Path planning is a very important problem in robotics, especially in the development of Automatic Guided Vehicles (AGVs). These problems are usually formulated as search problems, so many search algorithms with a high level of intelligence are evaluated to solve them. We propose a navigation algorithm based on bacterial swarming from a simplified model of bacterium that promises simple designs both at the system level and at the agent level. The most important feature of the algorithm is the inclusion of bacterial Quorum Sensing (QS), which reduces the convergence time, which is the major disadvantage of the scheme. The results in both simulation and real prototypes show not only stability but higher performance in convergence speed, showing that the strategy is feasible and valid for decentralized autonomous navigation.