Robot Trajectory Optimization for the Relaxed End-effector Path

Sergey Alatartsev, Anton Belov, Mykhaylo Nykolaychuk, Frank Ortmeier

2014

Abstract

In this paper we consider the trajectory optimization problem for the effective tasks performed by industrial robots, e.g., welding, cutting or camera inspection. The distinctive feature of such tasks is that a robot has to follow a certain end-effector path with its motion law. For example, welding a line with a certain velocity has an even influence on the surface. The end-effector path and its motion law depend on the industrial process requirements. They are calculated without considering robot kinematics, hence, are often “awkward” for the robot execution, e.g., cause high jerks in the robot’s joints. In this paper we present the trajectory optimization problem where the end-effector path is allowed to have a certain deviation. Such path is referred to as relaxed path. The goal of the paper is to make use of this freedom and construct the minimal-cost robot trajectory. To demonstrate the potential of the problem, jerk of the robot joint trajectory was minimized.

References

  1. Alatartsev, S., Mersheeva, V., Augustine, M., and Ortmeier, F. (2013). On optimizing a sequence of robotic tasks. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
  2. Alatartsev, S. and Ortmeier, F. (2014). Improving the Sequence of Robotic Tasks with Freedom of Execution. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
  3. Aspragathos, N. (1998). Cartesian trajectory generation under bounded position deviation. Mechanism and machine theory, 33(6):697-709.
  4. Ata, A. A. (2007). Optimal trajectory planning of manipulators: A review. Journal of Engineering Science and Technology, 1:32.
  5. Ata, A. A. and Myo, T. R. (2005). Optimal point-to-point trajectory tracking of redundant manipulators using generalized pattern search. International Journal of Advanced Robotic Systems, 2(3).
  6. Berenson, D., Srinivasa, S., and Kuffner, J. (2011). Task space regions: A framework for pose-constrained manipulation planning. International Journal of Robotics Research (IJRR), 30(12):1435-1460.
  7. Biagiotti, L. and Melchiorri, C. (2008). Trajectory Planning for Automatic Machines and Robots. Springer Berlin Heidelberg.
  8. Chettibi, T., Lehtihet, H., Haddad, M., and Hanchi, S. (2004). Minimum cost trajectory planning for industrial robots. European Journal of Mechanics-A/Solids, 23(4):703-715.
  9. Craig, J. (2005). Introduction to Robotics: Mechanics and Control. Pearson.
  10. Dror, M., Efrat, A., Lubiw, A., and Mitchell, J. S. B. (2003). Touring a sequence of polygons. In 35th annual ACM symposium on Theory of Computing, pages 473-482. ACM Press.
  11. From, P. J., Gunnar, J., and Gravdahl, J. T. (2011). Optimal paint gun orientation in spray paint applications - experimental results. IEEE Transactions on Automation Science and Engineering, 8(2):438-442.
  12. Gasparetto, A. and Zanotto, V. (2010). Optimal trajectory planning for industrial robots. Advances in Engineering Software, 41:548-556.
  13. Hooke, R. and Jeeves, T. A. (1961). Direct search solution of numerical and statistical problems. Journal of the ACM, 8(2):212-229.
  14. Kolter, J. Z. and Ng, A. Y. (2009). Task-space trajectories via cubic spline optimization. In IEEE International Conference on Robotics and Automation (ICRA).
  15. Kovács, A. (2013). Task sequencing for remote laserwelding in the automotive industry. In 23rd International Conference on Automated Planning and Scheduling (ICAPS).
  16. Liu, H., Lai, X., and Wu, W. (2013). Time-optimal and jerkcontinuous trajectory planning for robot manipulators with kinematic constraints. Robotics and ComputerIntegrated Manufacturing, 29(2):309-317.
  17. Macfarlane, S. and Croft, E. A. (2003). Jerk-Bounded Manipulator Trajectory Planning: Design for Real-Time Applications. IEEE Transactions on Robotics and Automation, 19(1):42-52.
  18. Olabi, A., Béarée, R., Gibaru, O., and Damak, M. (2010). Feedrate planning for machining with industrial sixaxis robots. Control Engineering Practice, 18(5):471- 482.
  19. Pan, X., Li, F., and Klette, R. (2010). Approximate shortest path algorithms for sequences of pairwise disjoint simple polygons. In Canadian Conference on Computational Geometry, pages 175-178.
  20. Simon, D. (1993). The application of neural networks to optimal robot trajectory planning. Robotics and Autonomous Systems, 11:23-34.
Download


Paper Citation


in Harvard Style

Alatartsev S., Belov A., Nykolaychuk M. and Ortmeier F. (2014). Robot Trajectory Optimization for the Relaxed End-effector Path . In Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-039-0, pages 385-390. DOI: 10.5220/0005093103850390


in Bibtex Style

@conference{icinco14,
author={Sergey Alatartsev and Anton Belov and Mykhaylo Nykolaychuk and Frank Ortmeier},
title={Robot Trajectory Optimization for the Relaxed End-effector Path},
booktitle={Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2014},
pages={385-390},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005093103850390},
isbn={978-989-758-039-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Robot Trajectory Optimization for the Relaxed End-effector Path
SN - 978-989-758-039-0
AU - Alatartsev S.
AU - Belov A.
AU - Nykolaychuk M.
AU - Ortmeier F.
PY - 2014
SP - 385
EP - 390
DO - 10.5220/0005093103850390