In general, Robotics is the area concerned with the linking of perception to action, and AI must have a central role in Robotics if the association is to be intelligent. Skiing and Snowboarding are famous winter games worldwide, enjoyed by participants of all ages and skill levels. Leg dominance has been recounted as a probable risk factor in downhill skiers for lower-limb injuries. Furthermore, snowboarders are more likely to injure their ankles than alpine skiers. To overcome these issues, in this paper, the Artificial Intelligence assisted Statistical model (AIASM) has been proposed to the smart robotic supporting leg for skiers and snowboarders. This paper introduces the concept and study of a robotic modular leg (RML) system with a reduced degree of freedom (DOF). The RML gives a perspective on physics that uses dynamic skiing methods and strategies to produce functional ski movements. Kinematic and dynamic models for the leg system are developed and used for modeling tendency, angle, and measurement, unweighting technique to create balanced and realistic curvature turns and peaks. The experimental results show that the suggested system has a performance rate of 95.31% with different ski movements at various intervals, curves, diameters, and peak shapes for tracking the desired footpath.

中文翻译：

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人工智能与机器人技术为滑雪者和滑雪板爱好者提供腿部支撑

一般而言，机器人技术是将感知与行动联系起来的领域，如果关联要变得智能，人工智能必须在机器人技术中发挥核心作用。滑雪和单板滑雪是全球著名的冬季运动，所有年龄和技能水平的参与者都喜欢。腿部优势被认为是速降滑雪者下肢受伤的一个可能风险因素。此外，单板滑雪者比高山滑雪者更容易伤到脚踝。为了克服这些问题，本文提出了人工智能辅助统计模型（AIASM）用于滑雪者和单板滑雪者的智能机器人支撑腿。本文介绍了具有降低自由度 (DOF) 的机器人模块化腿 (RML) 系统的概念和研究。RML 给出了使用动态滑雪方法和策略来产生功能性滑雪运动的物理学观点。腿部系统的运动学和动力学模型被开发并用于对趋势、角度和测量进行建模，加权技术以创建平衡和逼真的曲率转弯和峰值。实验结果表明，所建议的系统在不同间隔、曲线、直径和峰形的不同滑雪运动下具有 95.31% 的性能，以跟踪所需的人行道。