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新型模型基于预测控制的运动暗示算法,用于补偿基于KUKA机器人车的驾驶模拟器中的离心加速

Duc-An Pham¹, Trung Nghia Pham¹, Duc-Toan Nguyen¹

¹School of Mechanical Engineering, Hanoi University of Science and Technology, Hanoi City, Vietnam.

Science progress

|October 3, 2023

概括

对于库卡Robocoaster驾驶模拟器而言,一种新的冲洗运动暗示算法 (MCA) 使用循环运动来准确模拟横向运动. 这种模型预测控制 (MPC) 方法最大限度地减少了虚假运动线索,以增强驾驶员的沉浸感.

科学领域:

机器人技术机器人技术机器人技术
人与计算机的交互
汽车工程汽车工程

背景情况:

动作暗示算法 (MCAs) 对于现实的驾驶模拟至关重要,旨在准确地复制动作暗示.
传统的MCA通常是为特定的平台设计的,比如斯图尔特平台.
库卡Robocoaster为驾驶模拟器提供了一个经济的替代方案,但需要专门的轨迹转换.

研究的目的:

开发一种针对基于KUKA Robocoaster的驾驶模拟器量身定制的新型运动暗示算法 (MCA).
为了提高运动感应的准确性,并优化库卡Robocoaster模拟器的工作空间利用率.
为了应对模拟使用循环运动和减轻离心力的横向运动的挑战.

主要方法:

实施基于MCA的新型模型预测控制 (MPC) 为KUKA Robocoaster平台.
使用大型平面圆形运动来模拟横向驾驶员的运动.
采用斜倾角来抵消破坏性的离心力,并保持控制的角速度.

主要成果:

拟议的MPC成功生成模拟运动,精确追踪横向特定力目标.
滚动和俯冲角速度保持在各自的值以下.
模拟结果表明,在横向加速任务期间,在滚动/摇摆和俯冲/冲动通道中消除了虚假运动线索.

结论:

基于MPC的新型MCA有效地利用了KUKA Robocoaster平台的驾驶模拟能力.
这种方法提供了准确和沉浸式的运动线索,克服了以前的MCAs的局限性.
这项研究为KUKA Robocoaster模拟器中的运动提示提供了一个原创的解决方案,提高了模拟保真度.

关键词:

卡拉拉卡拉拉卡拉拉库卡的机器人火车站运动提示算法的算法.驾驶模拟器上的驾驶模拟器模型预测控制模型预测控制

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