An MPC Framework For Planning Safe & Trustworthy Robot Motions

被引:14
作者
Eckhoff, Moritz [1 ]
Kirschner, Robin Jeanne [1 ]
Kern, Elena [1 ]
Abdolshah, Saeed [1 ]
Haddadin, Sami [1 ]
机构
[1] Tech Univ Munich, Munich Inst Robot & Machine Intelligence, Chair Robot & Syst Intelligence, D-80797 Munich, Germany
来源
2022 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2022) | 2022年
关键词
MODEL-PREDICTIVE CONTROL; TRACKING;
D O I
10.1109/ICRA46639.2022.9812160
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Strategies for safe human-robot interaction (HRI), such as the well-established Safe Motion Unit, provide a velocity scaling for biomechanically safe robot motion. In addition, psychologically-based safety approaches are required for trustworthy HRI. Such schemes can be very conservative and robot motion complying with such safety approaches should be time efficient within the robot motion planning. In this study, we improve the efficiency of a previously introduced approach for psychologically-based safety in HRI via a Model Predictive Control robot motion planner that simultaneously adjusts Cartesian path and speed to minimise the distance to the target pose as fast as possible. A subordinate real-time motion generator ensures human physical safety by integrating the Safe Motion Unit. Our motion planner is validated by two experiments. The simultaneous adjustment of path and velocity accomplishes highly time efficient robot motion, while considering the human physical and psychological safety. Compared to direct path velocity scaling approaches our planner enables 28% faster motion execution.
引用
收藏
页码:4737 / 4742
页数:6
相关论文
共 28 条
[1]  
[Anonymous], 2016, 15066201602 DIN ISOT
[2]  
Ardakani MMG, 2015, IEEE INT CON AUTO SC, P942, DOI 10.1109/CoASE.2015.7294220
[3]   Constrained model predictive control for mobile robotic manipulators [J].
Avanzini, Giovanni Buizza ;
Zanchettin, Andrea Maria ;
Rocco, Paolo .
ROBOTICA, 2018, 36 (01) :19-38
[4]  
Avanzini GB, 2015, IEEE INT C INT ROBOT, P1473, DOI 10.1109/IROS.2015.7353562
[5]  
Beckert D, 2017, IEEE DECIS CONTR P
[6]  
Brudigam T., 2020, PROC IEEE 23 INT C I, P1
[7]  
Ding H, 2011, LECT NOTES ARTIF INT, V7101, P520, DOI 10.1007/978-3-642-25486-4_52
[8]  
Faroni M, 2019, IEEE INT C EMERG, P1555, DOI [10.1109/ETFA.2019.8869047, 10.1109/etfa.2019.8869047]
[9]   qpOASES: a parametric active-set algorithm for quadratic programming [J].
Ferreau, Hans Joachim ;
Kirches, Christian ;
Potschka, Andreas ;
Bock, Hans Georg ;
Diehl, Moritz .
MATHEMATICAL PROGRAMMING COMPUTATION, 2014, 6 (04) :327-363
[10]   Model Predictive Position and Force Trajectory Tracking Control for Robot-Environment Interaction [J].
Gold, Tobias ;
Voelz, Andreas ;
Graichen, Knut .
2020 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2020, :7397-7402