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Sampling-based motion planning of manipulator with goal-oriented sampling

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Abstract

A sampling-based planning algorithm is one of the most powerful tools for collision avoidance in the motion planning of manipulators. However, this algorithm takes a long time to generate motions of the manipulator. This work proposes a goal-oriented (GO) sampling method for the motion planning of a manipulator. The GO sampling method can identify the initial solution in a shorter time than other sampling-based algorithms, leading to significant improvement in computational efficiency. Based on the GO sampling method, cases involving configuration space and collision checking are implemented based on the proposed equations in the planning of manipulator motion. Different combinations of configuration space settings are mainly analyzed and compared through experiments using a six-degree-of-freedom manipulator.

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References

  1. Kavraki LE, Svestka P, Latombe JC, Overmars MH (1996) Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Trans Robot Autom 12(4):566–580

    Article  Google Scholar 

  2. LaValle SM, Kuffner JJ (2001) Randomized kinodynamic planning. Int J Robot Res 20(5):378–400

    Article  Google Scholar 

  3. Elbanhawi M, Simic M, Jazar RN (2014) Continuous path smoothing for car-like robots using B-spline curves. J Intell Robot Syst 80:23–56

    Article  Google Scholar 

  4. Kwon H, Chung W (2014) Performance analysis of path planners for car-like vehicles toward automatic parking control. Intell Serv Robot 7(1):15–23

    Article  Google Scholar 

  5. Karaman S, Walter M, Perez A, Frazzoli E, Teller S (2011) Anytime motion planning using the RRT*. In: Proceedings of IEEE international conference on robotics and automation (ICRA), pp 1478–1483

  6. Kuffner J, LaValle S (2000) RRT-connect: an efficient approach to single-query path planning. In: Proceedings of IEEE international conference on robotics and automation (ICRA), pp 995–1001

  7. Gammell JD, Srinivasa SS, Barfoot TD (2014) Informed RRT*: optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic. In: Proceedings of IEEE/RSJ international conference on intelligent robots and systems (IROS)

  8. Choudhury S, Gammell JD, Barfoot TD, Srinivasa SS, Scherer S (2016) Regionally accelerated batch informed trees (RABIT*): a framework to integrate local information into optimal path planning. In: Proceedings of IEEE international conference on robotics and automation (ICRA), pp 4207–4214

  9. Kim MC, Song JB (2018) Informed RRT* with improved converging rate by adopting wrapping procedure. Intell Serv Robot 11(1):53–60

    Article  Google Scholar 

  10. Weghe MV, Ferguson D, Srinivasa SS (2007) Randomized path planning for redundant manipulators without inverse kinematics. In: Proceedings of IEEE-RAS international conference on humanoid robots, pp 477–482

  11. Perez A, Karaman S, Shkolnik A, Frazzoli E, Teller S, Walter MR (2011) Asymptotically-optimal path planning for manipulation using incremental sampling-based algorithms. In: Proceedings of IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 25–30

  12. Kang G, Kim YB, You WS, Lee YH, Oh HS, Moon H, Choi HR (2016) Sampling-based path planning with goal oriented sampling. In: Proceedings of IEEE international conference on advanced intelligent mechatronics (AIM), pp 1285–1290

  13. Yershova A, Jaillet L, Simeon T, LaValle S (2005) Dynamic-domain RRTs: efficient exploration by controlling the sampling domain. In: Proceedings of IEEE international conference on robotics and automation (ICRA), pp 3856–3861

  14. Noreen I, Khan A, Ryu H, Doh NL, Habib Z (2017) Optimal path planning in cluttered environment using RRT*-AB. In: Intelligent Service Robotics, pp 1–12

  15. Perez-Sala X, Igual L, Escalera S, Angulo C (2013) Uniform sampling of rotations for discrete and continuous learning of 2D shape models. In: Robotic vision: technologies for machine learning and vision applications, IGI Global, pp 56–77

  16. Stilman M (2010) Global manipulation planning in robot joint space with task constraints. IEEE Trans Robot 28(3):576–584

    Article  Google Scholar 

  17. Bohlin R, Kavraki EE (2000) Path planning using lazy PRM. In: Proceedings of IEEE international conference on robotics and automation (ICRA), vol 1, pp 521–528

  18. Lin M, Gottschalk S (1998) Collision detection between geometric models: a survey. In: Proceedings of IMA conference on mathematics of surfaces, vol 1, pp 602–608

  19. Duguleana M, Barbuceanu FG, Teirelbar A, Mogan G (2012) Obstacle avoidance of redundant manipulators using neural networks based reinforcement learning. Robot Comput Integr Manuf 28:132–146

    Google Scholar 

  20. Eberly D (2002) Dynamic collision detection using oriented bounding boxes. Technical report. Geometric Tools Inc, Scottsdale

  21. Elbanhawi M, Simic M (2014) Sampling-based robot motion planning: a review. IEEE Access 2:56–77

    Article  Google Scholar 

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Acknowledgements

This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of Republic of Korea (No. 20171510300500).

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Authors and Affiliations

  1. School of Mechanical Engineering, Sungkyunkwan University, ChunchunDong 300, Jangan-gu, Suwon, Kyungki-do, 440-746, Korea

    Gitae Kang, Yong Bum Kim, Young Hun Lee, Hyun Seok Oh, Won Suk You & Hyouk Ryeol Choi

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  1. Gitae Kang
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  2. Yong Bum Kim
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  3. Young Hun Lee
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  4. Hyun Seok Oh
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  5. Won Suk You
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  6. Hyouk Ryeol Choi
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Correspondence to Hyouk Ryeol Choi.

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Kang, G., Kim, Y.B., Lee, Y.H. et al. Sampling-based motion planning of manipulator with goal-oriented sampling. Intel Serv Robotics 12, 265–273 (2019). https://doi.org/10.1007/s11370-019-00281-y

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  • DOI: https://doi.org/10.1007/s11370-019-00281-y

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