Distributed efficient localization in swarm robotics using Min–Max and Particle Swarm Optimization

• We propose an efficient localization method for swarm robotic systems.
• The method, based on Min–Max and PSO algorithms, presents low positioning error.
• We demonstrate that the method overcomes the drawback of the Min–Max method.
• It is shown that the processing time grows with the swarm connectivity.

In a wireless sensors network in general, and a swarm of robots in particular, solving the localization problem consists of discovering the sensor’s or robot’s positions without the use of external references, such as the Global Positioning System – GPS. In this problem, the solution is performed based on distance measurements to existing reference nodes also known as anchors. These nodes have knowledge about their respective positions in the environment. Aiming at efficient yet accurate method to approach the localization problem, some bio-inspired algorithms have been explored. In this sense, targeting the accuracy of the final result rather than the efficiency of the computational process, we propose a new localization method based on Min–Max and Particle Swarm Optimization. Generally, the performance results prove the effectiveness of the proposed method for any swarm configuration. Furthermore, its efficiency is demonstrated for high connectivity swarms. Specifically, the proposed method was able to reduce the localization average error by 84%, in the worst case, considering a configuration of 10 anchors and 100 unknown nodes and by almost 100%, in the best case, considering 30 anchors and 200 unknown nodes. This proves that for high connectivity networks or swarms, the proposed method provides almost exact solution to the localization problem, which is a big shift forward in the state-of-the-art methods.