动态模型的简化对六关节工业关节型机械臂运动的影响

Mehdi Fazilat, N. Zioui
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引用次数: 0

摘要

本研究探讨了模型简化对 ABB IRB-140 六关节工业机械臂动态性能的影响,主要集中在扭矩预测和能耗方面。根据机械臂的技术规格,我们提出了包括正向、反向、微分运动学和动态模型在内的整个数学模型,并获得了质心和惯性矩阵,它们是动态模型的重要组成部分。利用 Solidworks,我们开发了三个代表机械手的 CAD/CAM 模型,其细节程度各不相同,如简化、半精细和精细。我们的研究结果表明,模型的扭矩和能耗图略有不同。除关节 1 外,半详细模型的能耗最高,详细模型的能耗降低了 0.53%,简化模型的能耗降低了 6.8%。尽管存在这些差异,但所有模型都能有效预测机器人在 30 秒标准任务中的表现,这表明它们适用于各种工业应用。这项研究强调了在选择模型时计算效率和精度之间的平衡。虽然详细模型精度最高,但需要更多计算资源,适用于高精度任务。与此不同的是,简化的、精度较低的模型具有较高的计算效率,因此适用于特定场景。我们的研究为工业机器人选择动态模型提供了重要见解。它指导我们根据所需的任务精度和可用的计算资源优化性能和能效。对动态模型的全面比较强调了它们在不同工业环境中的适用性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Impact of Simplifications of the Dynamic Model on the Motion of a Six-Jointed Industrial Articulated Robotic Arm Movement
This research investigates the impact of model simplification on the dynamic performance of an ABB IRB-140 six-jointed industrial robotic arm, concentrating on torque prediction and energy consumption. The entire mathematical model of forward, reverse, differential kinematics, and dynamic model proposed based on the technical specifications of the arm, and to obtain the center of the mass and inertia matrices, which are essential components of the dynamic model, Utilizing Solidworks, we developed three CAD/CAM models representing the manipulator with varying detail levels, such as simplified, semi-detailed, and detailed. Our findings indicate minor differences in the model's torque and energy consumption graphs. The semi-detailed model consumed the most energy, except for joint 1, with the detailed model showing a 0.53% reduction and the simplified model a 6.8% reduction in energy consumption. Despite these variations, all models proved effective in predicting the robot's performance during a standard 30-second task, demonstrating their adequacy for various industrial applications. This research highlights the balance between computational efficiency and accuracy in model selection. While the detailed model offers the highest precision, it demands more computational resources, which is suitable for high-precision tasks. In discrepancy, simplified, less precise models offer computational efficiency, making them adequate for specific scenarios. Our study provides critical insights into selecting dynamic models in industrial robotics. It guides the optimization of performance and energy efficiency based on the required task precision and available computational resources. This comprehensive comparison of dynamic models underscores their applicability and effectiveness in diverse industrial settings.
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