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Optimization-based motion planning of mobile manipulator with high degree of kinematic redundancy

  • Jianfeng Liao
  • Fanghao Huang
  • Zheng ChenEmail author
  • Bin Yao
Regular Paper
  • 56 Downloads

Abstract

With the integration of mobility and manipulation, mobile manipulator constructed by mobile platform and manipulator has become a potential solution for the fields of industrial manufacturing and services. One of the key issues in utilizing the mobile manipulator is the motion planning, i.e., finding a feasible and efficient trajectory for operation. Compared with the traditional motion planning methods on mobile robot and fixed manipulator, the motion planning of mobile manipulator is more challenging due to the high degree of kinematic redundancy from the coupling of mobile platform and manipulator. And the optimization problem becomes more complicated by the integration of the high degree of kinematic redundancy, obstacle avoidance, constraints and task requirements. To address the issue, the integrated kinematic model is proposed, including mobile platform, manipulator and their coupling property. Subsequently, the coordinated motion planning for mobile manipulator to generate a collision free trajectories from the initial state to target state is developed, where the trajectories of mobile platform and manipulator are planned simultaneously by optimization-based method. The proposed motion planning algorithm is implemented and tested in various environments. Simulation and experiment results demonstrate the good effectiveness of the proposed motion planning algorithm.

Keywords

Motion planning Mobile manipulator Optimization 

Notes

Acknowledgements

This work is supported by Natural Science Foundation of Zhejiang Province, China (No. LY19E050016), National Natural Science Foundation of China (No.51875508), Youth Funds of the State Key Laboratory of Fluid Power and Mechatronic Systems (Zhejiang University), and Science Fund for Creative Research Groups of National Natural Science Foundation of China (No.51821093).

Supplementary material

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References

  1. Chen, Z., Yao, B., Wang, Q.: Accurate motion control of linear motors with adaptive robust compensation of nonlinear electromagnetic field effect. IEEE/ASME Trans. Mechatron. 18(3), 1122 (2013)CrossRefGoogle Scholar
  2. Chen, Z., Yao, B., Wang, Q.: \(mu\)-synthesis-based adaptive robust control of linear motor driven stages with high-frequency dynamics: a case study. IEEE/ASME Trans. Mechatron. 20(3), 1482 (2015)CrossRefGoogle Scholar
  3. Chen, Z., Pan, Y.J., Gu, J.: Integrated adaptive robust control for multilateral teleoperation systems under arbitrary time delays. Int. J. Robust Nonlinear Control 26(12), 2708 (2016)MathSciNetCrossRefzbMATHGoogle Scholar
  4. Chitta, S., Jones, E.G., Ciocarlie, M., Hsiao, K.: Mobile manipulation in unstructured environments: perception, planning, and execution. IEEE Robot. Autom. Mag. 19(2), 58 (2012)CrossRefGoogle Scholar
  5. Cohen, B.J., Chitta, S., Likhachev, M.: Search-based planning for manipulation with motion primitives. In: 2010 IEEE international conference on robotics and automation (ICRA) (IEEE, 2010), pp. 2902–2908 (2010)Google Scholar
  6. Craig, J.J.: Introduction to robotics: mechanics and control, vol. 3. Pearson/Prentice Hall, Upper Saddle River (2005)Google Scholar
  7. D’Andrea, R.: Guest editorial: a revolution in the warehouse: a retrospective on kiva systems and the grand challenges ahead. IEEE Trans. Autom. Sci. Eng. 9(4), 638 (2012)CrossRefGoogle Scholar
  8. Hansen, E.A., Zhou, R.: Anytime heuristic search. J. Artif. Intell. Res. 28, 267 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 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 (1968)CrossRefGoogle Scholar
  10. He, W., Huang, H., Ge, S.S.: Adaptive neural network control of a robotic manipulator with time-varying output constraints. IEEE Trans. Cybern. 47(10), 3136 (2017)CrossRefGoogle Scholar
  11. Hsu, D., Latombe, J.C., Motwani, R.: Path planning in expansive configuration spaces. In: 1997 IEEE International Conference on Robotics and Automation, 1997. Proceedings., vol. 3 (IEEE, 1997), pp. 2719–2726 (1997)Google Scholar
  12. Huang, Q., Tanie, K., Sugano, S.: Coordinated motion planning for a mobile manipulator considering stability and manipulability. Int. J. Robot. Res. 19(8), 732 (2000)CrossRefGoogle Scholar
  13. Karaman, S., Frazzoli, E.: Sampling-based algorithms for optimal motion planning. Int. J. Robot. Res. 30(7), 846 (2011)CrossRefzbMATHGoogle Scholar
  14. Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. In: Autonomous robot vehicles (Springer, 1986), pp. 396–404 (1986)Google Scholar
  15. Kuffner, J.J., LaValle, S.M.: RRT-connect: An efficient approach to single-query path planning. In: IEEE International Conference on Robotics and Automation, 2000. Proceedings. ICRA’00. Vol. 2 (IEEE, 2000), pp. 995–1001 (2000)Google Scholar
  16. LaValle, S.M.: Planning Algorithms. Cambridge University Press, Cambridge (2006)CrossRefzbMATHGoogle Scholar
  17. LaValle, S.M., Kuffner Jr., J.J.: Randomized kinodynamic planning. Int. J. Robot. Res. 20(5), 378 (2001)CrossRefGoogle Scholar
  18. Li, C., Li, C., Chen, Z., Yao, B.: Advanced synchronization control of a dual-linear-motor-driven gantry with rotational dynamics. IEEE Trans. Ind. Electron. (2018)Google Scholar
  19. Li, Z., Ge, S.S., Adams, M., Wijesoma, W.S.: Adaptive robust output-feedback motion/force control of electrically driven nonholonomic mobile manipulators. IEEE Trans. Control Syst. Technol. 16(6), 1308 (2008)CrossRefGoogle Scholar
  20. Li, Z., Li, J., Kang, Y.: Adaptive robust coordinated control of multiple mobile manipulators interacting with rigid environments. Automatica 46(12), 2028 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  21. Liao, J., Chen, Z., Yao, B.: Model-based coordinated control of four-wheel independently driven skid steer mobile robot with wheel/ground interaction and wheel dynamics. IEEE Trans. Ind. Inf. (2018)Google Scholar
  22. Likhachev, M., Ferguson, D.I., Gordon, G.J., Stentz, A., Thrun, S.: Anytime dynamic a*: An anytime, replanning algorithm. ICAPS, 262–271 (2005)Google Scholar
  23. Likhachev, M., Gordon, G.J., Thrun, S.: ARA*: Anytime A* with provable bounds on sub-optimality. In: Advances in neural information processing systems, pp. 767–774 (2004)Google Scholar
  24. Lyu, L., Chen, Z., Yao, B.: Development of pump and valves combined hydraulic system for both high tracking precision and high energy efficiency. IEEE Trans. Ind. Electron. PP(99), 1 (2018)CrossRefGoogle Scholar
  25. Paden, B., Čáp, M., Yong, S.Z., Yershov, D., Frazzoli, E.: A survey of motion planning and control techniques for self-driving urban vehicles. IEEE Trans. Intell. Vehicles 1(1), 33 (2016)CrossRefGoogle Scholar
  26. Ratliff, N., Zucker, M., Bagnell, J.A., Srinivasa, S.: CHOMP: Gradient optimization techniques for efficient motion planning. In: IEEE International Conference on Robotics and Automation, 2009. ICRA’09 (IEEE, 2009), pp. 489–494 (2009)Google Scholar
  27. Roa, M.A., Berenson, D., Huang, W.: Mobile manipulation: toward smart manufacturing [tc spotlight]. IEEE Robot. Autom. Mag. 22(4), 14 (2015)CrossRefGoogle Scholar
  28. Salzman, O., Halperin, D.: Asymptotically near-optimal RRT for fast, high-quality motion planning. IEEE Trans. Robot. 32(3), 473 (2016)CrossRefGoogle Scholar
  29. Schulman, J., Ho, J., Lee, A.X., Awwal, I., Bradlow, H., Abbeel, P.: Finding locally optimal, collision-free trajectories with sequential convex optimization. In: Robotics: science and systems, vol. 9 (Citeseer, 2013), pp. 1–10 (2013)Google Scholar
  30. Schulman, J., Duan, Y., Ho, J., Lee, A., Awwal, I., Bradlow, H., Pan, J., Patil, S., Goldberg, K., Abbeel, P.: Motion planning with sequential convex optimization and convex collision checking. Int. J. Robot. Res. 33(9), 1251 (2014)CrossRefGoogle Scholar
  31. Shimoga, K.B.: Robot grasp synthesis algorithms: a survey. Int. J. Robot. Res. 15(3), 230 (1996)CrossRefGoogle Scholar
  32. Sun, W., Zhang, Y., Huang, Y., Gao, H., Kaynak, O.: Transient-performance-guaranteed robust adaptive control and its application to precision motion control systems. IEEE Trans. Ind. Electron. 63(10), 6510 (2016)CrossRefGoogle Scholar
  33. Sun, W., Tang, S., Gao, H., Zhao, J.: Two time-scale tracking control of nonholonomic wheeled mobile robots. IEEE Trans. Control Syst. Technol. 24(6), 2059 (2016)CrossRefGoogle Scholar
  34. Tang, C.P., Miller, P.T., Krovi, V.N., Ryu, J.C., Agrawal, S.K.: Differential-flatness-based planning and control of a wheeled mobile manipulator Theory and experiment. IEEE/ASME Trans. Mechatron. 16(4), 768 (2011)Google Scholar
  35. Urmson, C., Anhalt, J., Bagnell, D., Baker, C., Bittner, R., Clark, M., Dolan, J., Duggins, D., Galatali, T., Geyer, C., et al.: Autonomous driving in urban environments: boss and the urban challenge. J. Field Robot. 25(8), 425 (2008)CrossRefGoogle Scholar
  36. Vannoy, J., Xiao, J.: Robotics, Real-time adaptive motion planning (RAMP) of mobile manipulators in dynamic environments with unforeseen changes. IEEE Trans. 24(5), 1199 (2008)Google Scholar
  37. Wright, S., Nocedal, J.: Numerical optimization. Springer Sci. 35(67–68), 7 (1999)zbMATHGoogle Scholar
  38. Xia, K., Gao, H., Ding, L., Liu, G., Deng, Z., Liu, Z., Ma, C.: Trajectory tracking control of wheeled mobile manipulator based on fuzzy neural network and extended Kalman filtering. Neural Comput. Appl. 1–16 (2016)Google Scholar
  39. Yao, J., Deng, W.: Active disturbance rejection adaptive control of hydraulic servo systems. IEEE Trans. Ind. Electron. 64(10), 8023 (2017a)CrossRefGoogle Scholar
  40. Yao, J., Deng, W.: Active disturbance rejection adaptive control of uncertain nonlinear systems: theory and application. Nonlinear Dyn. 89(3), 1611 (2017b)MathSciNetCrossRefzbMATHGoogle Scholar
  41. Yuan, M., Chen, Z., Yao, B., Zhu, X.: Time optimal contouring control of industrial biaxial gantry: a highly efficient analytical solution of trajectory planning. IEEE/ASME Trans. Mechatron. 22(1), 247 (2017)CrossRefGoogle Scholar
  42. Zhang, Z., Zhang, Y.: Variable joint-velocity limits of redundant robot manipulators handled by quadratic programming. IEEE/ASME Trans. Mechatron. 18(2), 674 (2013)CrossRefGoogle Scholar
  43. Zhijun, L., Shuzhi, G., S, Adams, M., Wijesoma, W.: Robust adaptive control of uncertain force/motion constrained nonholonomic mobile manipulators. Automatica 44(3), 776 (2008)Google Scholar
  44. Zhou, K., Doyle, J.C., Glover, K., et al.: Robust and optimal control, vol. 40. Prentice hall, New Jersey (1996)zbMATHGoogle Scholar
  45. Zucker, M., Ratliff, N., Dragan, A.D., Pivtoraiko, M., Klingensmith, M., Dellin, C.M., Bagnell, J.A., Srinivasa, S.S.: Chomp: Covariant hamiltonian optimization for motion planning. Int. J. Robot. Res. 32(9–10), 1164 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Fluid Power and Mechatronic SystemsZhejiang UniversityHangzhouChina
  2. 2.Ocean CollegeZhejiang UniversityHangzhouChina
  3. 3.School of Mechanical EngineeringPurdue UniversityWest LafayetteUSA

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