Swarm Robots – A Collective Effort

By Samudrapom DamReviewed by Susha Cheriyedath, M.Sc.Updated on Oct 14 2024

Collective behaviors observed in biological systems, such as ant or bee colonies working in harmony to perform complex tasks, have inspired significant research into swarm robotics. With the right algorithms, researchers can replicate these collective behaviors in robots, enabling them to work together to solve problems in dynamic environments.

Image Credits: Chesky/shutterstock.com

Collective Behavior in Biological Systems

Understanding how insects that live in colonies can convey orders and work in coordination to accomplish elaborate plans is a fascinating and complex puzzle. Wasps, for example, illustrate the advantages of a collective approach over individual efforts. A wasp colony, when constructing a nest, divides its labor into distinct roles: pulp foragers, water foragers, and builders. The number of workers in each role is dynamically adjusted based on sensory input from other wasps, enabling the colony to adapt efficiently to changing needs.

Similarly, ants are known for their ability to find food sources and build complex tunnel systems through coordinated efforts. Ants use pheromone trails to communicate directions and resource availability, allowing the entire colony to efficiently locate and gather food. This decentralized form of communication enables ants to adapt quickly if a food source is depleted or if obstacles arise, demonstrating remarkable flexibility and resilience.

Bees also showcase sophisticated collective behaviors, particularly in their foraging and decision-making processes. Scout bees search for new food sources and communicate their findings to the hive through a waggle dance, effectively guiding worker bees to rich food supplies. This type of collective decision-making ensures that the hive optimizes its energy expenditure while maximizing resource collection.

This type of decision-making highlights the challenge of combining individual acts into a cohesive collective behavior—a core aspect of swarm robotics research. The goal is to understand how cooperation can emerge among robots that all share the same basic functional capacity, similar to how social insects divide tasks and solve problems collectively.

Collective performances such as finding food, building nests, and coordinating for defense in insect colonies are all self-organized acts. The colonies adapt their behavior in response to environmental changes, demonstrating a high level of flexibility that is a significant challenge to model in swarm-intelligent systems. While such adaptability has been replicated to some extent in robotic systems, it remains an ongoing focus of research.¹

Figure 1. Cooperation and self-assembly among swarm robots. This video demonstrates the how swarm robots work together to help move an object from one location to another.

Modeling Swarm Robots

Various algorithms have been developed to model swarm robotics, each inspired by different aspects of natural behavior.

• Ant Colony Optimization: This algorithm mimics the behavior of ant colonies, specifically their use of pheromones to communicate and find the shortest path to resources. ACO is commonly used to solve optimization problems, such as route planning in logistics.²
• Self-propelled Particles: This concept models agents that move at a constant speed, responding to random stimuli based on the average motion of neighboring particles. Applied to robots, this helps achieve emergent behaviors where, in the absence of central control, the swarm eventually synchronizes and acts as a unified group.
• Particle Swarm Optimization (PSO): PSO introduces a problem to a swarm sample, where each particle offers a potential solution using stochastic optimization. The best solution found by any particle is shared across the swarm, guiding others to converge on the optimal solution. PSO has been used in tasks such as exploration and resource distribution.³
• Genetic Algorithm (GA): GA draws inspiration from natural selection, using operators like mutation, reproduction, and crossover to evolve solutions to a problem. GA is applied in swarm robotics for tasks such as navigation, path planning, and formation control.⁴
• Artificial Bee Colony (ABC): This algorithm is inspired by the intelligent foraging behavior of honey bees. In ABC, bees communicate information about food sources, enabling division of labor and efficient resource gathering. ABC is particularly useful for scheduling and path planning tasks in multi-robot systems.⁴
• Consensus Algorithm: The goal here is for agents to agree on a common objective, such as forming a specific structure or moving in a coordinated manner. Once consensus is reached, the agents work collectively towards the objective. This is crucial in achieving synchronization in swarm robots.⁵
• Firefly Algorithms: Inspired by the flashing behavior of fireflies, this algorithm is used for optimization. Firefly algorithms are simple and easy to implement, making them a popular choice for initial modeling of swarm behavior.⁷

Applications of Robot Swarms

Impact of Artificial Intelligence on Swarm Robotics

Artificial Intelligence, particularly Reinforcement Learning (RL), has become a powerful tool in controlling swarm behavior. RL treats individual robots as agents in a multi-agent system, allowing them to learn optimal behaviors through interaction with their environment..⁸

Recent advances in AI have led to the development of novel methods, such as the Federated Learning Deep Deterministic Policy Gradient (FLDDPG) algorithm, which enhances the ability of robotic swarms to navigate and avoid obstacles under limited communication conditions. Additionally, neural networks have been applied in real-time to update swarm behavior, further enhancing adaptability in dynamic environments.¹⁰

What is the Future of AI in Robotics?

Conclusion and Future Outlook

Swarm robotics, inspired by the collective behavior of biological systems, is rapidly advancing with the help of algorithms and AI innovations. Recent breakthroughs, such as the implementation of federated learning and real-time neural network applications, demonstrate the potential of robotic swarms to solve complex problems and adapt to changing environments.

In the future, swarm robotics is expected to play a significant role in fields such as precision medicine, enabling personalized therapies that can operate at a microscopic level. Continued research will focus on overcoming challenges in coordination and adaptability, pushing the boundaries of what swarms of simple robots can achieve together.

References and Further Reading

1. Phan, T. A., Russell, R. A. (2011). A swarm robot methodology for collaborative manipulation of non-identical objects. The International Journal of Robotics Research. DOI: 10.1177/0278364911416392, https://journals.sagepub.com/doi/abs/10.1177/0278364911416392
2. Jain, P. (2012) Swarm Robotics [Online] Available at https://www.engineersgarage.com/swarm-robotics/
3. Bonabeau, E., Dorigo, M., Theraulaz, G. (1999) Swarm Intelligence: From Natural to Artificial Systems, Oxford Academic. DOI: 10.1093/oso/9780195131581.001.0001, https://academic.oup.com/book/40811
4. Shahzad, M. M. et al. (2023). A Review of Swarm Robotics in a NutShell. Drones, 7(4), 269. DOI: 10.3390/drones7040269, https://www.mdpi.com/2504-446X/7/4/269
5. Connor, J., Champion, B., Joordens, M. A. (2020). Current algorithms, communication methods and designs for underwater swarm robotics: A review. IEEE Sensors Journal, 21(1), 153-169. DOI: 10.1109/JSEN.2020.3013265, https://ieeexplore.ieee.org/abstract/document/9153840
6. Smith, T., Gutierrez, E., Bredu, J. A., Gu, Y., Gross, J. (2022). Cooperative and coordinated localization of swarm robots using adaptive boids rules. Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022), 2927-2940. DOI: 10.33012/2022.18560, https://www.ion.org/publications/abstract.cfm?articleID=18560
7. Patel, P. (2023). Swarm Intelligence Optimization-Collective Behavior: Investigating collective behavior in swarm intelligence optimization techniques, including swarm robotics, firefly algorithms, and bacterial foraging optimization. Australian Journal of Machine Learning Research & Applications, 3(1), 33-41. https://sydneyacademics.com/index.php/ajmlra/article/view/6
8. Blais, M., Akhloufi, M. A. (2022). Reinforcement learning for swarm robotics: An overview of applications, algorithms and simulators. Cognitive Robotics, 3, 226-256. DOI: 10.1016/j.cogr.2023.07.004, https://www.sciencedirect.com/science/article/pii/S2667241323000241
9. Na, S., Rouček, T., Ulrich, J., Pikman, J., Krajník, T., Lennox, B., Arvin, F. (2023). Federated reinforcement learning for collective navigation of robotic swarms. IEEE Transactions on Cognitive and Developmental Systems, 15(4), 2122-2131.  DOI: 10.1109/TCDS.2023.3239815, https://ieeexplore.ieee.org/abstract/document/10025836
10. Yazici, E., Temeltas, H. (2023). Implementation of Neural Networks in Real-Time Swarm Robotics Applications. 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 4979-4984. DOI: 10.1109/SMC53992.2023.10394278, https://ieeexplore.ieee.org/abstract/document/10394278

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