Hostname: page-component-78c5997874-v9fdk Total loading time: 0 Render date: 2024-11-10T11:52:23.242Z Has data issue: false hasContentIssue false
  • English
  • Français

Constrained model predictive control for mobile robotic manipulators

Published online by Cambridge University Press:  03 April 2017

Giovanni Buizza Avanzini ,
Andrea Maria Zanchettin  and
Paolo Rocco
Giovanni Buizza Avanzini*
Affiliation:
Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, Piazza L. Da Vinci 32, 20133, Milano, Italy. E-mails: andreamaria.zanchettin@polimi.it, paolo.rocco@polimi.it
Andrea Maria Zanchettin
Affiliation:
Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, Piazza L. Da Vinci 32, 20133, Milano, Italy. E-mails: andreamaria.zanchettin@polimi.it, paolo.rocco@polimi.it
Paolo Rocco
Affiliation:
Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, Piazza L. Da Vinci 32, 20133, Milano, Italy. E-mails: andreamaria.zanchettin@polimi.it, paolo.rocco@polimi.it
*
*Corresponding author. E-mail: giovannimassimo.buizza@polimi.it
Get access Rights & Permissions [Opens in a new window]

Summary

This paper discusses the application of a constraint-based model predictive control (MPC) to mobile manipulation tracking problems. The problem has been formulated so as to guarantee offset-free tracking of piecewise constant references, with convergence and recursive feasibility guarantees. Since MPC inputs are recomputed at every control iteration, it is possible to deal with dynamic and unknown scenarios. A number of motion constraints can also be easily included: Acceleration, velocity and position constraints have been enforced, together with collision avoidance constraints for the mobile base and the arm and field-of-view constraints. Such constraints have been extended over the prediction horizon maintaining a linear-quadratic formulation of the problem. Navigation performance has been improved by devising an online algorithm that includes an additional goal to the problem, derived from the classical vortex field approach. Experimental validation shows the applicability of the proposed approach.

Keywords

Control of robotic systemsMobile manipulatorsPredictive controlRedundant manipulatorsNavigationMotion planning
Type
Articles
Information
Robotica , Volume 36 , Issue 1 , January 2018 , pp. 19 - 38
Copyright
Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Decré, W., Bruyninckx, H. and De Schutter, J., “Extending the Itasc Constraint-Based Robot Task Specification Framework to Time-Independent Trajectories and User-Configurable Task Horizons,” Proceedings of the IEEE International Conference on Robotics and Automation, ICRA (May 2013) pp. 1941–1948.Google Scholar
2. de Lasa, M. and Hertzmann, A., “Prioritized Optimization for Task-Space Control,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS (Oct. 2009) pp. 5755–5762.Google Scholar
3. Zanchettin, A. M. and Rocco, P., “Near Time-Optimal and Sensor-Based Motion Planning for Robotic Manipulators,” Proceedings of the IEEE 52nd Annual Conference on Decision and Control, CDC (Dec. 2013) pp. 965–970.Google Scholar
4. Samson, C., Espiau, B. and Borgne, M. L., Robot Control: The Task Function Approach (Oxford University Press, 1991).Google Scholar
5. Del Prete, A., Romano, F., Natale, L., Metta, G., Sandini, G. and Nori, F., “Prioritized Optimal Control,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), May 2014, pp. 2540–2545.Google Scholar
6. Camacho, E. and Bordons, C., Model Predictive Control (Springer, 2004).Google Scholar
7. Mayne, D., Rawlings, J., Rao, C. and Scokaert, P., “Constrained model predictive control: Stability and optimality,” Automatica 36 (6), 789–814 (2000).Google Scholar
8. Vukov, M., Domahidi, A., Ferreau, H., Morari, M. and Diehl, M., “Auto-Generated Algorithms for Nonlinear Model Predictive Control on Long and on Short Horizons,” Proceedings of the IEEE 52nd Annual Conference on Decision and Control (CDC) (Dec. 2013) pp. 5113–5118.Google Scholar
9. Maniatopoulos, S., Panagou, D. and Kyriakopoulos, K., “Model Predictive Control for the Navigation of a Nonholonomic Vehicle with Field-of-View Constraints,” Proceedings of the American Control Conference (ACC) (Jun. 2013) pp. 3967–3972.Google Scholar
10. Nascimento, T. P., Moreira, A. P. and Conceição, A. G. S., “Multi-robot nonlinear model predictive formation control: Moving target and target absence,” Robot. Auton. Syst. 61 (12), 1502–1515 (2013).Google Scholar
11. Nascimento, T. P., Conceição, A. G. S. and Moreira, A. P., “Multi-robot nonlinear model predictive formation control: the obstacle avoidance problem,” Robotica 34, 549–567 (Mar. 2016).Google Scholar
12. Brooks, A., Kaupp, T. and Makarenko, A., “Randomised MPC-Based Motion-Planning for Mobile Robot Obstacle Avoidance,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (May 2009) pp. 3962–3967.Google Scholar
13. Sobrinho, J. C. L. Barreto, Conceição, A., Dorea, C., Martinez, L. and De Pieri, E., “Design and implementation of model-predictive control with friction compensation on an omnidirectional mobile robot,” IEEE/ASME Trans. Mechatronics 19 (2), 467–476 (Apr. 2014).Google Scholar
14. Fruchard, M., Morin, P. and Samson, C., “A framework for the control of nonholonomic mobile manipulators,” Int. J. Robot. Res. 25 (8), 745–780 (2006).Google Scholar
15. Padois, V., Fourquet, J.-Y. and Chiron, P., “Kinematic and dynamic model-based control of wheeled mobile manipulators: A unified framework for reactive approaches,” Robotica 25 (2), 157–173 (2007).CrossRefGoogle Scholar
16. Falco, P. and Natale, C., “Low-level flexible planning for mobile manipulators: A distributed perception approach,” Adv. Robot. 28 (21), 1431–1444 (2014).Google Scholar
17. Heshmati-alamdari, S., Karras, G. C., Eqtami, A. and Kyriakopoulos, K. J., “A Robust Self Triggered Image Based Visual Servoing Model Predictive Control Scheme for Small Autonomous Robots,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS (Sep. 2015) pp. 5492–5497.CrossRefGoogle Scholar
18. Ide, S., Takubo, T., Ohara, K., Mae, Y. and Arai, T., “Real-Time Trajectory Planning for Mobile Manipulator using Model Predictive Control with Constraints,” Proceedings of the 8th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI (Nov. 2011) pp. 244–249.CrossRefGoogle Scholar
19. Avanzini, G. Buizza, Zanchettin, A. M. and Rocco, P., “Reactive Constrained Model Predictive Control for Redundant Mobile Manipulators,” Proceedings of the 13th International Conference Intelligent Autonomous Systems, IAS-13 (Springer International Publishing 2016) pp. 1301–1314.Google Scholar
20. Avanzini, G. Buizza, Zanchettin, A. M. and Rocco, P., “Constraint-Based Model Predictive Control for Holonomic Mobile Manipulators,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS (2015).Google Scholar
21. Wang, L., “A tutorial on model predictive control: Using a linear velocity-form model,” Dev. Chem. Eng. Miner. Process. 12 (5–6), pp. 573–614 (2004).CrossRefGoogle Scholar
22. Limon, D., Alvarado, I., Alamo, T. and Camacho, E. F., “MPC for tracking piecewise constant references for constrained linear systems,” Automatica 44 (9), 2382–2387 (2008).Google Scholar
23. Betti, G., Farina, M. and Scattolini, R., “A robust MPC algorithm for offset-free tracking of constant reference signals,” IEEE Trans. Autom. Control 58 (9), 2394–2400 (2013).Google Scholar
24. Bischoff, R., Huggenberger, U. and Prassler, E., “KUKA Youbot - A Mobile Manipulator for Research and Education,” Proceedings of the IEEE International Conference on Robotics and Automation, ICRA (2011).CrossRefGoogle Scholar
25. Alvarado, I., Limon, D., Alamo, T. and Camacho, E., “Output Feedback Robust Tube Based MPC for Tracking of Piece-Wise Constant References,” Proceedings of the 46th IEEE Conference on Decision and Control (Dec. 2007) pp. 2175–2180.Google Scholar
26. Ferramosca, A., Limon, D., Alvarado, I., Alamo, T. and Camacho, E., “MPC for tracking with optimal closed-loop performance,” Automatica 45 (8), 1975–1978 (2009).CrossRefGoogle Scholar
27. Limon, D., Alvarado, I., Alamo, T. and Camacho, E., “Robust tube-based MPC for tracking of constrained linear systems with additive disturbances,” J. Process Control 20 (3), 248–260 (2010).Google Scholar
28. De Medio, C., Nicol, F. and Oriolo, G., “Robot Motion Planning Using Vortex Fields,” New Trends in Systems Theory, Progress in Systems and Control Theory, vol. 7 (Birkhuser, Boston, 1991) pp. 237–244.CrossRefGoogle Scholar
29. Lacevic, B. and Rocco, P., “Kinetostatic Danger Field - A Novel Safety Assessment for Human-Robot Interaction,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS (2010).Google Scholar
30. Zanchettin, A. M. and Rocco, P., “A general user-oriented framework for holonomic redundancy resolution in robotic manipulators using task augmentation,” IEEE Trans. Robot. 28 (2), 514–521 (2012).Google Scholar
31. Ceriani, N. M., Zanchettin, A. M., Rocco, P., Stolt, A. and Robertsson, A., “Reactive task adaptation based on hierarchical constraints classification for safe industrial robots,” IEEE/ASME Trans. Mechatron. 20 (6), 2935–2949 (2015).Google Scholar
32. Sciavicco, L. and Siciliano, B., “A solution algorithm to the inverse kinematic problem for redundant manipulators,” IEEE J. Robot. Autom. 4 (4), 403–410 (1988).Google Scholar
33. Zanchettin, A. M., Ceriani, N. M., Rocco, P., Ding, H. and Matthias, B., “Safety in human-robot collaborative manufacturing environments: Metrics and control,” IEEE Trans. Autom. Sci. Eng. 13 (2), 882–893 (2016).Google Scholar
34. Ragaglia, M., Zanchettin, A. M., and Rocco, P., “Safety-Aware Trajectory Scaling for Human-Robot Collaboration with Prediction of Human Occupancy,” Proceedings of the International Conference on Advanced Robotics, ICAR (2015).Google Scholar
35. Open cv. [Online]. Available: http://opencv.org/ Google Scholar
36. Open ni. [Online]. Available: http://github.com/OpenNI/OpenNI Google Scholar
37. Point cloud library pcl. [Online]. Available: http://pointclouds.org/ Google Scholar
38. Ferreau, H., Bock, H. and Diehl, M., “An online active set strategy to overcome the limitations of explicit MPC,” Int. J. Robust Nonlinear Control 18 (8), 816–830 (2008).Google Scholar
39. Sharma, S., Kraetzschmar, G. K., Scheurer, C. and Bischoff, R., “Unified Closed Form Inverse Kinematics for the KUKA Youbot,” Proceedings of the ROBOTIK/German Conference on Robotics (2012).Google Scholar
40. Vahrenkamp, N., Asfour, T., Metta, G., Sandini, G. and Dillmann, R., “Manipulability Nnalysis,” Proceedings of the 2012 12th IEEE-RAS International Conference on Humanoid Robots, Humanoids (Nov. 2012) pp. 568–573.Google Scholar
41. Nocedal, J. and Wright, S. J., Numerical Optimization, Springer Series in Operations Research and Financial Engineering (Springer, 2006).Google Scholar

Buizza Avanzini supplementary material

Supplementary Video

Download Buizza Avanzini supplementary material(Video)
Video 55.4 MB