Map Merge and Accurate Localization in Multi-robot Systems in Real Environments

Abstract

This article presents an approach to map merged and precise position determination in multi-robot systems operating in real environments, using the matrix transformation technique and Particle Swarm Optimization (PSO). The primary objective is to combine information from multiple maps, represented by occupancy matrices, into a unique and comprehensive map. This map can be employed in applications that require cooperation and coordination among robots. To achieve the proposed objective, the PSO technique is applied to find the optimal values of rotation ($\psi$), translation along the x-axis ($dx$), and translation along the y-axis ($dy$) to optimize the map merged. The map merged is performed based on identified correspondences between the maps, using the Jaccard Similarity algorithm. The PSO approach is employed in this work to determine the best possible overlap between the maps. After obtaining the transformation values, they are used to find and update the real positions of the robots on the resulting fused map. Consequently, the proposed technique provides an advanced and efficient solution for map merged and robot positioning in real environments. This approach opens doors for the application of effective cooperation techniques and intelligent navigation for multiple robots in complex and dynamically changing scenarios. An experiment video has been produced and can be accessed through the following link https://youtu.be/RLQsJhlnMuQ.