End-to-end robot intelligent obstacle avoidance method based on deep reinforcement learning with spatiotemporal transformer architecture.

To enhance the obstacle avoidance performance and autonomous decision-making capabilities of robots in complex dynamic environments, this paper proposes an end-to-end intelligent obstacle avoidance method that integrates deep reinforcement learning, spatiotemporal attention mechanisms, and a Transformer-based architecture. Current mainstream robot obstacle avoidance methods often rely on system architectures with separated perception and decision-making modules, which suffer from issues such as fragmented feature transmission, insufficient environmental modeling, and weak policy generalization. To address these problems, this paper adopts Deep Q-Network (DQN) as the core of reinforcement learning, guiding the robot to autonomously learn optimal obstacle avoidance strategies through interaction with the environment, effectively handling continuous decision-making problems in dynamic and uncertain scenarios. To overcome the limitations of traditional perception mechanisms in modeling the temporal evolution of obstacles, a spatiotemporal attention mechanism is introduced, jointly modeling spatial positional relationships and historical motion trajectories to enhance the model's perception of critical obstacle areas and potential collision risks. Furthermore, an end-to-end Transformer-based perception-decision architecture is designed, utilizing multi-head self-attention to perform high-dimensional feature modeling on multi-modal input information (such as LiDAR and depth images), and generating action policies through a decoding module. This completely eliminates the need for manual feature engineering and intermediate state modeling, constructing an integrated learning process of perception and decision-making. Experiments conducted in several typical obstacle avoidance simulation environments demonstrate that the proposed method outperforms existing mainstream deep reinforcement learning approaches in terms of obstacle avoidance success rate, path optimization, and policy convergence speed. It exhibits good stability and generalization capabilities, showing broad application prospects for deployment in real-world complex environments.