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Motion Planning Method of Redundant Dual-Chain Manipulator with Multiple Constraints

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Abstract

Redundant and dual-arm manipulators exhibit good flexibility and excellent cooperation ability, respectively. Therefore, both these manipulators are used to design a redundant dual-chain manipulator with two kinematic chains and a fixed base. In addition, a motion planning method is proposed using a manifold analysis based on the characteristics of this designed manipulator to resolve the difficulty associated with motion planning for a dual-chain manipulator under multiple constraints. Firstly, the paths of the two end-effectors of the dual-chain manipulator are planned synchronously by the Rapidly-exploring Random Tree Star (RRT*) in the Cartesian space. Then, joint trajectories that correspond to the paths are generated by the manifold analysis in the joint space. In this method, the manifold continuity constraint is added to the end-effector path planning of RRT* to ensure that the planned paths have continuous joint trajectories. The joint trajectories are optimised by analysing the performance of the manipulator on the manifolds to improve their motion performance. The prototype experiments of loose coordination and tight coordination are provided in the experimental section; these experiments prove the feasibility and practicability of the motion planning method proposed in this study.

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Data Availability

The raw evaluation data can be viewed at: https://www.scidb.cn/anonymous/SkozWVZ6.

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References

  1. Fan, J., Jin, L., Xie, Z., et al.: Data-driven motion-force control scheme for redundant manipulators: A kinematic perspective. IEEE Trans. Industr. Inform. 18, 5338–5347 (2022). https://doi.org/10.1109/TII.2021.3125449

    Article  Google Scholar 

  2. Wang, X., Chen, L.: A vision-based coordinated motion scheme for dual-arm robots. J. Intell. Robot. Syst. 97, 67–79 (2020). https://doi.org/10.1007/s10846-019-01035-9

    Article  Google Scholar 

  3. Matsueda, T., Kuraoka, K., Goma, K., et al.: JEMRMS system design and development status. In: NTC '91 - National Telesystems Conference Proceedings, pp. 391–395 (1991). https://doi.org/10.1109/NTC.1991.148052

  4. Coleshill, E., Oshinowo, L., Rembala, R., et al.: Dextre: Improving maintenance operations on the international space station. Acta Astronaut. 64, 869–874 (2009). https://doi.org/10.1016/j.actaastro.2008.11.011

    Article  Google Scholar 

  5. Lynch, K.M., Park, F.: Modern robotics -Mechanics, planning, and control. Cambridge University Press, Cambridge (2017)

    Google Scholar 

  6. Minnetoglu, O., Conkur, E.S.: Tight maneuvering for path planning of hyper-redundant manipulators in three-dimensional environments. Appl. Sci. 12, 8882 (2022). https://doi.org/10.3390/app12178882

    Article  Google Scholar 

  7. Wei, Y., Zheng, Z., Li, Q., et al.: A trajectory planning method for the redundant manipulator based on configuration plane. Int. J. Adv. Robot. Syst. 18(6), 17298814211058558 (2021). https://doi.org/10.1177/17298814211058558

  8. Canny, J.: A Voronoi method for the piano-movers problem. Proc. IEEE Int. Conf. Rob. Autom. 530–535 (1985). https://doi.org/10.1109/ROBOT.1985.1087297

  9. Elbanhawi, M., Simic, M., Jazar, R.: Autonomous robots path planning: An adaptive roadmap approach. Appl. Mech. Mater. 246–254 (2013). https://doi.org/10.4028/www.scientific.net/AMM.373-375.246

  10. Asano, T., Asano, T., Guibas, L., et al.: Visibility-polygon search and Euclidean shortest paths. Proc. Annu. IEEE Symp. Found. Comput. Sci FOCS, 155–164 (1985). https://doi.org/10.1109/SFCS.1985.65

  11. Dijkstra, E.W.: A note on two problems in connexion with graphs. Numer. Math. 1, 269–271 (1959). https://doi.org/10.1007/BF01386390

  12. Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern. 4(2), 100–107 (1968). https://doi.org/10.1109/TSSC.1968.300136

  13. Stentz, A.: Optimal and efficient path planning for unknown and dynamic environments. Int. J. Robot. Autom. 10(3), 89–100 (1995)

  14. Likhachev, M., Ferguson, D., Gordon, G., et al.: Anytime search in dynamic graphs. Artif. Intell. 172(14), 1613–1643 (2008). https://doi.org/10.1016/j.artint.2007.11.009

  15. Amato, N.M., Wu, Y.: Randomized roadmap method for path and manipulation planning. Proc. IEEE Int. Conf. Rob. Autom. 113–120 (1996). https://doi.org/10.1109/ROBOT.1996.503582

  16. Xanthidis, M., Esposito, J.M., Rekleitis, I., et al.: Motion planning by sampling in subspaces of progressively increasing dimension. J. Intell. Robot. Syst. 100, 777–789 (2020). https://doi.org/10.1007/s10846-020-01217-w

    Article  Google Scholar 

  17. Li, Z., Peng, F., Yan, R., et al.: A virtual repulsive potential field algorithm of posture trajectory planning for precision improvement in robotic multi-axis milling. Robot. Comput. Integr. Manuf. 74, 102288 (2022). https://doi.org/10.1016/j.rcim.2021.102288

    Article  Google Scholar 

  18. Zhang, H., Zhu, Y., Liu, X., et al.: Analysis of obstacle avoidance strategy for dual-arm robot based on speed field with improved artificial potential field algorithm. Electronics. 10(15), 1850 (2021). https://doi.org/10.3390/electronics10151850

  19. Li, Y., Liu, K.: Two-degree-of-freedom manipulator path planning based on zeroing neural network. MATEC Web Conf. 309, 04005 (2020). https://doi.org/10.1051/matecconf/202030904005

  20. Baressi Segota, S., Andelic, N., Lorencin, I., et al.: Path planning optimization of six-degree-of-freedom robotic manipulators using evolutionary algorithms. Int. J. Adv. Robot. Syst. 17(2), 1729881420908076 (2020). https://doi.org/10.1177/1729881420908076

  21. Baghli, F.Z., Bakkali, L. el, Lakhal, Y.: Optimization of arm manipulator trajectory planning in the presence of obstacles by ant colony algorithm. Procedia Eng., pp. 560-567 (2017). https://doi.org/10.1016/j.proeng.2017.02.434

  22. Wang, W., Fu, Z., Yang, L., et al.: Application of artificial bee colony algorithm in time-optimal trajectory planning of manipulators. Intl. J. Adv. Comput. Technolog. 4(22), 537–544 (2012). https://doi.org/10.4156/ijact.vol4.issue22.60

  23. Ren, J.Y., Sun, H.X.: Novel method of trajectory planning in Cartesian space. Robot. 24(3), 217–221 (2002). https://doi.org/10.3321/j.issn:1002-0446.2002.03.006

  24. Subir, K.S.: Introduction to robotics. Harbin Institute of Technology Press, Harbin (2017)

    Google Scholar 

  25. Sun, J., Han, X., Zuo, Y., et al.: Trajectory planning in joint space for a pointing mechanism based on a novel hybrid interpolation algorithm and NSGA-II algorithm. IEEE Access. 8, 228628–228638 (2020). https://doi.org/10.1109/ACCESS.2020.3042890

    Article  Google Scholar 

  26. Buschhaus, A., Krusemark, S., Karlidag, E., et al.: Universal fine interpolation algorithms for accuracy improvements of industrial robots. Int. Cong. Ultra Mod. Telecommun. Control Syst. Workshops. 356–362 (2016). https://doi.org/10.1109/ICUMT.2016.7765402

  27. Gasparetto, A., Zanotto, V.: A new method for smooth trajectory planning of robot manipulators. Mech. Mach. Theory. 42(4), 455–471 (2007). https://doi.org/10.1016/j.mechmachtheory.2006.04.002

  28. Yan, L., Mu, Z., Xu, W., et al.: Coordinated compliance control of dual-arm robot for payload manipulation: Master-slave and shared force control. IEEE Int. Conf. Intell. Rob. Syst. 2697–2702 (2016). https://doi.org/10.1109/IROS.2016.7759419

  29. Burdick, J.W.: On the inverse kinematics of redundant manipulators: Characterization of the self-motion manifolds. Proc. IEEE Int. Conf. Rob. Autom. 264–270 (1989). https://doi.org/10.1109/ROBOT.1989.99999

  30. Peidro, A., Reinoso, O., Gil, A., et al.: A method based on the vanishing of self-motion manifolds to determine the collision-free workspace of redundant robots. Mech. Mach. Theory. 128, 84–109 (2018). https://doi.org/10.1016/j.mechmachtheory.2018.05.013

  31. Liegeois, A.: Automatic supervisory control of the configuration and behavior of multibody mechanisms. IEEE Trans. Syst. Man. Cybern. 7(12), 868–871 (1977). https://doi.org/10.1109/tsmc.1977.4309644

  32. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. J. Glob. Optim. 39, 459–471 (2007). https://doi.org/10.1007/s10898-007-9149-x

  33. Karaman, S., Frazzoli, E.: Sampling-based algorithms for optimal motion planning. Int. J. Rob. Res. 30, 846–894 (2011). https://doi.org/10.1177/0278364911406761

    Article  MATH  Google Scholar 

  34. Mu, Z., Liu, L., Jia, L., et al.: Intelligent demolition robot: Structural statics, collision detection, and dynamic control. Autom. Constr. 142, 104490 (2022). https://doi.org/10.1016/j.autcon.2022.104490

    Article  Google Scholar 

  35. Peiper, D.L.: The kinematics of manipulators under computer control. PhD Dissertation,. Stanford University (1969)

Download references

Acknowledgements

This work has been supported by the National Natural Science Foundation of China (Grant numbers 51975008 and 52275001).

Funding

This work was supported by National Natural Science Foundation of China (Grant numbers 52275001 and 51975008).

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Authors and Affiliations

Authors

Contributions

All authors contributed to the study conception and design. Data collection and analysis were performed by Zhenyong Zhou and Ziqiang Zhang. The experimental scheme was formulated by Xiaohui Li. The first draft of the manuscript was written by Jing Zhao and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Jing Zhao.

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Appendices

Appendix 1: The D-H Parameters of the Leader and Follower Chains

Table 4

Table 4 D-H parameters of the leader chain manipulator

Table 5

Table 5 D-H parameters of the follower chain manipulator

Appendix 2: Derivation of Analytical Expressions for Inverse Kinematics of Follower Chain

The end-effector poses of the dual-chain manipulator can be calculated when the angle of each joint is known. The end-effector pose \({}_{t1}{}^{0}{\varvec{T}}\) of the leader chain can be expressed as

$${}_{t1}{}^{0}{\varvec{T}}\text{=}{}_{1}{}^{0}{\varvec{T}}{}_{2}{}^{1}T{}_{3}{}^{2}{\varvec{T}}{}_{4}{}^{3}T{}_{5}{}^{4}{\varvec{T}}{}_{6}{}^{5}{\varvec{T}}{}_{7}{}^{6}{\varvec{T}}{}_{t1}{}^{7}{\varvec{T}}$$
(15)

where \({}_{i-1}{}^{i}{\varvec{T}}\in {{\varvec{R}}}^{4\times 4}\) represents the transformation relation between adjacent coordinate systems.

The end-effector pose \({}_{t2}{}^{0}{\varvec{T}}\) of the follower chain can be expressed as

$${}_{t2}{}^{0}{\varvec{T}}\text{=}{}_{4}{}^{0}{\varvec{T}}{}_{8}{}^{4}{\varvec{T}}{}_{9}{}^{8}{\varvec{T}}{}_{10}{}^{9}{\varvec{T}}{}_{11}{}^{10}{\varvec{T}}{}_{12}{}^{11}{\varvec{T}}{}_{13}{}^{12}{\varvec{T}}{}_{t2}{}^{13}{\varvec{T}}$$
(16)

where \({}_{4}{}^{0}{\varvec{T}}\) represents the pose of joint 4 relative to the world coordinate system, which is obtained by calculating Eq. 15.

A closed solution exists because the axes of joints 9–11 of the follower chain manipulator are parallel and satisfy the Pieper criterion. According to Eq. 16, the pose of the follower chain end-effector relative to the coordinate system of joint 4 can be expressed as

$${}_{13}{}^{4}{\varvec{T}}\text{=}{{}_{4}{}^{0}{\varvec{T}}}^{-1}{}_{t2}{}^{0}{\varvec{T}}{{}_{t2}{}^{13}{\varvec{T}}}^{-1}\text{=}{}_{8}{}^{4}{\varvec{T}}{}_{9}{}^{8}{\varvec{T}}{}_{10}{}^{9}{\varvec{T}}{}_{11}{}^{10}{\varvec{T}}{}_{12}{}^{11}{\varvec{T}}{}_{13}{}^{12}{\varvec{T}}$$
(17)

where \({}_{13}{}^{4}{\varvec{T}}\) can be denoted as \({}_{13}{}^{4}{\varvec{T}}\text{=}\left[\begin{array}{cccc}{n}_{x}& {o}_{x}& {a}_{x}& {p}_{x}\\ {n}_{y}& {o}_{y}& {a}_{y}& {p}_{y}\\ {n}_{z}& {o}_{z}& {a}_{z}& {p}_{z}\\ 0& 0& 0& 1\end{array}\right]\).

The analytical equation of the follower chain joint 8 is

$${\theta }_{8}=\pm 2{\text{arctan}}\left(\sqrt{\frac{1-{m}_{1}}{1+{m}_{1}}}\right)-{\text{atan}}2\left(\frac{{p}_{x}}{{n}_{1}},\frac{{p}_{y}}{{n}_{1}}\right)$$
(18)

where \({n}_{1}=\sqrt{{p}_{x}^{2}+{p}_{y}^{2}}\) and \({m}_{1}=\frac{{d}_{9}+{d}_{10}-{d}_{11}}{{n}_{1}}\).

The analytical equation of the follower chain joint 13 is

$${\theta }_{13}={\text{atan}}2\left({n}_{y}{c}_{8}-{n}_{x}{s}_{8},{o}_{y}{c}_{8}-{o}_{x}{s}_{8}\right)\pm \frac{\pi }{2}$$
(19)

where \({c}_{8}\) is shorthand for \(\mathrm{cos}{\theta }_{8}\), and \({s}_{8}\) is shorthand for \(\mathrm{sin}({\theta }_{8})\).

The analytical equation of the follower chain joint 12 is

$${\theta }_{12}={\text{atan}}2\left({m}_{2},{n}_{2}\right)$$
(20)

where \({m}_{2}={n}_{y}{c}_{8}{c}_{13}-{n}_{x}{s}_{6}{c}_{13}-{o}_{y}{c}_{8}{s}_{13}+{o}_{x}{s}_{8}{s}_{13}\) and \({n}_{2}={a}_{x}{s}_{8}-{a}_{y}{c}_{8}\).

The analytical equation of the follower chain joint 10 is

$${\theta }_{10}=\pm 2{\text{arctan}}\left(\sqrt{\frac{1-{o}_{3}}{1+{o}_{3}}}\right)$$
(21)

where \({o}_{3}=\frac{{m}_{3}^{2}+{n}_{3}^{2}-{a}_{10}^{2}-{a}_{11}^{2}}{2{a}_{10}{a}_{11}}\), \({m}_{3}={p}_{x}{c}_{8}+{p}_{y}{s}_{8}-{d}_{12}\left({c}_{13}\left({o}_{x}{c}_{8}+{o}_{y}{s}_{8}\right)+{s}_{13}\left({n}_{x}{c}_{8}+{n}_{y}{s}_{8}\right)\right)\), and \({n}_{3}={d}_{12}\left({n}_{z}{s}_{13}+{o}_{z}{c}_{13}\right)+{d}_{8}-{p}_{z}\).

The analytical equation of the follower chain joint 9 is

$${\theta }_{9}={\text{atan}}2\left({m}_{4},{n}_{4}\right)$$
(22)

where \({m}_{4}=\frac{{n}_{3}\left({a}_{10}+{a}_{11}{c}_{10}\right)-{m}_{3}{a}_{11}{s}_{10}}{{\text{(}{a}_{10}+{a}_{11}{c}_{10}\text{)}}^{2}+{\left({a}_{11}{s}_{10}\right)}^{2}}\) and \({n}_{4}=\frac{{m}_{3}\left({a}_{10}+{a}_{11}{c}_{10}\right)+{n}_{3}{a}_{11}{s}_{10}}{{\left({a}_{10}+{a}_{11}{c}_{10}\right)}^{2}+{\left({a}_{11}{s}_{10}\right)}^{2}}\).

The analytical equation of the follower chain joint 11 is

$${\theta }_{11}={\theta }_{9}+{\theta }_{10}-{\text{atan}}2\left({m}_{5},{n}_{5}\right)$$
(23)

where \({m}_{5}={o}_{x}{c}_{8}{c}_{13}+{o}_{y}{s}_{8}{c}_{13}+{n}_{x}{c}_{8}{s}_{13}+{n}_{y}{s}_{8}{s}_{13}\) and \({n}_{5}={o}_{z}{c}_{13}+{n}_{z}{s}_{13}\).

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Zhou, Z., Zhao, J., Zhang, Z. et al. Motion Planning Method of Redundant Dual-Chain Manipulator with Multiple Constraints. J Intell Robot Syst 108, 69 (2023). https://doi.org/10.1007/s10846-023-01921-3

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