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<p>This paper presents an innovative adaptive control system for collaborative sorting robotic arms achieving three key technical breakthroughs: (1) a multimodal sensor fusion algorithm integrating vision, force, and position sensors with dynamic reliability weighting achieving 98.7% sorting accuracy, (2) a distributed edge computing architecture enabling 3.2ms average response time through local processing optimization, and (3) adaptive control mechanisms with online learning capabilities delivering 847 items/hour throughput capacity. The system combines advanced fusion algorithms with machine learning techniques to optimize performance under varying operational conditions including payload changes, environmental disturbances, and collaborative coordination requirements. Experimental validation demonstrates 15% improvement in accuracy over commercial systems, 60% reduction in communication latency compared to centralized architectures, and robust operation with maintained performance above 85% during sensor failures. The research contributes to intelligent manufacturing advancement by providing a scalable framework for collaborative robotic sorting applications that adapts to dynamic production requirements while maintaining optimal performance metrics in Industry 4.0 implementations.</p>

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Adaptive control system for collaborative sorting robotic arms based on multimodal sensor fusion and edge computing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月29日

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  • Yanfang Feng

摘要

This paper presents an innovative adaptive control system for collaborative sorting robotic arms achieving three key technical breakthroughs: (1) a multimodal sensor fusion algorithm integrating vision, force, and position sensors with dynamic reliability weighting achieving 98.7% sorting accuracy, (2) a distributed edge computing architecture enabling 3.2ms average response time through local processing optimization, and (3) adaptive control mechanisms with online learning capabilities delivering 847 items/hour throughput capacity. The system combines advanced fusion algorithms with machine learning techniques to optimize performance under varying operational conditions including payload changes, environmental disturbances, and collaborative coordination requirements. Experimental validation demonstrates 15% improvement in accuracy over commercial systems, 60% reduction in communication latency compared to centralized architectures, and robust operation with maintained performance above 85% during sensor failures. The research contributes to intelligent manufacturing advancement by providing a scalable framework for collaborative robotic sorting applications that adapts to dynamic production requirements while maintaining optimal performance metrics in Industry 4.0 implementations.