessens by up to 23.5% in complex search environments. Also, the proposed algorithm has a success rate equal to the state-of-the-art, which is 100% in all of the tested environments.

For more details check out the paper:

Khalil Alrahman Youssefi Darmian, Modjtaba Rouhani, Habib Rajabi Mashhadi, and Wilfried Elmenreich. A swarm intelligence-based robotic search algorithm integrated with game theory. Applied Soft Computing, 122, 4 2022. (doi:10.1016/j.asoc.2022.108873)

Robotics is an exciting and ever-evolving field, and this new algorithm shows us the potential of swarm intelligence-based robotic search algorithms. We look forward to seeing more innovations in this area as researchers continue to explore new ways to navigate.

Swarm Intelligence and Cyber-Physical Systems

Swarm Intelligence (SI) is a popular multi-agent framework that has been originally inspired by swarm behaviors observed in natural systems, such as ant and bee colonies. In a system designed after swarm intelligence, each agent acts autonomously, reacts on dynamic inputs, and, implicitly or explicitly, works collaboratively with other swarm members without a central control. The system as a whole is expected to exhibit global patterns and behaviors.

When is it advantageous to use a Swarm approach?
The scaling principle depicts a range where a swarm
outperforms a linear system of the same size

Although well-designed swarms can show advantages in adaptability, robustness, and scalability, it must be noted that SI system have not really found their way from lab demonstrations to real-world applications, so far. This is particularly true for embodied SI, where the agents are physical entities, such as in swarm robotics scenarios.

In the paper 

Melanie Schranz, Gianni di Caro, Thomas Schmickl, Wilfried Elmenreich, Farshad Arvin, Ahmet Sekercioglu, and Micha Sende. Swarm Intelligence and Cyber-Physical Systems: Concepts, challenges and future trends. Swarm and Evolutionary Computation, 60, 2020. (doi:10.1016/j.swevo.2020.100762)

we start from these observations, outline different definitions and characterizations, and then discuss present challenges in the perspective of future use of swarm intelligence. These include application ideas, research topics, and new sources of inspiration from biology, physics, and human cognition. To motivate future applications of swarms, we make use of the notion of cyber-physical systems (CPS). CPSs are a way to encompass the large spectrum of technologies including robotics, internet of things (IoT), Systems on Chip (SoC), embedded systems, and so on. Thereby, we give concrete examples for visionary applications and their challenges representing the physical embodiment of swarm intelligence in

• autonomous driving and smart traffic,
• emergency response,
• environmental monitoring,
• electric energy grids,
• space missions,
• medical applications,
• and human networks.

In the future, swarm-based applications will play an important role when there is not enough information to solve the problem in a centralized way, when there are time constraints which do not allow to find an analytical solution, and when the operation needs to be performed in a dynamically changing environment. With an increasing complexity in upcoming applications this will mean that SI will be applied to solve a significant part of ubiquitous complex problems.

Swarm Robotic Behaviors in Real-World Applications

Spiderino - a low-cost robot for swarm
research and educational purposes

With potential benefits from self-organization (e.g., resilience, scalability, and adaptivity to dynamic environments) the motivation is strong to apply swarm robotics in industrial applications. While there exist several swarm robotics research platforms that are developed for educational and scientific purposes, many industrial applications still rely on centralized control. Moreover, in cases where a multi-robot solution is employed, the principal idea of swarm robotics of distributed decision making is often not implemented. To address this topic, the paper

Melanie Schranz, Micha Sende, Martina Umlauft, and Wilfried Elmenreich. Swarm robotic behaviors and current applications. Frontiers in Robotics and AI, 7(36), 2020. (doi:10.3389/frobt.2020.00036)

The e-puck, a robot designed for
education in engineering

provides a collection and categorization of swarm robotic behaviors. Along with this taxonomy, the paper gives a comprehensive overview of research platforms and industrial projects and products, separated into terrestrial, aerial, aquatic, and outer space. In a final discussion, the authors identify several open issues including dependability, emergent characteristics, security and safety, communication as hindrances for the implementation of fully distributed autonomous swarm systems.

The paper was published as part of a Research Topic on Designing Self-Organization in the Physical Realm in the Frontiers in Robotics and AI journal.

In another paper in this issue,

Danesh Tarapore, Roderich Groß, and Klaus-Peter Zauner. Sparse robot swarms: Moving swarms to real-world applications. Frontiers in Robotics and AI, 7(36), 2020. (doi:10.3389/frobt.2020.00083)

the authors address a common property of swarms: the underlying assumption that the robots act in close proximity of each other (for example a few body lengths apart), and typically employ uninterrupted, situated, close-range communication for coordination. Many real-world applications, including environmental monitoring and precision agriculture, however, require scalable groups of robots to act jointly over larger distances (e.g., 1000 body lengths), rendering the use of dense swarms impractical. Using a dense swarm for such applications would be invasive to the environment and unrealistic in terms of mission deployment, maintenance, and post-mission recovery. To address this problem, the paper proposes a sparse swarm concept, which is illustrated via four application scenarios.

The Spiderino Swarm Robot at Research Days 2017

In Klagenfurt from July 10-12, 2017, the Lakeside Research Days were held in collaboration between the Lakeside Labs and the Alpen-Adria-Universität Klagenfurt. Researchers presented their work and discussed open issues in self-organization and swarm intelligence in cyber physical systems.

The Research Days included also laboratory sessions with training on swarm robotics platform. Midhat Jdeed from theAlpen-Adria-Universität gave a lab session about Spiderino, how to program and implement the basic functionalities such as walking, turning and lighting two LEDs using Arduino Studio. In addition, a program has been implemented using sensors distance to make a small swarm of Spiderinos search for the light source and follow it. The sensors employed in Spiderino are CNY70s, which can measure distances and detect obstacles based on the amount of reflected light from an obstacle. This method is prone to ambient light, but it can be also used to make the robots finding a light source.

In the lab session, the Research Days participants learned about the possibilities and programming interface of Spiderino and could implement their own first program hands on. By the end of the session we had a lab of swarming spiders.

More details about Research Days 2017 can be found on the Research Days'17 webpage.

If you want to learn more about the Spiderino swarm robot, check out this paper:

M. Jdeed, S. Zhevzhyk, F. Steinkellner, and W. Elmenreich. Spiderino - a low-cost robot for swarm research and educational purposes In Proceedings of the 13th International Workshop on Intelligent Solutions in Embedded Systems (WISES'17), Hamburg, Germany, June 2017.

Hardware for Swarm Robotics

Collective behavior that emerges from the interactions between agents and with the environment are one of the most prominent examples of self-organizing systems. The field of swarm robotics allows to research and engineer such collective behavior. However to do this, there is the need for small cost-effective swarmbots. 

The table below lists a number of robots used in real experiments in the field of swarm robotics. It shows the most important parameters and information about the robots simplifying comparison between them. For more detailed description there are links (right after the robots' names) to websites or articles exhibiting specifications or conducted experiments.

Robot	Cost	Locomotion	Speed (cm/s)	Size (cm)	Battery life (hours)	Communication
Alice[1]	N/A	wheels	4	2.1x2.1x2.1	10	RF
Jasmine[1,2]	$118	wheels	N/A	2.6x2.6x2.6	1-2	RF, IR
Elisa[1]	$390	wheels	60	∅5	3	RF,IR
Colias[1,2]	$37	wheels	35	∅4	1-3	IR
Kilobot[1,2]	$111	vibration	1	∅3.3	3-24	IR
Flockbots[1]	$500	wheels	N/A	∅18	3	Wi-Fi
E-puck[1,2]	$850	wheels	13	∅7	1-10	Bluetooth, ZigBee
Kobot[1,2]	$1174	wheels	N/A	∅12	10	ZigBee
R-one[1,2]	$322	wheels	30 cm/s	∅10	6	RF, IR
ZeeRO[1]	N/A	wheels	N/A	∅25	N/A	Bluetooth
MinDART[1]	N/A	tracks	17	29x24x37	N/A	None
marXbot[1,2]	N/A	treels	50	∅17	4-7	Wi-Fi
JL-2[1]	N/A	tracks	20	35x25x15	2	Wi-Fi
Khepera IV[1, 2]	$2700	wheels	100	∅14	4-7	Wi-Fi, Bluetooth

One of the first questions, coming up in the beginning of a project concerning swarm robotics, is money. Conducting real experiments with dozens of robots require high investments to buy all needed hardware and software. The price of the robots varies greatly that depends on a number of sensors and actuators, quality of a robot's frame, and demand for a particular robot. Unfortunately, the price of some robots is not specified. The most of these robots have an open-source design and code so they can be built from scratch in case there is such a need.

The table shows that the majority of the robots are differential drive robots whose movement is based on two wheels placed on either side of the robot. It allows achieving higher speed and precisely controlling the direction of movement. However, there are also some disadvantages, such as a need for a prepared surface without pits and bumps. Robots using the tracks usually can move on the unprepared surface, which facilitates a preparation for experiments and demonstrations. A unique type of locomotion is vibration (check the Kilobot robot) which tends to be slow and imprecise way of movement. It also requires a special smooth surface.

Size matters in the field of swarm robotics. A swarm consists of a large number of individuals which are quite effective in performing of important tasks for surviving of the colony. A significant number of robots is important in order to reproduce the swarm behavior or to check a hypothesis.

Unlike individuals in the real swarms, robots cannot generate energy from organics and still require a source of energy such as a battery. The working time of a robot depends on the capacity of battery and the amount of sensor and actuators using by a robot in experiments. Some of the robots have charging stations (e.g. Elisa, Jasmine) which may be useful in conducting of long-term experiments. The marXbot goes even further supporting exchange of the battery in less than 10 seconds without shutting down the power.

Communication in swarm robotics can play a crucial role while the effectiveness of the whole colony depends on how well the robots can send and receive messages. There are two types of communication: abstract (e.g. Wi-Fi, Bluetooth, ZigBee, RF) and situated (e.g. IR). Both of them work well for messages exchange, but situated communication provides additional information about the sender and the message, e.g. strength of the signal and its direction.

The choice of the robot depends on experiments where it will be used. Select the most important features of the future research and pick a robot that fits best.