A survey of deep learning for robot path planning: from convolutional networks to generative models

This paper systematically reviews the latest advances in deep learning-based path planning for autonomous mobile robots, addressing the limitations of traditional methods (e.g., A*, RRT) in dynamic, high-dimensional, and unstructured environments. We comprehensively analyze five major deep learning model categories: Convolutional Neural Networks (CNNs) for spatial feature extraction, Graph Neural Networks (GNNs) for multi-agent collaboration, Recurrent Neural Networks (RNNs) for temporal modeling, Transformers for long-range dependency and complex instruction understanding, and generative models (e.g., GANs, Diffusion Models) for creative path generation. Our analysis covers technical principles, advantages, limitations, application scenarios, and development trends of these methods. The review reveals that deep learning has fundamentally transformed path planning from perception enhancement to decision substitution, from isolated agents to multi-agent collaboration, and from search-based to generative paradigms. Key findings indicate significant performance improvements: GNN-based distributed planning triples multi-robot collaboration efficiency, and generative models increase complex instruction planning success rates to 78.1%. Future directions include cross-modal integration, lightweight deployment, simulation-to-reality transfer, and verifiable safety assurance, which will be crucial for advancing next-generation intelligent mobile robot navigation systems.