Hostname: page-component-586b7cd67f-r5fsc Total loading time: 0 Render date: 2024-12-04T19:53:59.612Z Has data issue: false hasContentIssue false
  • English
  • Français

A direct approach to solving trajectory planning problems using genetic algorithms with dynamics considerations in complex environments

Published online by Cambridge University Press:  10 March 2014

Fares J. Abu-Dakka ,
Francisco J. Valero ,
Jose Luis Suñer  and
Vicente Mata
Fares J. Abu-Dakka*
Affiliation:
Centro de Investigación en Tecnología de Vehículos, Universitat Politècnica de València, Valencia 46022, Spain
Francisco J. Valero
Affiliation:
Centro de Investigación en Tecnología de Vehículos, Universitat Politècnica de València, Valencia 46022, Spain
Jose Luis Suñer
Affiliation:
Centro de Investigación en Tecnología de Vehículos, Universitat Politècnica de València, Valencia 46022, Spain
Vicente Mata
Affiliation:
Centro de Investigación en Tecnología de Vehículos, Universitat Politècnica de València, Valencia 46022, Spain
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

This paper presents a new genetic algorithm methodology to solve the trajectory planning problem. This methodology can obtain smooth trajectories for industrial robots in complex environments using a direct method. The algorithm simultaneously creates a collision-free trajectory between initial and final configurations as the robot moves. The presented method deals with the uncertainties associated with the unknown kinematic properties of intermediate via points since they are generated as the algorithm evolves looking for the solution. Additionally, the objective of this algorithm is to minimize the trajectory time, which guides the robot motion. The method has been applied successfully to the PUMA 560 robotic system. Four operational parameters (execution time, computational time, end-effector distance traveled, and significant points distance traveled) have been computed to study and analyze the algorithm efficiency. The experimental results show that the proposed optimization algorithm for the trajectory planning problem of an industrial robot is feasible.

Keywords

RoboticsTrajectory planningObstacles avoidanceGenetic algorithms
Type
Articles
Information
Robotica , Volume 33 , Issue 3 , March 2015 , pp. 669 - 683
Copyright
Copyright © Cambridge University Press 2014 

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.Abu-Dakka, F. J., Valero, F. and Mata, V., “Evolutionary path planning algorithm for industrial robots,” Adv. Robot. 26, 1369–1392 (2012).CrossRefGoogle Scholar
2.Abu-Dakka, F. J., Rubio, F., Valero, F. and Mata, V., “Evolutionary indirect approach to solving trajectory planning problem for industrial robots operating in workspaces with obstacles,” Eur. J. Mech. - A/Solids 42, 210–218 (2013).CrossRefGoogle Scholar
3.Saramago, S. F. P. and Steffen, V. Jr., “Trajectory modeling of robot manipulators in the presence of obstacles,” J. Optim. Theory Appl. 110, 17–34 (2001).CrossRefGoogle Scholar
4.Valero, F., Mata, V., Cuadrado, J. I. and Ceccarelli, M., “A formulation for path planning of manipulators in complex environments by using adjacent configurations,” Adv. Robot. 11, 33–56 (1996).CrossRefGoogle Scholar
5.Plessis, L. J. d. and Snyman, J. A., “Trajectory-planning through interpolation by overlapping cubic arcs and cubic splines,” Int. J. Numer. Methods Eng. 57, 1615–1641 (2003).CrossRefGoogle Scholar
6.Piazzi, A. and Visioli, A., “A Global Optimization Approach to Trajectory Planning for Industrial Robots,” IEEE/RSJ International Conference on Intelligent Robots and Systems IROS '97, Grenoble (1997) pp. 1553–1559.Google Scholar
7.Piazzi, A. and Visioli, A., “Global minimum-jerk trajectory planning of robot manipulators,” IEEE Trans. Ind. Electron. 47, 140–149 (2000).CrossRefGoogle Scholar
8.Bertolazzi, E., Biral, F. and Lio, M. D., “Real-time motion planning for multibody systems,” Multibody Syst. Dyn. 17, 119–139 (2007).CrossRefGoogle Scholar
9.Behzadipour, S. and Khajepour, A., “Time-optimal trajectory planning in cable-based manipulators,” IEEE Trans. Robot. 22, 559–563 (2006).CrossRefGoogle Scholar
10.Chettibi, T., Lehtihet, H. E., Haddad, M. and Hanchi, S., “Minimum cost trajectory planning for industrial robots,” Eur. J. Mech. 23, 703–715 (2004).CrossRefGoogle Scholar
11.Abdel-malek, K., Mi, Z., Yang, J. and Nebel, K., “Optimization-based trajectory planning of the human upper body,” Robotica 24, 683–696 (2006).CrossRefGoogle Scholar
12.Constantinescu, D. and Croft, E. A., “Smooth and time-optimal trajectory planning for industrial manipulators along specified paths,” J. Robot. Syst. 17, 233–249 (2000).3.0.CO;2-Y>CrossRefGoogle Scholar
13.Abu-Dakka, F. J., Trajectory Planning for Industrial Robot Using Genetic Algorithms Ph.D., Dpto de Ingeniería Mecánica y de Materiales (Valencia, Spain: Universitat Politècnica de València, 2011).Google Scholar
14.Holland, J. H., Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence, 2nd ed. (1st ed., 1975) (MIT Press, Cambridge, MA, USA, 1975/1992).Google Scholar
15.Davidor, Y., Genetic Algorithms and Robotics: A Heuristic Strategy for Optimization (World Scientific, Singapore, 1991).CrossRefGoogle Scholar
16.Toogood, R., Hao, H. and Wong, C., “Robot Path Planning Using Genetic Algorithms,” IEEE International Conference on Intelligent Systems for the 21st Century, Vancouver, BC (1995) pp. 489–494.Google Scholar
17.Yun, W.-M. and Xi, Y.-G., “Optimum motion planning in joint space for robots using genetic algorithms,” Robot. Auton. Syst. 18, 373–393 (1996).CrossRefGoogle Scholar
18.Rana, A. S. and Zalzala, A. M. S., “An Evolutionary Planner for Near Time-Optimal Collision-Free Motion of Multi-Arm Robotic Manipulators,” International Conference on Control '96, UKACC, University of Exeter, UK (1996) pp. 29–35.CrossRefGoogle Scholar
19.Monteiro, D. C. and Madrid, M. K., “Planning of Robot Trajectories with Genetic Algorithms,” The First Workshop on Robot Motion and Control, RoMoCo '99, Kiekrz, Poland (1999) pp. 223–228.Google Scholar
20.Pires, E. J. S., Machado, J. A. T. and Oliveira, P. B. d. M., “An Evolutionary Approach to Robot Structure and Trajectory,” Proceedings of the 10th International Conference on Advanced Robotics (ICAR'01), Budapest, Hungary (2001).Google Scholar
21.Tian, L. and Collins, C., “Motion planning for redundant manipulators using a floating point genetic algorithm,” J. Intell. Robot. Syst. 38, 297–312 (2003).CrossRefGoogle Scholar
22.Tian, L. and Collins, C., “An effective robot trajectory planning method using a genetic algorithm,” Mechatronics 14, 455–470 (2004).CrossRefGoogle Scholar
23.Pires, E. J. S., Oliveira, P. B. d. M. and Machado, J. A. T., “Manipulator trajectory planning using a MOEA,” Appl. Soft Comput. 7, 659–667 (2007).CrossRefGoogle Scholar
24.Saravanan, R. and Ramabalan, S., “Evolutionary minimum cost trajectory planning for industrial robots,” J. Intell. Robot. Syst. 52, 45–77 (2008).CrossRefGoogle Scholar
25.Saravanan, R., Ramabalan, S. and Balamurugan, C., “Evolutionary multi-criteria trajectory modeling of industrial robots in the presence of obstacles,” Eng. Appl. Artif. Intell. 22, 329–342 (2009).CrossRefGoogle Scholar
26.Saravanan, R., Ramabalan, S., Balamurugan, C. and Subash, A., “Evolutionary trajectory planning for an industrial robot,” Int. J. Autom. Comput. 7, 190–198 (2010).CrossRefGoogle Scholar
27.Abu-Dakka, F. J., Assad, I. F., Valero, F. and Mata, V., “Parallel-Populations Genetic Algorithm for the Optimization of Cubic Polynomial Joint Trajectories for Industrial Robots,” Intelligent Robotics and Applications, Lecture Notes in Computer Science, Vol. 7101. (Springer Berlin Heidelberg, Berlin, Germany, 2011) pp. 83–92.CrossRefGoogle Scholar
28.Macfarlane, S. and Croft, E. A., “Jerk-bounded manipulator trajectory planning: Design for real-time applications,” IEEE Trans. Ind. Electron. Robot. Autom. 19, 42–52 (2003).CrossRefGoogle Scholar
29.Valero, F., Mata, V. and Besa, A., “Trajectory planning in workspaces with obstacles taking into account the dynamic robot behaviour,” Mech. Mach. Theory 41, 525–536 (2006).CrossRefGoogle Scholar
30.Gorges-Schleuter, M., “ASPARAGOS an Asynchronous Parallel Genetic Optimization Strategy,” The Third International Conference on Genetic Algorithms, George Mason University, USA (1989) pp. 422–427.Google Scholar
31.Rojas, R., Neutral Networks: A Systematic Introduction (Springer-Verlag New York Incorporated, New York, 1996).CrossRefGoogle Scholar
32.Abu-Dakka, F. J., Valero, F. and Mata, V., “Obtaining Adjacent Configurations with Minimum Time Considering Robot Dynamics Using Genetic Algorithm,” The 17th International Workshop on Robotics in Alpe-Adria-Danube Region RAAD2008, Ancona, Italy (2008).Google Scholar
33.Abu-Dakka, F. J., Valero, F., Tubaileh, A. and Rubio, F., “Obtaining Adjacent Configurations with Minimum Time Considering Robot Dynamics,” The 12th World Congress in Mechanism and Machine Science, IFToMM, Besançon, France (2007).Google Scholar
34.Lozano-Pérez, T. and Wesley, M. A., “An algorithm for planning collision-free paths among polyhedral obstacles,” Mag. Commun. ACM 22, 560–570 (1979).CrossRefGoogle Scholar
35.Craig, J. J., Introduction to Robotics Mechanics and Control, 2nd ed. (Addison-Wesley Publishing Company, Reading, MA, USA, 2005).Google Scholar
36.Wall, M., “GAlib, A C++ Library of Genetic Algorithm Components,” available at: http://lancet.mit.edu/ga (1996).Google Scholar