Real-Time Model Predictive Control for Energy-Optimal Obstacle Avoidance in Parallel SCARA Robot for a Pick and Place Application

被引：0
|
作者：
Singh, Taranjitsingh [1 ]
Mrak, Branimir [1 ]
Gillis, Joris [2 ]
机构：
[1] Flanders Make, MotionS, Lommel, Belgium
[2] Katholieke Univ Leuven, Dept Mech Engn, Leuven, Belgium
来源：
2024 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS, CCTA 2024 | 2024年
关键词：
D O I：
10.1109/CCTA60707.2024.10666612
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This article presents a nonlinear model predictive control (NL-MPC) design and real-time implementation for energy-optimal obstacle avoidance of a parallel Selective Compliance Articulated Robot Arm (SCARA) robot for a pick and place application. The NL-MPC problem is solved online using a real-time iteration scheme that guarantees the computational time within the solving time for the pick and place application with obstacle avoidance. The proposed approach is tested on a conveyor belt simulation scenario with dynamic obstacles and compared with a conventional industrial application solution. The results show that the NL-MPC approach can improve the energy efficiency by approx. 25% while avoiding the obstacles successfully.
引用
收藏
页码：694 / 701
页数：8
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