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Optimal Two-Step Collision-Free Trajectory Planning for Cylindrical Robot using Particle Swarm Optimization

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Abstract

Among the works related to the planning of trajectories of manipulator robots, a subject that has been approached by different authors is the study associated to the manipulator’s movement, without considering the variable "time" involved in these tasks. These studies involve the robot's kinematic constraints (path geometry, obstacles and the characteristics of the effector). This paper proposes an optimization technique for planning the trajectory of a cylindrical manipulator robot with 5 degrees of freedom. The technique considers two crucial factors: obstacle deviation and the kinematic characteristics of the manipulator. In the first step, an algorithm is employed to generate intermediate points along the trajectory. This algorithm optimizes the intermediate points to minimize the distance between them and the final destination. In the second step, the proposed method utilizes b-spline functions of the 5th degree to generate smooth and efficient trajectories with minimal joint movement. Joint space restrictions are proposed to ensure that the trajectory obtained does not cause collisions and it is applied within the operational limits of the robot. The proposed steps have been implemented using the Particle Swarm Optimization (PSO). The results of the study indicate that the proposed method is highly suitable for cylindrical robots operating in the presence of obstacles. The computational analyses conducted during the study clearly demonstrate that both the intermediate points and the trajectory within the joint space of the robot under investigation remained well within the designated collision-free zone.

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The authors declare that the information presented in this article is intended for preferential use by the Journal of Intelligent and Robotic Systems - JINT.

References

  1. Das, P. K., Behera, H. S., Jena P. K., Panigrahi y B. K.: An intelligent multi-robot path planning in a dynamic environment using improved gravitational search algorithm, Int. J. Autom. Comput. (2016). https://doi.org/10.1007/s11633-016-1019-x

  2. Panda, M., Das, B., Subudhi, B., Pati, y B. B.: A Comprehensive Review of Path Planning Algorithms for Autonomous Underwater Vehicles, Int. J. Autom. Comput., ene. (2020). https://doi.org/10.1007/s11633-019-1204-9

  3. Yang, M., Jiang, Y., Sun, y J.: Research on Trajectory Planning of Manipulator Based on GA - APF Algorithm, en 2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), 210–215 (2017). https://doi.org/10.1109/CYBER.2017.8446214

  4. Çakır, M., Butun, E., Kayman, Y.: Effects of genetic algorithm parameters on trajectory planning for 6-DOF industrial robots. Ind. Robot Int. J. 33(3), 205–215 (2006). https://doi.org/10.1108/01439910610659114

    Article  Google Scholar 

  5. Chong, J.W.S., Ong, S.K., Nee, A.Y.C., Youcef-Youmi, K.: Robot programming using augmented reality: An interactive method for planning collision-free paths. Robot. Comput.-Integr. Manuf. 25(3), 689–701 (2009). https://doi.org/10.1016/j.rcim.2008.05.002

    Article  Google Scholar 

  6. Lei-ping, X., Zi-li, C., Shao-jie, y S.: Obstacle Avoiding Research on the Manipulator Based on Genetic Algorithm, ResearchGate. (2011). https://doi.org/10.1109/IMCCC.2011.218

  7. dos Santos, R.R., Steffen, V., Saramago, S.. de F.. P..: Robot path planning in a constrained workspace by using optimal control techniques. Multibody Syst. Dyn. 19(1), 159–177 (2008). https://doi.org/10.1007/s11044-007-9059-1

    Article  MATH  Google Scholar 

  8. Ata, A., Myo, T.: Collision-free Trajectory Planning for Manipulators Using Generalized Pattern Search. Int. J. Simul. Model. 5(4), 145–154 (2006). https://doi.org/10.2507/IJSIMM05(4)2.07

    Article  Google Scholar 

  9. Sengupta, A., Chakraborti, T., Konar A., Nagar, A.: Energy efficient trajectory planning by a robot arm using invasive weed optimization technique, en 2011 Third World Congress on Nature and Biologically Inspired Computing. 311–316 (2011). https://doi.org/10.1109/NaBIC.2011.6089465

  10. Abardeh, M.E., Akbarzadeh, A.: Online Trajectory Generation of a 2 Link Robot in Presence of Obstacle. Adv. Mater. Res. 488–489, 1772–1776 (2012). https://doi.org/10.4028/www.scientific.net/AMR.488-489.1772

    Article  Google Scholar 

  11. Severin, S., Rossmann, J.: A Comparison of Different Metaheuristic Algorithms for Optimizing Blended PTP Movements for Industrial Robots, en Intelligent Robotics and Applications, Springer Berlin Heidelberg. 321–330 (2012). https://doi.org/10.1007/978-3-642-33503-7_32

  12. Machmudah, A., Parman, S., Zainuddin, A., Chacko, S.: Polynomial joint angle arm robot motion planning in complex geometrical obstacles. Appl. Soft Comput. 13(2), 1099–1109 (2013). https://doi.org/10.1016/j.asoc.2012.09.025

    Article  Google Scholar 

  13. Abu-Dakka, F.J., Rubio, F., Valero, F., Mata, V.: Evolutionary indirect approach to solving trajectory planning problem for industrial robots operating in workspaces with obstacles. Eur. J. Mech. - ASolids 42, 210–218 (2013). https://doi.org/10.1016/j.euromechsol.2013.05.007

    Article  Google Scholar 

  14. Ardiyanto, I., Miura, J.: Time-space Viewpoint Planning for Guard Robot with Chance Constraint. Int. J. Autom. Comput. 16(4), 475–490 (2019). https://doi.org/10.1007/s11633-018-1146-7

    Article  Google Scholar 

  15. Piazzi, A., Visioli, A.: Global minimum-jerk trajectory planning of robot manipulators. IEEE Trans. Ind. Electron. 47(1), 140–149 (2000). https://doi.org/10.1109/41.824136

    Article  Google Scholar 

  16. Gasparetto, A., Zanotto, V.: A new method for smooth trajectory planning of robot manipulators. Mech. Mach. Theory 42(4), 455–471 (2007). https://doi.org/10.1016/j.mechmachtheory.2006.04.002

    Article  MathSciNet  MATH  Google Scholar 

  17. Gasparetto, A., Zanotto, V.: A technique for time-jerk optimal planning of robot trajectories. Robot. Comput.-Integr. Manuf. 24(3), 415–426 (2008). https://doi.org/10.1016/j.rcim.2007.04.001

    Article  Google Scholar 

  18. Tangpattanakul, P., Artrit, P.: Minimum-time trajectory of robot manipulator using Harmony Search algorithm, en 6th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 2009. ECTI-CON 2009, 354–357 (2009). https://doi.org/10.1109/ECTICON.2009.5137025

  19. Meligy, R. E., Bassiuny, A. M., Bakr, E. M., Tantawy, A. A.: A feasible minimum-time trajectory of robot manipulator, en 2013 9th International Symposium on Mechatronics and its Applications (ISMA), 1–5 (2013). https://doi.org/10.1109/ISMA.2013.6547390

  20. Sarmanho, C. A.: Desenvolvimento de um robô pneumático de 5 graus de liberdade com controlador não linear com compensação de atrito, Phd Thesis, Universidade Federal do Rio Grande do Sul, Brazil, 2014. [En línea]. Disponible en: UFRGS. Phd Thesis, Porto Alegre, RS, Brazil

  21. Missiaggia, L.: Planejamento otimizado de trajetória para um robô cilíndrico acionado pneumaticamente, MasterThesis, Universidade Federal do Rio Grande do Sul, Porto Alegre, 2014. [En línea]. Disponible en: UFRGS. Master's Thesis, Porto Alegre, RS, Brazil

  22. Izquierdo, R. C.: Planejamento de trajetórias livres de colisão: um estudo considerando restrições cinemáticas e dinâmicas de um manipulador pneumático por meio de algoritmos metaheurísticos, UFRGS, 2017. [En línea]. Disponible en: UFRGS. Phd Thesis, Porto Alegre, RS, Brazil

  23. Piegl, L., Tiller, W.: The NURBS Book. Springer Science & Business Media, 2012. [En línea]. Disponible en: ISBN 978–3–642–97385–7

  24. Gasparetto, A., Boscariol, P., Lanzutti, A., Vidoni, R.: Trajectory Planning in Robotics. Math. Comput. Sci. 6(3), 269–279 (2012). https://doi.org/10.1007/s11786-012-0123-8

    Article  MathSciNet  Google Scholar 

  25. Baba, N., Kubota, N.: Collision avoidance planning of a robot manipulator by using genetic algorithm. A consideration for the problem in which moving obstacles and/or several robots are included in the workspace, en Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence. 2, 714–719 (1994). https://doi.org/10.1109/ICEC.1994.349970

  26. Kennedy, J., Eberhart, R.: Particle swarm optimization, presentado en , IEEE International Conference on Neural Networks, 1995. Proceedings, 4, 1942–1948 (1995). https://doi.org/10.1109/ICNN.1995.488968

  27. Li, L.J., Huang, Z.B., Liu, F., Wu, Q.H.: A heuristic particle swarm optimizer for optimization of pin connected structures. Comput. Struct. 85(7), 340–349 (2007). https://doi.org/10.1016/j.compstruc.2006.11.020

    Article  Google Scholar 

  28. Vanden Bergh, F., Engelbrecht, A.P.: A study of particle swarm optimization particle trajectories. Inf. Sci. 176(8), 937–971 (2006). https://doi.org/10.1016/j.ins.2005.02.003

    Article  MathSciNet  MATH  Google Scholar 

  29. Simon, D.: Data smoothing and interpolation using eighth-order algebraic splines. IEEE Trans. Signal Process. 52(4), 1136–1144 (2004). https://doi.org/10.1109/TSP.2004.823489

    Article  MathSciNet  MATH  Google Scholar 

  30. ShyhChyan, G., Ponnambalam, S.G.: Obstacle avoidance control of redundant robots using variants of particle swarm optimization. Robot. Comput.-Integr. Manuf. 28(2), 147–153 (2012). https://doi.org/10.1016/j.rcim.2011.08.001

    Article  Google Scholar 

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Correspondence to Anselmo Rafael Cukla.

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Izquierdo, R.C., Cukla, A.R., Lorini, F.J. et al. Optimal Two-Step Collision-Free Trajectory Planning for Cylindrical Robot using Particle Swarm Optimization. J Intell Robot Syst 108, 56 (2023). https://doi.org/10.1007/s10846-023-01903-5

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