Navigation of autonomous mobile robots in dynamic unknown environments based on dueling double deep q networks

This study focuses on applying the algorithm of Dueling Double Deep Q Networks to create a robust and adaptable navigation system for autonomous robots. The main objective of the study is to propose a network model that is capable of making a robot explore unknown areas, avoid static and dynamic obstacles efficiently, recognize predefined targets, and achieve them with high accuracy. Toward this, three different network models have been designed and trained with depth images obtained from a depth camera and directional and distance information from an RGB camera. First, these models were trained and tested in simple and complex simulated environments. The D3QN-C model demonstrated strong performance, achieving a success rate of 89% in the simple environment and of 87% in the complex environment. Tests were further extended by adding real-world data with different obstacle densities in order to prove the strength of the model in increasingly difficult and realistic scenarios. During all tests, the D3QN-C model could sustain high performance, showing 90% success rates in low-density, 85% in medium-density, and 82% in high-density environments. These results are evidence of the efficiency, robustness, and flexibility of this model and underline the potential of the algorithm of the Dueling Double Deep Q Networks as a main tool in using robots within real-world scenarios characterized by dynamics and complexity.