首页 /研究 /State Estimation and Model-Predictive Control for Multi-Robot Handling and Tracking of AGV Motions using iGPS
SWARM

State Estimation and Model-Predictive Control for Multi-Robot Handling and Tracking of AGV Motions using iGPS

Christoph Storm, Henrik Hose, Robert Schmitt

发表年份
2021
引用次数
3

摘要

In this paper, we present a solution for simultaneous handling of large components with industrial robots performing synchronized motions with an AGV in flexible flow assembly. For this purpose, we implement an Extended Kalman Filter with a global localization system to track an AGV and multiple manipulators. We propose a model-predictive controller for force compliance and trajectory tracking in multi-robot cooperative, decentralized, and fast manipulation tasks. In order to show the effectiveness of our system, we assemble a truck windshield using two industrial robots and an AGV in motion. In our experiments, we reliably achieve assembly tolerances of 1.5mm at AGV velocities up to $400\frac{{{\text{mm}}}}{{\text{s}}}$. The presented system makes flexible assembly systems with AGVs and freely reconfigurable manipulators possible. It enables the automation of high variant, low volume, large size assembly tasks such as aircraft, truck or steel beam assembly, which are mostly manual processes at present.

关键词

TrajectoryRobotAutomated guided vehicleKalman filterAutomationModel predictive controlControl engineeringTracking (education)Control theory (sociology)Computer science

相关论文

查看 SWARM 分类全部论文