Advances in Autonomous Robotics: Integrating AI and Machine Learning for Enhanced Automation and Control in Industrial Applications.

Mandeep Singh, Subair Ali Liayakath, Ali Khan
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Abstract

The integration of Artificial Intelligence (AI) and Machine Learning (ML) into autonomous robotics has heralded significant advancements in industrial applications, enhancing operational efficiencies, precision, and adaptability. This paper explores the transformative impact of AI and ML technologies on autonomous robotics in industrial settings, emphasizing the enhancements in automation and control mechanisms. Through a comprehensive literature review and analysis, we discuss the synergistic relationship between AI, ML, and robotics, and how this integration not only improves sensory and decision-making capabilities but also introduces adaptive learning and collaborative functionalities in robotic systems. Our findings reveal that AI-enhanced sensory technologies enable robots to perform complex recognition and manipulation tasks with unprecedented accuracy. Simultaneously, ML algorithms facilitate predictive maintenance, reducing downtime and extending the lifecycle of machinery. Moreover, adaptive learning capabilities allow robots to adjust to new environments and tasks without extensive reprogramming, showcasing significant flexibility and cost-efficiency. The deployment of AI and ML in robotics is not without challenges. The paper identifies key limitations such as data dependency, high computational demands, and adaptability issues. Ethical and societal implications, including job displacement and privacy concerns, are also critically examined to propose a balanced approach towards technology adoption. These include increased investment in R&D, the development of robust ML models, enhanced data governance frameworks, and the establishment of ethical standards to ensure responsible integration of these technologies into industrial practices. By addressing these challenges and leveraging collaborative efforts across sectors, the potential of AI and ML in revolutionizing industrial robotics can be fully realized, leading to a new era of manufacturing excellence.
自主机器人技术的进步:集成人工智能和机器学习,增强工业应用中的自动化和控制。
将人工智能(AI)和机器学习(ML)融入自主机器人技术,预示着工业应用领域的重大进步,可提高运营效率、精度和适应性。本文探讨了人工智能和 ML 技术对工业环境中自主机器人技术的变革性影响,强调了自动化和控制机制的改进。通过全面的文献综述和分析,我们讨论了人工智能、ML 和机器人技术之间的协同关系,以及这种集成如何不仅提高了感知和决策能力,还在机器人系统中引入了自适应学习和协作功能。我们的研究结果表明,人工智能增强的感知技术使机器人能够以前所未有的准确性执行复杂的识别和操纵任务。同时,ML 算法有助于预测性维护,减少停机时间,延长机械的生命周期。此外,自适应学习能力使机器人能够适应新的环境和任务,而无需进行大量的重新编程,从而大大提高了灵活性和成本效益。在机器人技术中部署人工智能和 ML 并非没有挑战。本文指出了一些关键限制因素,如数据依赖性、高计算要求和适应性问题。此外,还批判性地研究了伦理和社会影响,包括工作岗位转移和隐私问题,从而为技术应用提出了一种平衡的方法。其中包括增加研发投资、开发强大的 ML 模型、加强数据管理框架以及建立道德标准,以确保负责任地将这些技术整合到工业实践中。通过应对这些挑战并利用跨部门的合作努力,人工智能和 ML 在革新工业机器人技术方面的潜力可以得到充分发挥,从而开创卓越制造的新时代。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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