Abstract
In this work, we deal with a class of problems of trajectory planning taking into account the smoothness of the trajectory. We assume that we have a set of positions in which the robot must pass. These positions are not assigned in the time axis. In this work, we propose a formulation of this problem, where the total length of the trajectory and the total time to move from the initial to the final position are minimized simultaneously. In order to ensure effective results and avoid abrupt movement, we should ensure the smoothness of the trajectory not only at the position level but also at the velocity and the acceleration levels. Thus, the position function must be at least two times differentiable. In our case, we use a polynomial function. We formulate this problem as a constraint optimization problem. To resolve it, we adapt the usual particle swarm algorithm to our problem and we show its efficiency by simulation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Da Graa Marcos, M., Tenreiro Machado, J.A., Azevedo-Perdicoulis, T.-P.: Multi-objective approach for the motion planning of redundant manipulators. Applied Soft Computing 12, 589–599 (2012)
Hauser, K., Ng-Thow-Hing, V.: Fast Smoothing of Manipulator Trajectories using Optimal Bounded-Acceleration Shortcuts. In: IEEE Intl. Conf. of Robotics and Automation (ICRA), Anchorage, USA (May 2010)
Solteiro Pires, E.J., de Moura Oliveira, P.B., Tenreiro Machado, J.A.: Manipulator trajectory planning using a MOEA. Applied Soft Computing 7, 659–667 (2007)
Kuffner, J.J., LaValle, S.M.: RRT-COnnect: an efficient approach to single-query path planning, In: Proc. 2000 IEEE Int’I Conf. on Robotics an Automation, ICRA (2000)
Clark, C.M.: Probabilistic road map sampling strategies for multi-robot motion planning. Robotics and Autonomous Systems 53, 244–264 (2005)
Liu, H., Lai, X., Wu, W.: Time-optimal and jerk-continuous trajectory planning for robot manipulators with kinematic constraints. Robotics Computer-Integrated Manufacturing 29, 309–317 (2013)
Daachi, B., Madani, T., Benallegue, A.: Adaptive neural controller for redundant robot manipulators and collision avoidance with mobile obstacles. Neurocomputing 79, 50–60 (2012)
Le Boudec, B., Saad, M., Nerguizian, V.: Modeling and adaptive control of redundant robots. Mathematics and Computers in Simulation 71, 395–403 (2006)
Gasparetto, A., Zanotto, V.: Optimal trajectory planning for industrial robots. Advances in Engineering Software 41, 548–556 (2010)
Shukla, A., Singla, E., Wahi, P., Dasgupta, B.: A direct variational method for planning monotonically optimal paths for redundant manipulators in constrained workspaces. Robotics and Autonomous Systems 61, 209–220 (2013)
Gasparetto, A., Zanotto, V.: A new method for smooth trajectory planning of robot manipulators. Mechanism and Machine Theory 42, 455–471 (2007)
S. Benyue and Z. Liping, Manipulator Trajectory Planning Based on the Algebraic-Trigonometric Hermite Blended Interpolation Spline, 2012 International Workshop on Information and Electronics Engineering, 29 (2012) 2093–2097
Xian, C.: Cubic trigonometric Bézier spline interpolation. Journal of Jiamusi University 27(3), 445–448 (2009)
Da Graa Marcos, M., Tenreiro Machado, J.A., Azevedo-Perdicoulis, T.-P.: Trajectory planning of redundant manipulators using genetic algorithms, Common Nonlinear Sci Numer Simulat, 14 (2009) 2858–2869
Tian, L., Collins, C.: An effective robot trajectory planning method using a genetic algorithm. Mechatronics 14, 455–470 (2004)
Menasri, R., Nakib, A., Oulhadj, H., Daachi, B., Siarry, P., Hains, G.: Path planning for redundant manipulators using metaheuristic for bilevel optimization and maximum of manipulability. In: IEEE International Conference on Robotics and Biomimetics (ROBIO) Shenzhen, China (December 2013)
Talbi, E.G.: Metaheuristic. John Wiley & Sons Inc, From Design to Implementation (2009)
Trelea, I.: Cristian. The particle swarm optimization algorithm: convergence analysis and parameter selection, Information processing letters 85(6), 317–325 (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Menasri, R., Oulhadj, H., Daachi, B., Nakib, A., Siarry, P. (2014). Smooth Trajectory Planning for Robot Using Particle Swarm Optimization. In: Siarry, P., Idoumghar, L., Lepagnot, J. (eds) Swarm Intelligence Based Optimization. ICSIBO 2014. Lecture Notes in Computer Science(), vol 8472. Springer, Cham. https://doi.org/10.1007/978-3-319-12970-9_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-12970-9_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12969-3
Online ISBN: 978-3-319-12970-9
eBook Packages: Computer ScienceComputer Science (R0)