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Tytuł artykułu

The Influence of the Duty Cycle Trajectory Length on the Energy Consumption of an Anthropomorphic Manipulator

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The work presents the use of one of the heuristic algorithms - the Particle Swarm Optimization method - to determine the optimal trajectory of end-effector of an anthropomorphic manipulator. The task was to calculate the shortest 3D path connecting three defined points avoiding fixed obstacles. An indirect method was used to search for trajectory with extra points generated randomly. The trajectory was defined as a spline. The influence of stop criterion, the number of auxiliary points between defined points on the action of the algorithm and the study results has been analyzed. For found out trajectory the electricity consumption required to execute a duty cycle of an anthropomorphic manipulator has been determined.
Rocznik
Strony
17--27
Opis fizyczny
Bibliogr. 14 poz., schem., tab., wykr.
Twórcy
autor
  • Politechnika Częstochowska, Instytut Mechaniki i Podstaw Konstrukcji Maszyn
autor
  • Politechnika Częstochowska, Instytut Mechaniki i Podstaw Konstrukcji Maszyn
Bibliografia
  • 1. Bai, Q. (2010). Analysis of particle swarm optimization algorithm. Computer and information science, 3(1):180.
  • 2. Cekus, D. and Skrobek, D. (2016). Trajectory optimization of a scara manipulator using particle swarm optimization. Machine Dynamics Research, 40(1).
  • 3. Cekus, D. and Skrobek, D. (2017). The influence of the length of trajectory of scara manipulator duty cycle on electricity consumption. Proceedings of 23rd International Conference Engineering Mechanics 2017,Svratka, Czech Republic, pages 238–241.
  • 4. Deb, K. (2001). Multi-objective optimization using evolutionary algorithms, volume 16. John Wiley & Sons.
  • 5. Dorigo, M. and Birattari, M. (2011). Ant colony optimization. In Encyclopedia of machine learning, pages 36–39. Springer.
  • 6. Gasparetto, A. and Zanotto, V. (2007). A new method for smooth trajectory planning of robot manipulators. Mechanism and machine theory, 42(4):455–471.
  • 7. Holland, J. H. (1975). Adaptation in natural and artificial systems. an introductory analysis with application to biology, control, and artificial intelligence. Ann Arbor, MI: University of Michigan Press, pages 439–444.
  • 8. Kennedy, J. (2011). Particle swarm optimization. In Encyclopedia of machine learning, pages 760–766. Springer.
  • 9. Kirkpatrick, S., Gelatt, C. D., and Vecchi, M. P. (1983). Optimization by simulated annealing. science, 220(4598):671–680.
  • 10. Kucuk, S. (2013). Energy minimization for 3-rrr fully planar parallel manipulator using particle swarm optimization. Mechanism and Machine Theory, 62:129–149.
  • 11. Posiadala, B., Tomala, M., Cekus, D., and Wary’s, P. (2015). Work cycle optimization problem of manipulator with revolute joints. International Journal of Dynamics and Control, 3(1):94–99.
  • 12. Rubio, F., Llopis-Albert, C., Valero, F., and Suñer, J. L. (2016). Industrial robot efficient trajectory generation without collision through the evolution of the optimal trajectory. Robotics and Autonomous Systems, 86:106–112.
  • 13.Saramago, S. and Steffen, V. (2001). Trajectory modeling of robot manipulators in the presence of obstacles. Journal of optimization theory and applications, 110(1):17–34.
  • 14. Ur-Rehman, R., Caro, S., Chablat, D., and Wenger, P. (2010). Multi-objective path placement optimization of parallel kinematics machines based on energy consumption, shaking forces and maximum actuator torques: Application to the orthoglide. Mechanism and Machine Theory, 45(8):1125–1141.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-4504fd71-5d6f-41d5-8b1b-657de0433c97
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