Hostname: page-component-78c5997874-dh8gc Total loading time: 0 Render date: 2024-11-16T17:04:39.688Z Has data issue: false hasContentIssue false
  • English
  • Français

Comparison of different sample-based motion planning methods in redundant robotic manipulators

Published online by Cambridge University Press:  17 February 2022

Mehdi Shahabi ,
Hashem Ghariblu Open the ORCID record for Hashem Ghariblu [Opens in a new window]  and
Manuel Beschi
Mehdi Shahabi
Affiliation:
Mechanical Engineering Department, University of Zanjan, Zanjan, Iran
Hashem Ghariblu*
Affiliation:
Mechanical Engineering Department, University of Zanjan, Zanjan, Iran
Manuel Beschi
Affiliation:
Italian National Research Council, Rome, Italy
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

The main objective of a motion planning algorithm is to find a collision-free path in the workspace of a robotic manipulator in a point-to-point motion. Among the various motion planning methods available, sample-based motion planning algorithms are easy to use, quick and powerful in redundant robotic systems applications. In this study, different sampling-based motion planning algorithms are employed to select the most appropriate method for efficient collision-free motion planning. As a case study, finding a collision-free robotic displacement for welding a main pipe with other intersecting pipes and joints is considered. The robotic manipulator employed in this study has seven degrees of freedom, where six degrees are related to the manipulator joints and one degree is related to its base linear movement suspended from ceiling. Five criteria, time, path length, path time, path smoothness and process time are used to evaluate the efficiency of different sample-based motion planning algorithms. Finally, a smaller set of more efficient algorithms are introduced based on the defined efficiency evaluation criteria.

Keywords

sample-basedmotion planningredundantrobotic manipulator
Type
Research Article
Information
Robotica , Volume 40 , Issue 9 , September 2022 , pp. 3104 - 3119
Copyright
© The Author(s), 2022. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Shahabi, M. and Ghariblu, H., “Optimal joint motion for complicated welding geometry by a redundant robotic system,” Eng. Optim. 52(5), 875–895 (2020).CrossRefGoogle Scholar
Shahabi, M., Ghariblu, H. and Beschi, M., “Obstacle avoidance of redundant robotic manipulators using safety ring concept,” Int. J. Comput. Integr. Manuf. 32(7), 695–704 (2019).CrossRefGoogle Scholar
Shahabi, M., Ghariblu, H., Beschi, M. and Pedrocchi, N. et al. “Path planning methodology for multi-layer welding of intersecting pipes considering collision avoidance,” Robotica 39(6), 945–958 (2021).CrossRefGoogle Scholar
Khatib, O., “Real-time obstacle avoidance for manipulators and mobile robots,” Proceedings. 1985 IEEE International Conference on Robotics and Automation (1985) IEEE.CrossRefGoogle Scholar
Mingxin, Y., Sun’an, W., Canyang, W. and Kunpeng, L., “Hybrid ant colony and immune network algorithm based on improved APF for optimal motion planning,” Robotica 28(6), 833–846 (2010).CrossRefGoogle Scholar
Likhachev, M., Search-based planning library(SBPL) (2016).Google Scholar
irzaei Teshnizi, M. M., Kosari, A., Goliaei, S. and Shakhesi, S., “Centralized Path Planning for Multi-aircraft in the Presence of Static and Moving Obstacles,” Int. J. Eng. 33(5), 923–933 (2020).Google Scholar
Elshamli, A., Abdullah, H. A. and Areibi, S., “Genetic algorithm for dynamic path planning,” Electrical and Computer Engineering, 2004. Canadian Conference on (2004) IEEE.Google Scholar
Sadegh Moghadasi, S. and Faraji, N., “An efficient target tracking algorithm based on particle filter and genetic algorithm,” Int. J. Eng. 32(7), 915–923 (2019).Google Scholar
Abu-Dakka, F. J. M., Trajectory planning for industrial robot using genetic algorithms (2011).Google Scholar
Abo-Hammour, Z. S., Alsmadi, O. M., Bataineh, S. I., Al-Omari, M. A. and Affach, N., “Continuous genetic algorithms for collision-free cartesian path planning of robot manipulators,” Int. J. Adv. Robot. Syst. 8(6), 74 (2011).CrossRefGoogle Scholar
Nearchou, A. C. and Aspragathos, N. A., “A genetic path planning algorithm for redundant articulated robots,” Robotica 15(02), 213–224 (1997).CrossRefGoogle Scholar
Koren, Y., and Borenstein, J., “Potential field methods and their inherent limitations for mobile robot navigation,” Robotics and Automation, 1991. Proceedings., 1991 IEEE International Conference on (1991) IEEE.Google Scholar
Connolly, C. I., Burns, J. B. and Weiss, R., “Path planning using Laplace’s equation,” Robotics and Automation, 1990. Proceedings., 1990 IEEE International Conference on (1990) IEEE.Google Scholar
Ge, S. S., and Cui, Y. J., “New potential functions for mobile robot path planning,” IEEE Trans. Robot. Autom. 16(5), 615–620 (2000).CrossRefGoogle Scholar
Keselman, L., Motion Planning for Redundant Manipulators and Other High Degree-of-Freedom Systems (Georgia Institute of Technology, Georgia, USA, 2014).Google Scholar
Likhachev, M. and Ferguson, D., “Planning long dynamically feasible maneuvers for autonomous vehicles,” Int. J. Robot. Res. 28(8), 933–945 (2009).CrossRefGoogle Scholar
Pivtoraiko, M., Mellinger, D., and Kumar, V., “Incremental micro-UAV motion replanning for exploring unknown environments,” Robotics and Automation (ICRA), 2013 IEEE International Conference on (2013) IEEE.CrossRefGoogle Scholar
Pivtoraiko, M. and Kelly, A., “Kinodynamic motion planning with state lattice motion primitives,” Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International Conference on (2011) IEEE.CrossRefGoogle Scholar
Pivtoraiko, M., Knepper, R. A. and Kelly, A., “Differentially constrained mobile robot motion planning in state lattices,” J. Field Robot. 26(3), 308–333 (2009).CrossRefGoogle Scholar
Kang, G., Yong, B. K., Young, H. L., Hyun, S. O., Won, S. Y. and Hyouk, R. C., “Sampling-based motion planning of manipulator with goal-oriented sampling,” Intell. Serv. Robot. 12(3), 265–273 (2019).CrossRefGoogle Scholar
Tsianos, K. I., Sucan, I. A. and Kavraki, L. E., “Sampling-based robot motion planning: Towards realistic applications,” Comput. Sci. Rev. 1(1), 2–11 (2007).CrossRefGoogle Scholar
Kaltsoukalas, K., Makris, S. and Chryssolouris, G., “On generating the motion of industrial robot manipulators,” Robot. Comput. Integr. Manuf. 32, 65–71 (2015).CrossRefGoogle Scholar
Larsen, L., Kim, J., Kupke, M. and Schuster, A., “Automatic path planning of industrial robots comparing sampling-based and computational intelligence methods,” Procedia Manuf. 11, 241–248 (2017).CrossRefGoogle Scholar
Kwon, M., Zhou, D., Liu, S. and Zhang, H., “A Robotic System for Autonomous Grasping and Manipulation,” In: First Robotic Grasping and Manipulation Challenge (Yu Sun and Joe Falco, eds.) (Springer, Cham, 2016) pp. 816. Google Scholar
Alatartsev, S., Stellmacher, S. and Ortmeier, F., “Robotic task sequencing problem: A survey,” J. Intell. Robot. Syst. 80(2), 279–298 (2015).CrossRefGoogle Scholar
Zhu, T., Tang, X., Chen, C., Peng, F., Yan, R., Zhou, L., Li, Z. and Wu, J., “High precision and efficiency robotic milling of complex parts: challenges, approaches and trends” Chinese J. Aeronaut. 35(2), 22–46 (2022).CrossRefGoogle Scholar
Javanmard, A. and Ghariblu, H., “The effect of base position on maximum allowable load of dual arm robots,” Int. J. Eng. 28(2), 284–294 (2015).Google Scholar
Ghariblu, H. and Shahabi, M., “Path planning of complex pipe joints welding with redundant robotic systems,” Robotica 37(6), 1020–1032 (2019).CrossRefGoogle Scholar
Beschi, M., Mutti, S., Nicola, G., Faroni, M., Magnoni, P., Villagrossi, E. and Pedrocchi, N., “Optimal robot motion planning of redundant robots in machining and additive manufacturing applications,” Electronics 8(12), 1437 (2019).CrossRefGoogle Scholar
Deng, W., Yao, J., Wang, Y., Yang, X. and Chen, J., “Output feedback backstepping control of hydraulic actuators with valve dynamics compensation,” Mech. Syst. Signal Process. 158 (2021).CrossRefGoogle Scholar
Deng, W. and Yao, J. “Asymptotic tracking control of mechanical servosystems with mismatched uncertainties,” IEEE/ASME Trans. Mechatron. 26(4), 2204–2214 (2020).CrossRefGoogle Scholar
Kavraki, L., Svestka, P. and Overmars, M. H., Probabilistic Roadmaps for Path Planning in High-Dimensional Configuration Spaces. IEEE Transactions on Robotics and Automation, 12(4), 566–580 (1996).Google Scholar
LaValle, S. M., Rapidly-exploring random trees: A new tool for path planning. The annual research report (1998): n. pag.Google Scholar
Karaman, S. and Frazzoli, E., “Sampling-based algorithms for optimal motion planning,” Int. J. Robot. Res. 30(7), 846–894 (2011).CrossRefGoogle Scholar
Kuffner, J. J. and LaValle, S. M., “RRT-connect: An efficient approach to single-query path planning,” Robotics and Automation, 2000. Proceedings. ICRA′00. IEEE International Conference on (2000) IEEE.Google Scholar
Salzman, O. and Halperin, D., “Asymptotically near-optimal RRT for fast, high-quality motion planning,” IEEE Trans. Robot. 32(3), 473–483 (2016).CrossRefGoogle Scholar
Jaillet, L., Cortés, J. and Siméon, T., “Sampling-based path planning on configuration-space costmaps,” IEEE Trans. Robot. 26(4), 635–646 (2010).CrossRefGoogle Scholar
Ko, I., Kim, B. and Park, F. C., “VF-RRT: Introducing optimization into randomized motion planning,” Control Conference (ASCC), 2013 9th Asian (2013) IEEE.Google Scholar
Bialkowski, J., Karaman, S. and Frazzoli, E., “Massively parallelizing the RRT and the RRT,” Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International Conference on (2011) IEEE.CrossRefGoogle Scholar
Devaurs, D., Siméon, T. and Cortés, J., “Enhancing the transition-based RRT to deal with complex cost spaces,” Robotics and Automation (ICRA), 2013 IEEE International Conference on (2013) IEEE.CrossRefGoogle Scholar
Hsu, D., Latombe, J.-C. and Motwani, R., “Path planning in expansive configuration spaces,” Proceedings of International Conference on Robotics and Automation (1997) IEEE.Google Scholar
Sánchez, G. and Latombe, J.-C., “A single-query bi-directional probabilistic roadmap planner with lazy collision checking,” In: Robotics Research (Raymond Austin Jarvis and Alexander Zelinsky, eds.) (Springer, Berlin, Heidelberg, 2003)pp. 403–417.CrossRefGoogle Scholar
Şucan, I. A. and Kavraki, L. E., “Kinodynamic motion planning by interior-exterior cell exploration,” In: Algorithmic Foundation of Robotics VIII, Springer Tracts in Advanced Robotics (Gregory S. Chirikjian, Howie Choset, Marco Morales and Todd Murphey, eds.) (Springer, Berlin, Heidelberg) pp. 449–464.CrossRefGoogle Scholar
Gipson, B., Moll, M. and Kavraki, L. E., “Resolution independent density estimation for motion planning in high-dimensional spaces,” Robotics and Automation (ICRA), 2013 IEEE International Conference on (2013) IEEE.CrossRefGoogle Scholar
Ladd, A. M. and Kavraki, L. E., “Motion Planning in the Presence of Drift, Underactuation and Discrete System Changes,” In: Robotics: Science and Systems (2005).CrossRefGoogle Scholar
Janson, L., Schmerling, E., Clark, A. and Pavone, M. et al. , “Fast marching tree: A fast marching sampling-based method for optimal motion planning in many dimensions,” Int. J. Robot. Res. 34(7), 883–921 (2015).CrossRefGoogle ScholarPubMed
Starek, J. A., Gomez, V., Schmerling, E., Janson, L., Moreno, L. and Pavone, M.. “An asymptotically-optimal sampling-based algorithm for bi-directional motion planning,” Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on (2015) IEEE.CrossRefGoogle Scholar
Bohlin, R. and Kavraki, L. E., “Path planning using lazy PRM,” Robotics and Automation, 2000. Proceedings. ICRA′00. IEEE International Conference on (2000) IEEE.Google Scholar
Dobson, A. and Bekris, K. E., “Sparse roadmap spanners for asymptotically near-optimal motion planning,” Int. J. Robot. Res. 33(1), 18–47 (2014).CrossRefGoogle Scholar
Dobson, A. and Bekris, K. E., “Improving sparse roadmap spanners,” Robotics and Automation (ICRA), 2013 IEEE International Conference on (2013) IEEE.Google Scholar