Transformer-Based Visual Perception Models for Autonomous Robots

  • Authors

    • H. N. Mahabala Computer Scientist, Tata Institute of Fundamental Research, India Author

    DOI:

    https://doi.org/10.67228/30715725/IJIARE-2025PII4Q8N

    Published 11-03-2025

  • Autonomous Robots, Visual Perception, Vision Transformer (Vit), Self-Attention Mechanism, Deep Learning, Object Detection, Semantic Segmentation, Multi-Modal Sensor Fusion, Intelligent Robotics, Artificial Intelligence, Computer Vision, Scene Understanding

    Issue

    Vol. 8 No. 2 (2025)

    Section

    Articles

    How to Cite

    [1]
    H. N. Mahabala, “Transformer-Based Visual Perception Models for Autonomous Robots”, IJIARE, vol. 8, no. 2, pp. 01–16, Nov. 2025, doi: 10.67228/30715725/IJIARE-2025PII4Q8N.

  • Abstract

    Autonomous robots play a crucial role in industrial manufacturing, healthcare, transportation, logistics, agriculture, disaster response, planetary exploration, and service robotics. Reliable visual perception is essential for enabling robots to recognize objects, understand scenes, localize themselves, and navigate safely in dynamic environments. Although CNN-based vision models have significantly improved perception accuracy, they often struggle to capture long-range dependencies and generalize to complex or unseen environments. Recent advances in Transformer-based vision models address these limitations by employing self-attention mechanisms to learn both local visual features and global contextual relationships. Architectures such as Vision Transformer (ViT), Swin Transformer, DETR, SAM, and Mask2Former have achieved remarkable performance in object detection, semantic segmentation, SLAM, localization, obstacle avoidance, and autonomous navigation. This paper presents a comprehensive review and proposes the Transformer-Based Visual Perception Models for Autonomous Robots (TBVPM-AR) framework. The framework integrates RGB cameras, depth sensors, LiDAR, IMUs, multimodal sensor fusion, transformer-based feature extraction, contextual reasoning, and edge-cloud computing to achieve robust perception in dynamic environments. Mathematical formulations for self-attention, positional encoding, and feature embedding provide the theoretical foundation of the architecture. Experimental evaluations demonstrate that the proposed framework outperforms CNN-based and hybrid approaches on standard robotic perception benchmarks, achieving over 98% visual perception accuracy with improved scene understanding, localization, obstacle detection, navigation, and computational efficiency. The proposed architecture offers a scalable, explainable, and adaptable solution for future Industry 5.0, collaborative robotics, autonomous vehicles, and smart cyber-physical systems.

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