TY - GEN
T1 - A constraint programming approach to simultaneous task allocation and motion scheduling for industrial dual-arm manipulation tasks
AU - Behrens, Jan Kristof
AU - Lange, Ralph
AU - Mansouri, Masoumeh
PY - 2019/5/20
Y1 - 2019/5/20
N2 - Modern lightweight dual-arm robots bring the physical capabilities to quickly take over tasks at typical industrial workplaces designed for workers. Low setup times - including the instructing/specifying of new tasks - are crucial to stay competitive. We propose a constraint programming approach to simultaneous task allocation and motion scheduling for such industrial manipulation and assembly tasks. Our approach covers the robot as well as connected machines. The key concept are Ordered Visiting Constraints, a descriptive and extensible model to specify such tasks with their spatiotemporal requirements and combinatorial or ordering constraints. Our solver integrates such task models and robot motion models into constraint optimization problems and solves them efficiently using various heuristics to produce makespan-optimized robot programs. For large manipulation tasks with 200 objects, our solver implemented using Google's Operations Research tools requires less than a minute to compute usable plans. The proposed task model is robot-independent and can easily be deployed to other robotic platforms. This portability is validated through several simulation-based experiments.
AB - Modern lightweight dual-arm robots bring the physical capabilities to quickly take over tasks at typical industrial workplaces designed for workers. Low setup times - including the instructing/specifying of new tasks - are crucial to stay competitive. We propose a constraint programming approach to simultaneous task allocation and motion scheduling for such industrial manipulation and assembly tasks. Our approach covers the robot as well as connected machines. The key concept are Ordered Visiting Constraints, a descriptive and extensible model to specify such tasks with their spatiotemporal requirements and combinatorial or ordering constraints. Our solver integrates such task models and robot motion models into constraint optimization problems and solves them efficiently using various heuristics to produce makespan-optimized robot programs. For large manipulation tasks with 200 objects, our solver implemented using Google's Operations Research tools requires less than a minute to compute usable plans. The proposed task model is robot-independent and can easily be deployed to other robotic platforms. This portability is validated through several simulation-based experiments.
UR - http://www.scopus.com/inward/record.url?scp=85071485177&partnerID=8YFLogxK
U2 - 10.1109/ICRA.2019.8794022
DO - 10.1109/ICRA.2019.8794022
M3 - Conference contribution
SN - 9781538681763
T3 - International Conference on Robotics and Automation (ICRA)
SP - 8705
EP - 8711
BT - 2019 International Conference on Robotics and Automation (ICRA)
PB - IEEE Computer Society Press
T2 - 2019 International Conference on Robotics and Automation (ICRA)
Y2 - 20 May 2019 through 24 May 2019
ER -