Hostname: page-component-cd9895bd7-mkpzs Total loading time: 0 Render date: 2024-12-26T03:08:03.636Z Has data issue: false hasContentIssue false
  • English
  • Français

Global motion planning and redundancy resolution for large objects manipulation by dual redundant robots with closed kinematics

Published online by Cambridge University Press:  09 August 2021

Yongxiang Wu Open the ORCID record for Yongxiang Wu [Opens in a new window] ,
Yili Fu  and
Shuguo Wang
Yongxiang Wu
Affiliation:
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China
Yili Fu*
Affiliation:
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China
Shuguo Wang
Affiliation:
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China
*
*Corresponding author. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

The multi-arm robotic systems consisting of redundant robots are able to conduct more complex and coordinated tasks, such as manipulating large or heavy objects. The challenges of the motion planning and control for such systems mainly arise from the closed-chain constraint and redundancy resolution problem. The closed-chain constraint reduces the configuration space to lower-dimensional subsets, making it difficult for sampling feasible configurations and planning path connecting them. A global motion planner is proposed in this paper for the closed-chain systems, and motions in different disconnected manifolds are efficiently bridged by two type regrasping moves. The regrasping moves are automatically chosen by the planner based on cost-saving principle, which greatly improve the success rate and efficiency. Furthermore, to obtain the optional inverse kinematic solutions satisfying joint physical limits (e.g., joint position, velocity, acceleration limits) in the planning, the redundancy resolution problem for dual redundant robots is converted into a unified quadratic programming problem based on the combination of two diff erent-level optimizing criteria, i.e. the minimization velocity norm (MVN) and infinity norm torque-minimization (INTM). The Dual-MVN-INTM scheme guarantees smooth velocity, acceleration profiles, and zero final velocity at the end of motion. Finally, the planning results of three complex closed-chain manipulation task using two Franka Emika Panda robots and two Kinova Jaco2 robots in both simulation and experiment demonstrate the effectiveness and efficiency of the proposed method.

Keywords

closed kinematic chaindual arm coordinated manipulationmotion planningredundancy resolutionquadratic programming
Type
Research Article
Information
Robotica , Volume 40 , Issue 4 , April 2022 , pp. 1125 - 1150
Copyright
© The Author(s), 2021. 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

CortÉs, J. and SimÉon, T., “Sampling-Based Motion Planning under Kinematic Loop-Closure Constraints,” in Algorithmic Foundations of Robotics VI, Erdmann, M., Overmars, M., Hsu, D., and van der Stappen, F., Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005, pp. 75–90.CrossRefGoogle Scholar
Yakey, J. H., LaValle, S. M., and Kavraki, L. E., “Randomized path planning for linkages with closed kinematic chains,” IEEE Trans Rob Autom. 17, 951958 (2001).CrossRefGoogle Scholar
LaValle, S. M., Yakey, J. H. and Kavraki, L. E., “A probabilistic roadmap approach for systems with closed kinematic chains,” 1999 IEEE Int ConfRob Autom. pp. 1671–1676 (1999).Google Scholar
Berenson, D., Srinivasa, S. S., Ferguson, D. and Kuffner, J. J., “Manipulation planning on constraint manifolds,” 2009 IEEE Int. Conf Rob Autom. pp. 625–632 (2009).Google Scholar
Xie, D. and Amato, N. M., “A kinematics-based probabilistic roadmap method for high DOF closed chain systems,” 2004 IEEE Int Conf Rob Autom. pp. 473–478 (2004).Google Scholar
Cortes, J., Simeon, T. and Laumond, J. P., “A random loop generator for planning the motions of closed kinematic chains using PRM methods,” 2002 IEEE Int Conf Rob Autom. pp. 2141–2146 (2002).CrossRefGoogle Scholar
Jaillet, L. and Porta, J. M., “Path planning under kinematic constraints by rapidly exploring manifolds,” IEEE Trans Rob. 29 10–5117 (2013).CrossRefGoogle Scholar
Kim, B., Um, T. T., Suh, C. and Park, F. C., “Tangent bundle RRT: a randomized algorithm for constrained motion planning,” Robotica. 34, 202–225 (2016).CrossRefGoogle Scholar
Kingston, Z., Moll, M. and Kavraki, L. E., “Decoupling Constraints from Sampling-Based Planners,” In: Robotics Research (Springer International Publishing, 2020), pp. 913–928.CrossRefGoogle Scholar
Sintov, A., Borum, A. and Bretl, T., “Motion planning of fully actuated closed kinematic chains with revolute joints: a comparative analysis,” IEEE Rob Autom Lett. 3, 2886–2893 (2018).CrossRefGoogle Scholar
Xian, Z., Lertkultanon, P and Pham, Q., “Closed-chain manipulation of large objects by multi-arm robotic systems,” IEEE Rob Autom Lett. 2, 1832–1839 (2017).Google Scholar
Siciliano, B., Sciavicco, L., Villani, L. and Oriolo, G., eds., Differential Kinematics and Statics. Robotics: Modelling, Planning and Control (Springer, London, 2009), pp. 105–160.CrossRefGoogle Scholar
Hassan, A. A., El-Habrouk, M. and Deghedie, S., “Inverse kinematics of redundant manipulators formulated as quadratic programming optimization problem solved using recurrent neural networks: a review,” Robotica. 38, 1495–1512 (2020).CrossRefGoogle Scholar
Guo, D. and Y Zhang, “A new inequality-based obstacle-avoidance MVN scheme and its application to redundant robot manipulators,” IEEE Trans Syst Man Cybernetics Part C (Appl Rev). 42, 1326–1340 (2012).Google Scholar
Guo, D. and Y Zhang, “Different-level two-norm and infinity-norm minimization to remedy joint-torque instability/divergence for redundant robot manipulators,” Rob Autonom Syst. 60, 874–888 (2012).Google Scholar
Zhang, Y, Guo, D. and Ma, S., “Different-level simultaneous minimization of joint-velocity and joint-torque for redundant robot manipulators,” J. Intell Rob Syst. 72, 301–323 (2013).Google Scholar
Cai, B. and Y Zhang, “Equivalence of velocity-level and acceleration-level redundancy- resolution of manipulators,” Phys Lett. A 373, 3450–3453 (2009).Google Scholar
Liu, S. and Wang, J., “Bi-criteria torque optimization of redundant manipulators based on a simplified dual neural network,” 2005 IEEE Int Joint Conf Neural Networks. pp. 2796–2801 (2005).Google Scholar
Zhang, Y and Li, K., “Bi-criteria velocity minimization of robot manipulators using LVI-based primal-dual neural network and illustrated via PUMA560 robot arm,” Robotica. 28, 525–537 (2010).Google Scholar
O’Neil, K. A., “Divergence of linear acceleration-based redundancy resolution schemes,” IEEE Trans Rob Autom. 18, 625–631 (2002).CrossRefGoogle Scholar
Pedrammehr, S., Danaei, B., Abdi, H., Masouleh, M. and Nahavandi, S., “Dynamic analysis of Hexarot: axis-symmetric parallel manipulator,” Robotica. 36, 1–16 (2017).Google Scholar
Zhang, Z., Lin, Y, Li, S., Li, Y, Z. Yu and Y Luo, “Tricriteria optimization-coordination motion of dual-redundant-robot manipulators for complex path planning,” IEEE Trans Control Syst Technol. 26, 1345–1357 (2018).CrossRefGoogle Scholar
Jia, Z., Chen, S., Zhang, Z., Zhong, N., Zhang, P, Qu, X., J. Xie and F Ouyang, “Tri-criteria optimization motion planning at acceleration-level of dual redundant manipulators,” Robotica. 38, 983–999 (2020).CrossRefGoogle Scholar
Latombe, J.-C. & Hauser, K., Motion planning for legged and humanoid robots, Ph.D. dissertation, Stanford University, 2008.Google Scholar
Stilman, M., “Global manipulation planning in robot joint space with task constraints,” IEEE Trans Rob. 26, 576–584 (2010).CrossRefGoogle Scholar
Pham, Q. and Stasse, O., “Time-optimal path parameterization for redundantly actuated robots: a numerical integration approach,” IEEE/ASME Trans Mechatron. 20, 3257–3263 (2015).Google Scholar
Gharbi, M., Cortes, J. and Simeon, T., “A sampling-based path planner for dual-arm manipulation,” 2008 IEEE/ASME Int Conf Adv Intell Mechatron. pp. 383–388 (2008).CrossRefGoogle Scholar
Koga, Y. and Latombe, J.-C., “Experiments in dual-arm manipulation planning,” Proceedings 1992 IEEE International Conference on Robotics and Automation, Vol. 3, pp. 2238–2245 (1992).CrossRefGoogle Scholar
Diankov, R., Automated Construction of Robotic Manipulation Programs, Ph.D. dissertation, Carnegie Mellon University, 2010.Google Scholar
Preda, N., Manurung, A., Lambercy, O., Gassert, R. and BonfÈ, M., “Motion planning for a multi- arm surgical robot using both sampling-based algorithms and motion primitives,” 2015 IEEE/RSJ Int Conf Intell Rob Syst. pp. 1422–1427 (2015).Google Scholar
Xu, W, Y Liu and Y Xu, “The coordinated motion planning of a dual-arm space robot for target capturing,” Robotica. 30, 755–771 (2012).CrossRefGoogle Scholar
GarcÍa, N., Rosell, J. and SuÁrez, R., “Motion planning by demonstration with human-likeness evaluation for dual-arm robots,” IEEE Trans Syst Man Cybernetics: Syst. 49, 2298–2307 (2019).CrossRefGoogle Scholar
Szynkiewicz, W and Blaszczyk, J., “Optimization-based approach to path planning for closed chain robot systems,” International Journal of Applied Mathematics and Computer Science 21, 659-670 (2011).Google Scholar
VÖlz, A. and Graichen, K., “An optimization-based approach to dual-arm motion planning with closed kinematics,” 2018 IEEE/RSJ Int Conf Intell Rob Syst. pp. 8346–8351 (2018).Google Scholar
Stouraitis, T., Chatzinikolaidis, I., Gienger, M. and Vijayakumar, S., “Online hybrid motion planning for dyadic collaborative manipulation via bilevel optimization,” IEEE Trans Rob. 36, 1452–1471 (2020).CrossRefGoogle Scholar
Song, G., Su, S., Li, Y, Zhao, X., Du, H., J. Han and Y Zhao, “A closed-loop framework for the inverse kinematics of the 7 degrees of freedom manipulator,” Robotica. 39, 572–581 (2021).CrossRefGoogle Scholar
Choi, H.-B., Lee, S. and Lee, J., “Minimum infinity-norm joint velocity solutions for singularity- robust inverse kinematics,” Int J Precis. Eng Manuf. 12, 469–474 (2011).CrossRefGoogle Scholar
Guo, D. and Y Zhang, “Acceleration-level inequality-based MAN scheme for obstacle avoidance of redundant robot manipulators,” IEEE Trans Ind Electronics 61, 6903–6914 (2014).Google Scholar
Zhang, Y, Yin, J. and Cai, B., “Infinity-norm acceleration minimization of robotic redundant manipulators using the LVI-based primaldual neural network,” Rob. Comput Integr Manu. 25, 358–365 (2009).Google Scholar
Zhang, Y, “Inverse-free computation for infinity-norm torque minimization of robot manipulators,” Mechatronics. 16, 177–184 (2006).CrossRefGoogle Scholar
Zhang, Y, Cai, B., Zhang, L. and Li, K., “Bi-criteria velocity minimization of robot manipulators using a linear variational inequalities-based primal-dual neural network and PUMA560 example,” Adv. Rob. 22, 1479–1496 (2008).Google Scholar
Baratcart, T., Salvucci, V and Koseki, T., “Experimental verification of two-norm, infinity-norm continuous switching implemented in resolution of biarticular actuation redundancy” Adv. Rob. 29, 1243–1252 (2015).CrossRefGoogle Scholar
Liao, B. and W Liu, “Pseudoinverse-type bi-criteria minimization scheme for redundancy resolution of robot manipulators,” Robotica. 33, 2100–2113 (2015).Google Scholar
Escande, A., Mansard, N. and Wieber, P-B., “Hierarchical quadratic programming: fast online humanoid-robot motion generation,” Int JRob Res. 33, 1006–1028 (2014).CrossRefGoogle Scholar
Wang, J., Hu, Q. and Jiang, D., “A Lagrangian network for kinematic control of redundant robot manipulators,” IEEE Trans Neural Networks 10, 1123–1132 (1999).CrossRefGoogle ScholarPubMed
Tang, W. S. and Wang, J., “A recurrent neural network for minimum infinity-norm kinematic control of redundant manipulators with an improved problem formulation and reduced architecture complexity,” IEEE Trans Syst Man Cybernetics, Part B (Cybernetics) 31, 98–105 (2001).Google Scholar
Zhang, Y and Wang, J., “A dual neural network for constrained joint torque optimization of kinematically redundant manipulators,” IEEE Trans Syst Man Cybernetics Part B (Cybernetics) 32, 654–662 (2002).Google Scholar
Boyd, S., Vandenberghe, L. and Faybusovich, L., “Convex optimization,” IEEE Trans Autom. Control. 51, 1859–1859 (2006).CrossRefGoogle Scholar
Pan, J., Chitta, S. and Manocha, D., “FCL: a general purpose library for collision and proximity queries,” 2012 IEEE Int Conf Rob Autom. pp. 3859–3866 (2012).Google Scholar
Gaz, C., Cognetti, M., Oliva, A., Giordano, P. R. and Luca, A. D., “Dynamic identification of the Franka Emika Panda robot with retrieval of feasible parameters using penalty-based optimization,” IEEE Rob Autom Lett. 4, 4147–4154 (2019).CrossRefGoogle Scholar