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.

This paper presents a methodology for assessing the impact of electric vehicles (EVs) on the power transmission grid of the Costa Rica Power System. The methodology considers penetration scenarios, user preferences, charging habits, and expected fleet growth. Using ETAP software, the study simulates power flow, demand behavior, and voltage levels in the presence of high penetration of electric vehicles. The analysis covers a 15-year horizon and focuses on voltage and demand profiles in 2025, 2030, and 2040. The results indicate a decline in voltage profiles that reaches dangerous levels after 2030, primarily in the distribution grid, and an increase in demand by Image 1 for 2040 in the most severe scenario. The analysis also reveals several key findings (a) the identification of problems in the electrical infrastructure starting in 2030 and a major insufficiency in accommodating the increase in EVs by 2040; (b) the need to evaluate stability in transmission grids considering loadability and voltage; (c) the necessity of investing in electrical infrastructure, driven by public policies, to meet future energy requirements and strengthen transmission networks; (d) the significance of accounting for both EV growth and electric infrastructure improvements in system analysis; and (e) the anticipation that the system's performance will fall within the extreme demand values presented in the analysis. The study emphasizes the importance of considering a broader range of scenarios and variability in parameters, especially user charging behaviors, to enable decision-makers to plan for the challenges and opportunities associated with the widespread adoption of EVs in a country's power grid.

Fossil fuel-based transportation is the main contributor to air pollution in the world. In this regard, electric vehicles (EVs) provide a viable and clean alternative. This paper shows a mathematical model to forecast EVs penetration, it also presents an application in Costa Rica Power System. A review of three forecasting methods is presented. These methods are analyzed and two of them are used to predict EVs penetration in the country for the next five years. Also, a framework of network conditions to integrate this type of technology is evaluated. ETAP software is employed to analyze the loadability and demand response of the results obtained in the EVs penetration. One scenario (the high bound) shows that by year 2025, 10315 EVs will be in circulation, this participation represents a 2.11% of the power grid. So that, new opportunities to research and analyze are also emerging for the power system under various study scenarios that can aid in power planning. The present paper provides an application and implementation in large power systems that can be used in other systems of the same size.

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.