机器学习在采摘机器人中的应用与挑战

Applications and Challenges of Machine Learning in Picking Robots

技术发展推动机器学习成为自动化与智能化系统的核心技术,机器学习在农业采摘机器人上的广泛应用显示出其巨大的发展潜力与面临的挑战。归纳了机器学习在采摘机器人中应用的现状,讨论遇到的难题,并对未来的发展趋势进行预测。详细探讨了机器学习在图像识别、决策过程和自适应学习方面的实际运用,为采摘机器人的进一步优化与创新提供理论依据和操作指南。

The development of technology has propelled machine learning to become the core technology in automation and intelligent systems.The widespread application of machine learning in agricultural picking robots highlights its huge potential for developement and the challenges it faces.In this paper,the current state of machine learning applications in picking robots is summarized,the challenges encountered are discussed,and future development trends are predicted.The practical applications of machine learning in visual recognition,decision-making processes,and adaptive learning are discussed in detail,providing theoretical foundations and operational guidelines for the further optimization and innovation of picking robots.