Swarm robotics is a field that imitates collective behavior observed in nature, enabling the execution of distributed tasks such as exploration, foraging, and predator-prey strategies. One of the recent approaches in this field is the use of the RAOI parameter algorithm, designed to regulate swarm behavior in various collaborative tasks. However, most previous studies have focused on optimizing decision-making and swarm coordination without explicitly considering the robots’ energy consumption. Since energy efficiency is a crucial aspect in the implementation of autonomous robotic swarms, one of the proposed improvements to the RAOI algorithm is the inclusion of battery level as an additional parameter that influences decision-making and task distribution within the swarm. This work conducts a detailed evaluation of the energy expenditure of the current algorithm during the execution of a foraging task. Based on the obtained results, possible strategies for optimizing energy consumption are analyzed, allowing for the development of an improved version of the RAOI algorithm. With these modifications, the aim is to achieve a more efficient swarm behavior, maximizing the robots’ operational time and enhancing their performance in real-world environments.