Arquitetura modular para navegação autônoma com aceleração de aprendizagem, fusão sensorial e comunicação colaborativa

Abstract

This work proposes a modular architecture for autonomous navigation of mobile robots in dynamic environments, integrating functional modules: (i) intelligent control through deep reinforcement learning, (ii) fusion of visual and non-visual sensors, (iii) a visionbased anti-collision safety module, and (iv) collaborative hybrid communication. The proposed EKF-DQN algorithm accelerates the learning process by integrating state predictions provided by the Extended Kalman Filter. The sensor fusion system combines data from visual and non-visual sensors through strategies such as Late Fusion, allowing the agent to acquire a robust perception of the environment. For operational safety, a person detection module was implemented to identify collision risks. In the communication layer, a hybrid protocol is proposed that combines: (i) LoRa technology, known for its long range and low power consumption, used for communication with remote stations (R2I); and (ii) the DDS middleware, used for short-range robot-to-robot (R2R) communication, ensuring real-time synchronization and sensory data exchange. To enhance the robustness of LoRa connectivity, a new algorithm called “Forced Data Rate” was developed, which adaptively enforces the spreading factor based on link quality, outperforming the traditional Adaptive Data Rate (ADR) algorithm in scenarios with constant mobility. To enable the transmission of high-resolution sensory data, such as those generated by LiDAR sensors, a compression technique based on autoencoders was used, achieving an average payload size reduction of 82% without significant loss of accuracy. Results obtained from both simulations and real-world robot experiments demonstrated significant improvements in navigation performance. The proposed EKF-DQN achieved a 93.33% success rate and outperformed the traditional D3QN. With the integration of the safety module, a 72% success rate was achieved in environments with moving people.