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.

The coordination of a swarm of simulated robots is proposed as a method for finding paths to a specific target in unknown environments. The solution to creating the most viable pathway to a desired target can be derived through swarm robotics by deploying a bio-inspired exploration algorithm based in a cellular automata model. This algorithm uses virtual pheromones to execute a better dispersion of the agents through the environment in order to decrease the iterations need it to cover it. This model is compared with the classic Random Walk algorithm, which works in a probabilistic and random way. Using the environment map, once obstacles have been identified, an adapted Rapidly-exploring Random Graph (RRG) scheme is developed to structure the environment through a network. From there a Dijkstra path planning algorithm is applied with the aim of defining an optimized route to a specific target. Application of that procedure creates an efficient and effective scheme for finding a path into unknown environments. For this study, performance simulations have been included to refine parameters in order to compare results with different strategies.

In this paper, we present a scheme to solve the problem to explore, cover and define paths in dangerous environments. The proposal is organized in three phases: exploration, mapping and path planning. Swarm robotics is used in the first phase along with a stigmergy approach for the communication between the agents. The explored cell will be stored in order to create a grid map. In the path planning phase, a multiobjective problem is used to minimize distance and danger, through a graph obtained by an adapted RRG, and a genetic algorithm called modified NSGA-II.

This article presents a novel bioinspired technology for the cooperation and coordination of heterogeneous robot swarms in uncontrolled environments, utilizing an artificial pheromone composed of magnetized ferrofluids. Communication between different types of robots is achieved indirectly through stigmergy, where messages are inherently associated with specific locations. This approach is advantageous for swarm experimentation outside controlled laboratory spaces, where localization is typically managed through centralized camera systems (e.g. infrared, RGB). Applying pheromone principles has also proven beneficial for various swarm behaviors. We introduce a detection methodology for the artificial ferrofluid pheromone using low-cost magnetic sensors, along with signal processing and parameter characterization. Experiments involved a heterogeneous swarm consisting of two types of robots: one equipped with camera and image processing capabilities and the other with basic sensor technologies. Validation in multiple uncontrolled environments (with varying floor surfaces, wind, and light conditions) demonstrated successful cooperation among robots with differing technological complexities using the proposed technology.