Industrial Robot Motion Planning Algorithms Performance Benchmarking
Year 2021,
Volume: 2 Issue: 2, 31 - 45, 21.12.2021
Uğur Yayan
,
Alim Erdoğmuş
Abstract
In robotics studies, motion and trajectory planning for industrial robot systems is one of the most actively studied topics. "Automated Robot Inspection Cell for Quality Control of Automotive Body-in-White (ROKOS)" has been defined as the Usage Scenario within the scope of the VALU3S project, where studies are carried out for the verification and validation (V&V) of autonomous systems. In this study, ROKOS system, developed in the real environment, was transferred to the GAZEBO simulation environment for the verification and validation of the defined usage scenario, and SRVT system (Simulation Based Robot Verification Testing Tool) was revealed and simulation-based tests of the evaluation scenarios created within the scope of the project were carried out on this system. In this study, within the scope of "Safety Trajectory Optimization", which is one of the evaluation scenarios, it is aimed to verify and validate the software developed by creating trajectories in which the robot arms of the ROKOS system will move safely according to the determined evaluation criteria for the quality control of the bus chassis transferred to the simulation environment. In this context, the OMPL and EST trajectory planning algorithms of the ROS MoveIt tool were studied, and the pros and cons of these algorithms were determined. In order to verify these algorithms in the simulation environment, detailed tests were carried out on the usage scenario and the results were analyzed. Tests were conducted for 3 different scenarios (Speed Test, Full Test with Reset (FTR) and Full Test without Reset (FToR)) and validation activities were improved in terms of time and cost for the existing ROKOS system transferred to SRVT. Figure A shows the results of the Full Test with Reset.
Supporting Institution
ECSEL Joint Undertaking (JU)
Thanks
The research leading to this paper has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No 876852. The JU receives support from the European Union's Horizon 2020 research and innovation programme and Austria, Czech Republic, Germany, Ireland, Italy, Portugal, Spain, Sweden, Turkey. The views expressed in this document are the sole responsibility of the authors and do not necessarily reect the views or position of the European Commission.
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20 Hao, W. G., Leck, Y. Y., & Hun, L. C. (2011, May). 6-DOF PC-Based Robotic Arm (PC-ROBOARM) with efficient trajectory planning and speed control. In 2011 4th International Conference on Mechatronics (ICOM) (pp. 1-7). IEEE.
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22 Iqbal, J., Islam, R. U., & Khan, H. (2012). Modeling and analysis of a 6 DOF robotic arm manipulator. Canadian Journal on Electrical and Electronics Engineering, 3(6), 300-306.
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33 Sniedovich, M. (2006). Dijkstra's algorithm revisited: the dynamic programming connexion. Control and cybernetics, 35(3), 599-620.
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Endüstriyel Robot Hareket Planlama Algoritmaları Performans Karşılaştırması
Year 2021,
Volume: 2 Issue: 2, 31 - 45, 21.12.2021
Uğur Yayan
,
Alim Erdoğmuş
Abstract
Robotik çalışmalarında, endüstriyel robot sistemleri için hareket ve yörünge planlama, aktif olarak çalışılan konuların başında gelmektedir. Otonom sistemlerin doğrulanması ve onaylanması (V&V) için çalışmalar yürütülen VALU3S projesi kapsamında Kullanım Senaryosu olarak "Araç Şase Kalite Kontrolü için Otonom Robot Denetleme Hücresi (ROKOS)" tanımlanmıştır. Bu çalışmada da, tanımlanmış kullanım senaryosunun doğrulanması ve onaylanması amacıyla gerçek ortamda geliştirilmiş ROKOS sistemi, GAZEBO simülasyon ortamına aktarılarak SRVT (Simulasyon Tabanlı Robot Doğrulama Test Sistemi) ortaya çıkarılmış ve bu sistem üzerinde ise proje kapsamında oluşturulan değerlendirme senaryolarının simülasyon tabanlı testleri yapılmıştır. Bu çalışmada, değerlendirme senaryolarından “Emniyetli Yörünge Optimizasyon” kapsamında, simülasyon ortamına aktarılmış olan otobüs şasesinin kalite kontrolü için ROKOS sistemine ait robot kollarının belirlenen değerlendirme kriterine göre emniyetli bir şekilde hareket edecekleri yörüngelerin oluşturularak geliştirilen yazılımların doğrulama ve onaylama işlemlerinin yapılması hedeflenmiştir. Bu kapsamda, ROS MoveIt aracına ait OMPL ve EST yörünge planlama algoritmaları üzerinde çalışılmış, bu algoritmaların artı ve eksi yönleri belirlenmiştir. Bu algoritmaların simülasyon ortamında doğrulanmaları için kullanım senaryosu üzerinde detaylı testler yapılmış ve sonuçları analiz edilmiştir. Testler 3 farklı senaryo (Hızlı test, Resetli Tam Test ve Resetsiz Tam Test) için yapılmış ve SRVT’ye aktarılmış mevcut ROKOS sistemi için doğrulama faaliyetleri zaman ve maliyet açısından iyileştirilmiştir.
References
- Referans1
1 Ragel, R., Maza, I., Caballero, F., & Ollero, A. (2015, November). Comparison of motion planning techniques for a multi-rotor UAS equipped with a multi-joint manipulator arm. In 2015 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS) (pp. 133-141). IEEE.
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2 Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., ... & Ng, A. Y. (2009, May). ROS: an open-source Robot Operating System. In ICRA workshop on open source software (Vol. 3, No. 3.2, p. 5).
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3 Gazebo website. [Online]. Available: http://gazebosim.org/, (2021)
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4 Chitta, S., Sucan, I., & Cousins, S. (2012). Moveit![ros topics]. IEEE Robotics & Automation Magazine, 19(1), 18-19.
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5 Sucan, I. A., Moll, M., & Kavraki, L. E. (2012). The open motion planning library. IEEE Robotics & Automation Magazine, 19(4), 72-82.
- Referans6
6 Zucker, M., Ratliff, N., Dragan, A. D., Pivtoraiko, M., Klingensmith, M., Dellin, C. M., ... & Srinivasa, S. S. (2013). Chomp: Covariant hamiltonian optimization for motion planning. The International Journal of Robotics Research, 32(9-10), 1164-1193.
- Referans7
7 Kalakrishnan, M., Chitta, S., Theodorou, E., Pastor, P., & Schaal, S. (2011, May). STOMP: Stochastic trajectory optimization for motion planning. In 2011 IEEE international conference on robotics and automation (pp. 4569-4574). IEEE.
- Referans8
8 LaValle, S. M., Kuffner, J. J., & Donald, B. R. (2001). Rapidly-exploring random trees: Progress and prospects. Algorithmic and computational robotics: new directions, 5, 293-308.
- Referans9
9 Kuffner, J. J., & LaValle, S. M. (2000, April). RRT-connect: An efficient approach to single-query path planning. In Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No. 00CH37065) (Vol. 2, pp. 995-1001). IEEE.
- Referans10
10 Karaman, S., & Frazzoli, E. (2011). Sampling-based algorithms for optimal motion planning. The international journal of robotics research, 30(7), 846-894.
- Referans11
11 Kavraki, L. E., Svestka, P., Latombe, J. C., & Overmars, M. H. (1996). Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE transactions on Robotics and Automation, 12(4), 566-580.
- Referans12
12 Marble, J. D., & Bekris, K. E. (2013). Asymptotically near-optimal planning with probabilistic roadmap spanners. IEEE Transactions on Robotics, 29(2), 432-444.
- Referans13
13 Plaku, E., Bekris, K. E., Chen, B. Y., Ladd, A. M., & Kavraki, L. E. (2005). Sampling-based roadmap of trees for parallel motion planning. IEEE Transactions on Robotics, 21(4), 597-608.
- Referans14
14 Sucan, I. A., & Kavraki, L. E. (2011). A sampling-based tree planner for systems with complex dynamics. IEEE Transactions on Robotics, 28(1), 116-131.
- Referans15
15 Görner, M., Haschke, R., Ritter, H., & Zhang, J. (2019, May). Moveit! task constructor for task-level motion planning. In 2019 International Conference on Robotics and Automation (ICRA) (pp. 190-196). IEEE.
- Referans16
16 Gasparetto, A., Boscariol, P., Lanzutti, A., & Vidoni, R. (2012). Trajectory planning in robotics. Mathematics in Computer Science, 6(3), 269-279.
- Referans17
17 Sciavicco, L., Siciliano, B., Villani, L., Oriolo, G.: Robotics. Modelling, Planning and Control. Springer, London (2009)
Referans18
- 18 Krishnaswamy, K., Sleeman, J., & Oates, T. (2011, May). Real-time path planning for a robotic arm. In Proceedings of the 4th International Conference on Pervasive Technologies Related to Assistive Environments (pp. 1-4).
- Referans19
19 Roy, R., Mahadevappa, M., & Kumar, C. S. (2016). Trajectory path planning of EEG controlled robotic arm using GA. Procedia Computer Science, 84, 147-151.
- Referans20
20 Hao, W. G., Leck, Y. Y., & Hun, L. C. (2011, May). 6-DOF PC-Based Robotic Arm (PC-ROBOARM) with efficient trajectory planning and speed control. In 2011 4th International Conference on Mechatronics (ICOM) (pp. 1-7). IEEE.
- Referans21
21 Xie, B., Zhao, J., & Liu, Y. (2011, June). Human-like motion planning for robotic arm system. In 2011 15th International Conference on Advanced Robotics (ICAR) (pp. 88-93). IEEE.
- Referans22
22 Iqbal, J., Islam, R. U., & Khan, H. (2012). Modeling and analysis of a 6 DOF robotic arm manipulator. Canadian Journal on Electrical and Electronics Engineering, 3(6), 300-306.
- Referans23
23 Gasparetto, A., Boscariol, P., Lanzutti, A., & Vidoni, R. (2015). Path planning and trajectory planning algorithms: A general overview. Motion and operation planning of robotic systems, 3-27.
- Referans24
24 Streinu, I. (2000, November). A combinatorial approach to planar non-colliding robot arm motion planning. In Proceedings 41st Annual Symposium on Foundations of Computer Science (pp. 443-453). IEEE.
- Referans25
25 Xinyu, W., Xiaojuan, L., Yong, G., Jiadong, S., & Rui, W. (2019). Bidirectional potential guided RRT* for motion planning. IEEE Access, 7, 95046-95057.
- Referans26
26 Savsani, P., Jhala, R. L., & Savsani, V. J. (2013, April). Optimized trajectory planning of a robotic arm using teaching learning based optimization (TLBO) and artificial bee colony (ABC) optimization techniques. In 2013 IEEE International Systems Conference (SysCon) (pp. 381-386). IEEE.
- Referans27
27 Cousins, S. (2010). Ros on the pr2 [ros topics]. IEEE Robotics & Automation Magazine, 17(3), 23-25.
- Referans28
28 Wurm, K. M., Hornung, A., Bennewitz, M., Stachniss, C., & Burgard, W. (2010, May). OctoMap: A probabilistic, flexible, and compact 3D map representation for robotic systems. In Proc. of the ICRA 2010 workshop on best practice in 3D perception and modeling for mobile manipulation (Vol. 2).
- Referans29
29 LaValle, S. M. (1998). Rapidly-exploring random trees: A new tool for path planning.
- Referans30
30 LaValle, S. M., & Kuffner Jr, J. J. (2001). Randomized kinodynamic planning. The international journal of robotics research, 20(5), 378-400.
- Referans31
31 Sagan, H. and J. Holbrook: "Space-filling curves", Springer-Verlag, New York, 1994.
- Referans32
32 Karaman, S., & Frazzoli, E. (2010). Incremental sampling-based algorithms for optimal motion planning. Robotics Science and Systems VI, 104(2).
- Referans33
33 Sniedovich, M. (2006). Dijkstra's algorithm revisited: the dynamic programming connexion. Control and cybernetics, 35(3), 599-620.
- Referans34
34 Hsu, D., Latombe, J. C., & Motwani, R. (1997, April). Path planning in expansive configuration spaces. In Proceedings of International Conference on Robotics and Automation (Vol. 3, pp. 2719-2726). IEEE.
- Referans35
35 Coleman, D., Sucan, I., Chitta, S., & Correll, N. (2014). Reducing the barrier to entry of complex robotic software: a moveit! case study. arXiv preprint arXiv:1404.3785.
- Referans36
36 Moll, M., Sucan, I. A., & Kavraki, L. E. (2014). An extensible benchmarking infrastructure for motion planning algorithms. arXiv preprint arXiv:1412.6673.
- Referans37
37 Cohen, B., Şucan, I. A., & Chitta, S. (2012, October). A generic infrastructure for benchmarking motion planners. In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 589-595). IEEE.
- Referans38
38 Liu, S., & Liu, P. (2021). Robot Motion Planning Benchmarking and Optimization using Motion Planning Pipeline.