Muhammad Muzamal Shahzad¹,²; Zubair Saeed³; Asima Akhtar¹; Hammad Munawar²; Muhammad Haroon Yousaf¹,³,*; Naveed Khan Baloach¹,³; Fawad Hussain³
¹ Swarm Robotics Laboratory (SRL), National Center of Robotics and Automation (NCRA), Taxila 47050, Pakistan
² Department of Avionics Engineering, College of Aeronautical Engineering, National University of Science and Technology (NUST), Islamabad 44000, Pakistan
³ Department of Computer Engineering, University of Engineering and Technology (UET), Taxila 47050, Pakistan
Correspondence should be addressed to Muhammad Haroon Yousaf.
Drones 2023, 7(4), 269; DOI: https://doi.org/10.3390/drones7040269
Submission received: 10 March 2023 / Revised: 9 April 2023 / Accepted: 10 April 2023 / Published: 14 April 2023
(This article is part of the Special Issue on Intelligent Coordination of UAV Swarm Systems)
Abstract
Swarm robotics involves coordinating many simple robots that work together to accomplish tasks more efficiently than a single robot could. This field has attracted significant attention over the past decade due to its potential in a wide range of applications—both military and civilian—such as exploration, search and rescue, surveillance, agriculture, air defense, area coverage, real-time monitoring, wireless service provisioning, and delivery systems. Inspired by the self-organizing behaviors of natural systems like bee colonies, fish schools, and bird flocks, swarm robotics relies on a set of local interaction rules that guide individual agents. Unlike typical multi-agent systems, a true robot swarm must consist of at least three autonomous units that share relative information (e.g., position, altitude, velocity) and operate without a centralized controller, remaining robust even if individual members leave the swarm. This review outlines the basic principles of swarm systems, traces their historical development, and projects future directions. It also discusses essential features of swarm robots, simulation environments, real-world applications, and innovative ideas for future research.
Keywords: swarm intelligence, swarm behaviors, swarm robotics, industrial swarm, swarm robotics applications
1. Introduction
Swarm robotics describes the collective operation of multiple individual units that cooperate without central control by relying on simple, local behaviors. With improvements in robot technology—especially in unmanned aerial systems (UAS)—robots are now more affordable and capable, making them viable solutions for tasks ranging from disaster response to environmental mapping. A robot qualifies as part of a swarm if it meets three key criteria: it is one of three or more units, it operates with little or no human intervention, and it collaborates based on a basic set of rules.
The essence of swarm robotics is that a group of autonomous robots work together toward a common goal without relying on an external infrastructure or centralized command. In contrast to sensor networks or systems where tasks are individually assigned by different operators, true swarms feature collective behaviors where every member follows the same interaction rules. This paper examines the evolution of swarm robotics—from algorithmic concepts to practical deployment—and highlights the differences between multi-agent systems and swarms, along with their applications and challenges.
2. Fundamental Behaviors in Swarm Robotics
Swarm algorithms emerge from the interaction of individual agents following local rules. These interactions, which involve sensing the environment and processing that information to drive actuators, give rise to overall collective behavior. The key behaviors can be categorized as follows:
2.1 Spatial Organization
Robots within a swarm can rearrange themselves spatially in response to environmental cues.
- Object Clustering and Assembly: Robots work together to gather or construct structures, which is crucial in construction tasks.
- Pattern Formation and Chain Formation: Swarms can arrange into specific shapes or lines, for example, to create multi-hop communication links.
- Self-Assembly and Morphogenesis: Robots physically connect or coordinate remotely to form predetermined structures.
- Aggregation: Individual robots converge at specific locations to enhance interaction.
2.2 Navigation
Swarm navigation allows robots to move cohesively:
- Collective Localization: Each unit determines its position relative to others, establishing a shared coordinate system.
- Collective Transport: The group can move objects that are too heavy for any one robot.
- Coordinated Motion: Robots maintain a defined formation (e.g., a line or triangle) similar to flocking behaviors.
- Collective Exploration: The swarm scouts an area to gather information or establish communication networks.
2.3 Decision Making
Collective decision-making is critical for task allocation and fault tolerance within a swarm:
- Group Size Regulation: Swarms can adjust their size by splitting into subgroups if needed.
- Collective Fault Detection: The system identifies and isolates malfunctioning units.
- Synchronization and Collective Perception: Robots align their actions in time and share data to form a unified view of the environment, leading to informed decisions.
- Task Allocation and Consensus: Tasks are dynamically assigned among robots, and consensus is reached to converge on the best strategy.
2.4 Miscellaneous Behaviors
Additional behaviors include:
- Self-Healing: The swarm can recover from individual failures, enhancing reliability.
- Self-Reproduction: New robots or patterns can be generated autonomously, reducing human intervention.
- Human-Swarm Interaction: Enables effective communication between humans and robot swarms, either remotely or in shared spaces.
3. Swarm Intelligence Algorithms
Swarm Intelligence (SI) encompasses decentralized, collective problem-solving methods. Common examples include genetic algorithms (GA), ant colony optimization (ACO), particle swarm optimization (PSO), differential evolution (DE), artificial bee colony (ABC), glowworm swarm optimization (GSO), and the cuckoo search algorithm (CSA). These algorithms mimic natural processes such as evolution, foraging behavior, and social interactions to solve complex problems.
3.1 Genetic Algorithm
Introduced by John Holland in 1975, genetic algorithms mimic natural selection. A population of candidate solutions is evolved using operations like crossover, mutation, and reproduction. The fitness of each solution is assessed, and the process continues until an optimal solution emerges. Applications span navigation, path planning, scheduling, machine learning, robotics, and more.
3.2 Ant Colony Optimization
Proposed by Marco Dorigo in 1992, ACO is based on the foraging behavior of ants, which deposit pheromones to mark favorable paths. The algorithm involves four main components: the ant (agent), pheromone levels, a global data collection (daemon action), and decentralized control. Over iterations, ants probabilistically select paths, reinforcing shorter or more efficient routes. ACO has been applied to vehicle routing, network modeling, path planning for robots and UAVs, and project management.
3.3 Particle Swarm Optimization
Developed by Kennedy and Eberhart in 1995, PSO simulates the social behaviors observed in bird flocking and fish schooling. Particles (candidate solutions) adjust their positions and velocities based on their own experience and that of their neighbors, balancing exploration and exploitation. PSO is valued for its simplicity, ease of configuration, and has been used in networking, power systems, control, machine learning, and image processing.
3.4 Differential Evolution
Differential Evolution (DE), introduced by Price and Storn in 1997, is similar to GA but emphasizes mutation over crossover. DE generates new candidate solutions by combining differences between randomly selected population vectors. This method has been effective in fields such as robot path planning, image processing, engineering design, and economics.
3.5 Artificial Bee Colony
The Artificial Bee Colony (ABC) algorithm, presented by Karaboga in 2005, is inspired by the foraging behavior of honey bees. In ABC, agents are categorized as employed, onlooker, or scout bees, each with specific roles in exploring and exploiting food sources. The simplicity and robustness of ABC make it useful in path planning, engineering, networking, scheduling, and image processing, though it may require additional fitness evaluations for high performance.
3.6 Glowworm Swarm Optimization
Glowworm Swarm Optimization (GSO), introduced in 2005, uses the concept of glowworms whose brightness (luciferin level) guides their movement. Each glowworm updates its position based on local interactions and the brightness of its neighbors. While GSO is effective for multimodal optimization and problems that require a limited sensing range, it can suffer from slow convergence.
3.7 Cuckoo Search Algorithm
Proposed in 2009 by Yang and Deb, the Cuckoo Search Algorithm (CSA) is inspired by the brood parasitism of cuckoos and the flight patterns known as Lévy flights. In CSA, each cuckoo lays an egg in a randomly chosen nest, and the best solutions are retained over generations. This algorithm is attractive for its multi-objective handling and minimal parameter tuning, with applications in UAV path planning, neural networks, embedded systems, and solving the traveling salesman problem.
4. Applications of Swarm Robotics
Swarm robotics remains a growing research field with several academic prototypes and emerging industrial deployments. Applications are explored across research platforms and real-world products.
4.1 Research Platforms
Research systems are designed to test swarm algorithms in controlled settings and can be classified by their operational environment:
4.1.1 Terrestrial
Platforms like the kilobot swarm—comprising over a thousand small, inexpensive robots—demonstrate self-assembly and spatial organization. Other notable systems include open-source platforms such as Jasmin, Alice, AMiR, Colias, Mona, and R-One, which provide various sensing and communication capabilities. Additionally, specialized robots like the e-puck series, Thymio II, and Spiderino are used for educational and research purposes. Microrobotic systems like I-Swarm and the Zooids platform illustrate how tiny, solar-powered robots or innovative human–machine interfaces can achieve swarming behavior.
4.1.2 Aerial
Miniature unmanned aerial vehicles (μUAVs) are used to study swarm behaviors in flight. Commercial platforms such as Crazyflies and FINken-III feature advanced sensors and communication modules, enabling coordinated aerial operations. The Distributed Flight Array and similar setups demonstrate how individual aerial units can combine to form a functional multirotor system.
4.1.3 Aquatic
Underwater and surface robotic swarms, including heterogeneous unmanned underwater vehicles (UUVs) and unmanned surface vehicles (USVs), are applied in environmental monitoring, pollution assessment, and maritime research. Projects like CoCoRo, Monsun, and CORATAM illustrate how aquatic swarms communicate using sonar, electric fields, and acoustic modems.
4.1.4 Outer Space
NASA’s initiatives, such as swarmies and Marsbees, show how small, autonomous spacecraft can work together to explore extraterrestrial environments. These projects focus on in-situ resource utilization and environmental data collection using swarm-based strategies.
4.2 Industrial Projects and Products
Industrial applications of swarm robotics span various domains:
4.2.1 Terrestrial
In agriculture, systems like SwarmBot 3.0 and the UGV Xaver enable autonomous field monitoring, seeding, and harvesting by dividing large areas among individual robots. Other applications include emergency and rescue operations (e.g., the GUARDIANS project) and warehouse automation, as seen in the Ocado, Amazon Kiva, and Alibaba systems, where swarms of robots manage order fulfillment and dispatch.
4.2.2 Aerial
Military applications such as the OFFSET and Perdix projects utilize UAV swarms for surveillance, hazard detection, and covert operations. Additionally, aerial swarms are used in emergency response to establish communication networks and in entertainment through coordinated light shows.
4.2.3 Aquatic
Autonomous boats and underwater vehicles—such as those in the Platypus, Apium Data Diver, and Hydromea’s Vertex Swarm projects—monitor water quality, map underwater features, and assist in offshore operations. Systems like SWARMs focus on reliable underwater communication and coordinated maritime activities.
4.2.4 Outer Space
Space-based swarms have been deployed for Earth observation and magnetosphere research. Missions such as Swarm (a group of three satellites) and Cluster II (a tetrahedral satellite formation) exemplify how coordinated spacecraft can gather three-dimensional environmental data.
5. Past, Present, and Future Perspectives
Natural systems like insect colonies, fish schools, and bird flocks have long inspired the development of swarm robotics. Early research focused on computational models of collective behavior, and since the early 2000s, significant progress has been made toward deploying actual robot swarms. Future applications envision swarms that can cooperate in both fully cooperative and semi-cooperative environments—ranging from autonomous vehicles to precision medical devices capable of minimally invasive surgery. While laboratory demonstrations typically involve small-scale prototypes, advances in technology are pushing toward larger, more capable swarms that can operate in varied environments and over extended periods. The historical evolution—from theoretical algorithms to industrial hardware—reveals a clear path toward real-world applications, even though challenges remain in bridging the gap between simulation and practical deployment.
6. Conclusions
Swarm robotics seeks to develop simple, self-governing robots capable of collective problem solving without central control. Progress in this field has evolved from early simulations to sophisticated hardware implementations. However, a significant gap persists between theoretical models and industrially viable systems. While academic research has focused primarily on algorithmic simulations, practical implementations demand hardware that can support complex swarm behaviors. This review has outlined the key differences between multi-agent systems and true robot swarms, detailed the core behaviors and algorithms that drive swarm intelligence, and provided an overview of both research platforms and industrial applications. By highlighting these challenges and opportunities, the article aims to guide new researchers and encourage the bridging of theoretical and practical efforts in swarm robotics.
