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Tytuł artykułu

New Approach to Planning the Complex Movements of 6-DOF Industrial Robot Subjected to Acceleration Constraints

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A method of trajectory planning with regards to joint velocity and acceleration constraints for industrial 6 DOF manipulator is presented. The task of the robot is to move to specified location in the workspace passing through intermediate waypoints. The proposed algorithm can be used to plan the task of the robot by autonomous systems in smart factories eliminating human participation in the robot programing process. Opposite to similar approaches it does not assume the type of function describing the motion of the robot. The trajectories generated using the proposed approach are smooth and provide smooth velocities and continuous joint accelerations. The motion is planned in such a way to fulfill joint velocity and acceleration constraints. Fulfillment of velocity limitations is accomplished by perturbing the manipulator motion close to velocity limits. To satisfy acceleration constraints a trajectory scaling approach carried out in limited periods of time is used. The results of the research are illustrated by simulations and experiments, in which an analysis of the method of performing robot tasks carried out using built-in algorithms and presented methods are performed.
Twórcy
  • Institute of Mechanical Engineering, University of Zielona Góra, ul. Licealna 9, 65-417 Zielona Góra, Poland
Bibliografia
  • 1. Wang S.Y., Wan J.F., Li D., Zhang C.H. Implementing smart factory of industrie 4.0: An outlook. International Journal of Distributed Sensor Networks 2016; 12(1): 3159805, DOI: 10.1155/2016/3159805.
  • 2. Sajjad A., Ahmad W., Hussain S. Decision making process development for industry 4.0 transformation. Advances in Science and Technology Research Journal 2022; 16(3): 1–11, DOI: 10.12913/22998624/147237.
  • 3. Sobaszek Ł. A lean robotics approach to the scheduling of robotic adhesive dispensing process. Advances in Science and Technology Research Journal 2022; 16(5): 136–146, DOI: 10.12913/22998624/152332.
  • 4. Pizoń, J., Cioch, M., Kanski, L., García, E.S. Cobots implementation in the era of industry 5.0 using modern business and management solutions. Advances in Science and Technology Research Journal 2022, 16(6): 166–178, DOI: 10.12913/22998624/156222.
  • 5. Bochen A., Ambrożkiewicz B. The influence of light intensity on the operation of vision system in collaborative robot. Advances in Science and Technology Research Journal 2023, 17(4): 206–214, DOI: 10.12913/22998624/169884.
  • 6. Palmieri G., Scoccia C. Motion planning and control of redundant manipulators for dynamical obstacle avoidance. Machines 2021; 9(6): 121, DOI: 10.3390/machines9060121.
  • 7. Guruji A.K., Agarwal H., Parsediya D. Time-efcient A* algorithm for robot path planning. Procedia Technol. 2016; 23: 144–149, DOI: 10.1016/j.protcy.2016.03.010.
  • 8. Pajak I. Real-time trajectory generation methods for cooperating mobile manipulators subject to state and control constraints. Journal of Intelligent and Robotic Systems 2019; 93(3–4, SI): 649–668, DOI: 10.1007/s10846-018-0878-5.
  • 9. Chen X.Y., You X.J., Jiang J.C., Ye J.H., Wu H.B. Trajectory planning of dual-robot cooperative assembly. Machines 2022; 10(8): 689, DOI: 10.3390/machines10080689.
  • 10. Pardi T., Ortenzi V., Fairbairn C., Pipe T., Esfahani A.M.G., Stolkin R. Planning Maximum-Manipulability Cutting Paths. IEEE Robotics and Automation Letters 2020; 5(2): 1999–2006, DOI10.1109/LRA.2020.2970949.
  • 11. Chen N., Song F., Li G., Sun X., Ai C. An adaptive sliding mode backstepping control for the mobile manipulator with nonholonomic constraints. Communications in Nonlinear Science and Numerical Simulation 2013; 18(10): 2885–2899, DOI: 10.1016/j.cnsns.2013.02.002.
  • 12. Dexu B., Wei S., Hongshan Y., Cong W., Hui Z. Adaptive robust control based on rbf neural networks for duct cleaning robot. International Journal of Control Automation and Systems 2015; 13(2): 475–487, DOI: 10.1007/s12555-012-0447-9.
  • 13. Tamizi M.G., Yaghoubi M., Najjaran, H. A review of recent trend in motion planning of industrial robots. International Journal of Intelligent Robotics and Applications 2023; 7(2): 253–274, DOI: 10.1007/s41315-023-00274-2.
  • 14. Boscariol P., Gasparetto A., Vidoni R. Planning continuous-jerk trajectories for industrial manipulators. In: Proc of 11th ASME Biennial Conference on Engineering Systems Design and Analysis, (ESDA 2012), Nantes, France 2012, 127–136, DOI: 10.1115/ESDA2012-82103.
  • 15. Huang J.S., Hu P.F., Wu K.Y., Zeng M. Optimal time-jerk trajectory planning for industrial robots. Mechanism and Machine Theory 2018; 121: 530–544, DOI: 10.1016/j.mechmachtheory.2017.11.006.
  • 16. Lin J.J., Rickert M., Knoll A. Parameterizable and jerk-limited trajectories with blending for robot motion planning and spherical cartesian waypoints. In: Proc of IEEE International Conference on Robotics and Automation ICRA 2021, Xian, China 2021, 13982–13988, DOI: 10.1109/ICRA48506.2021.9561601.
  • 17. Scheiderer C., Thun T., Meisen T. Bezier curve based continuous and smooth motion planning for selflearning industrial robots. In: Proc of 29th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM) - Beyond Industry 4.0 - Industrial Advances, Engineering Education and Intelligent Manufacturing, Limerick, Ireland 2019, 423–430, DOI: 10.1016/j.promfg.2020.01.054.
  • 18. Meyes R., Scheiderer C., Meisen T. Continuous motion planning for industrial robots based on direct sensory input. In: Proc of 51st CIRP Conference on Manufacturing Systems, Stockholm, Sweden 2018, 291–296, DOI: 10.1016/j.procir.2018.03.067.
  • 19. He F.F., Huang Q.J. Time-optimal trajectory planning of 6-DOF manipulator based on fuzzy control. Actuators 2022; 11(11), DOI: 10.3390/act11110332.
  • 20. Gasparetto A., Boscariol P., Lanzutti A., Vidoni R. Path planning and trajectory planning algorithms: A general overview. Motion and Operation Planning of Robotic Systems: Background and Practical Approaches, Mechanisms and Machine Science, Springer 2015, DOI: 10.1007/978-3-319-14705-5_1.
  • 21. Pajak G., Pajak I. Point-to-point collision-free trajectory planning for mobile manipulators. Journal of Intelligent and Robotic Systems 2017; 85(3-4, SI): 523-538, DOI: 10.1007/s10846-016-0390-8.
  • 22. Pajak G., Pajak I. Planning of a point to point collision-free trajectory for mobile manipulators. In: Proc of 10th International Workshop on Robot Motion and Control (RoMoCo), Poznan, Poland 2015, 142–147.
  • 23. Pajak G. Trajectory planning for mobile manipulators subject to control constraints. In: Proc of 11th International Workshop on Robot Motion and Control (RoMoCo) 2017, 117–122.
  • 24. Pajak G., Pajak I. Collision-free trajectory planning for mobile manipulators subject to control constraints. Archive of Mechanical Engineering 2014; 61(1): 35–55, DOI: 10.2478/meceng-2014-0002.
  • 25. Pajak I., Pajak G. Motion planning for a mobile humanoid manipulator working in an industrial environment. Applied Sciences-Basel 2021; 11(13), DOI: 10.3390/app11136209.
  • 26. DH parameters for calculations of kinematics and dynamics. Available online: https://www.universal-robots.com/articles/ur/application-installation/dh-parameters-for-calculations-of-kinematics-and-dynamics.
  • 27. The Robot Toolbox for Matlab 2.0 (Pajak G., Pajak I). Available online: http://staff.uz.zgora.pl/gpajak/rtoolbox (accessed on 23 July 2023).
  • 28. Universal Robots e-Series User Manual. Available online: https://www.universal-robots.com/download/manuals-e-series/user/ur3e/512/user-manual-ur3e-e-series-sw-512-english-international-en/.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-5142040f-ee86-4e37-9538-fbba6cd14a25
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