ENHANCED MOEA/D FOR TRAJECTORY PLANNING IMPROVEMENT OF ROBOT MANIPULATOR

Ying Huang,∗,∗∗ Minrui Fei,∗ and Wenju Zhou∗∗

References

  1. [1] L. Liu, C. Chen, X. Zhao, and Y. Li, Smooth trajectory planning for a parallel manipulator with joint friction and jerk constraints, International Journal of Control, Automation and Systems, 14(4), 2016, 1022–1036.
  2. [2] B.I. Kazem, A.I. Mahdi, and A.T. Oudah, Motion planning for a robot arm by using genetic algorithm, Jordan Journal of Mechanical and Industrial Engineering, 2(3), 2008, 131–136.
  3. [3] X. Sheng, L. Xu, and Z. Wang, A position-based explicit force control strategy based on online trajectory prediction, International Journal of Robotics and Automation, 32(1), 2017, 93–100.
  4. [4] J. Kim and J. Lee, Trajectory optimization with particle swarm optimization for manipulator motion planning, IEEE Transactions on Industrial Informatics, 11(3), 2015, 620–631.
  5. [5] E.A. Padilla-Garcia, A. Rodriguez-Angeles, J.R. Reséndiz, and C.A. Cruz-Villar, Concurrent optimization for selection and control of AC servomotors on the powertrain of industrial robots, IEEE Access, 6, 2018, 27923–27938.
  6. [6] Z. Mohamed, M. Kitani, S. Kaneko, and G. Capi, Humanoid robot arm performance optimization using multi objective evolutionary algorithm, International Journal of Control, Automation, and Systems, 12(4), 2014, 870–877.
  7. [7] Y. Huang and M. Fei, Motion Planning of robot manipulator based on improved NSGA-II, International Journal of Control, Automation and Systems, 16(4), 2018, 1878–1886.
  8. [8] S. Thabit and A. Mohades, Multi-robot path planning based on multi-objective particle swarm optimization, IEEE Access, 7, 2019, 2138–2147.
  9. [9] J. Ni, K. Wang, Q. Cao, Z. Khan, and X. Fan, A memetic algorithm with variable length chromosome for robot path planning under dynamic environments, International Journal of Robotics and Automation, 32(4), 2017, 414–424.
  10. [10] X. You, S. Liu, and C. Zhang, An improved ant colony system algorithm for robot path planning and performance analysis, International Journal of Robotics and Automation, 33(5), 2018, 527–533.
  11. [11] C. Lin, H. Wang, J. Yuan, and M. Fu, An online path planning method based on hybrid quantum ant colony optimization for AUV, International Journal of Robotics and Automation, 33(4), 2018, 435–444.
  12. [12] L. Deng, X. Ma, J. Gu, Y. Li, Z. Xu, and Y. Wang, Artificial immune network-based multi-robot formation path planning with obstacle avoidance, International Journal of Robotics and Automation, 31(3), 2016, 233–242.
  13. [13] K. Lee, H. Myung, and J. Kim, Online multiobjective evolutionary approach for navigation of humanoid robots, IEEE Transactions on Industrial Electronics, 62(9), 2015, 5586–2297.
  14. [14] C. Chen and H. Pham, Trajectory planning in parallel kinematic manipulators using a constrained multi-objective evolutionary algorithm, Nonlinear Dynamics, 67, 2012, 1669–1681.
  15. [15] Q. Zhang, S. Member, and H. Li, MOEA/D: a multiobjective evolutionary algorithm based on decomposition, IEEE Transactions on Evolutionary Computation, 11(6), 2007, 712–731.
  16. [16] A. Trivedi, D. Srinivasan, K. Sanyal, and A. Ghosh, A survey of multiobjective evolutionary algorithms based on decomposition, IEEE Transactions on Evolutionary Computation, 21(3), 2017, 440–462.
  17. [17] J. Chen, J. Li, and B. Xin, DMOEA-εC: decomposition-based multiobjective evolutionary algorithm with the ε-constraint framework, IEEE Transactions on Evolutionary Computation, 21(5), 2017, 714–730.
  18. [18] Z. Zhou, X. Liu, H. Xiao, et al., A DEA-based MOEA/D algorithm for portfolio optimization, Cluster Computing, 22, 2019, 14477–14486.
  19. [19] K. Li, A. Fialho, S. Kwong, and Q. Zhang, Adaptive operator selection with bandits for a multiobjective evolutionary algorithm based on decomposition, IEEE Transactions on Evolutionary Computation, 18(1), 2014, 114–130. 101
  20. [20] C. Zhang, K.C. Tan, L.H. Lee, and L. Gao, Adjust weight vectors in MOEA/D for bi-objective optimization problems with discontinuous Pareto fronts, Soft Computing, 22, 2018, 3997–4012.
  21. [21] X. Ma, F. Liu, Y. Qi, et al., MOEA/D with biased weight adjustment inspired by user preference and its application on multi-objective reservoir flood control problem, Soft Computing, 20, 2016, 4999–5023.
  22. [22] W. Zheng, Y. Tan, L. Meng, and H. Zhang, An improved MOEA/D design for many-objective optimization problems, Applied Intelligence, 48, 2018, 3839–3861.
  23. [23] H. Sato, Chain-reaction solution update in MOEA/D and its effects on multiand many-objective optimization, Soft Computing, 20, 2016, 3803–3820.
  24. [24] R. Wang, Z. Zhou, H. Ishibuchi, T. Liao, and T. Zhang, Localized weighted sum method for many-objective optimization, IEEE Transactions on Evolutionary Computation, 22(1), 2018, 3–18.
  25. [25] S.X. Zhang, L.M. Zheng, L. Liu, S.Y. Zheng, and Y.M. Pan, Decomposition-based multi-objective evolutionary algorithm with mating neighborhood sizes and reproduction operators adaptation, Soft Computing, 21, 2017, 6381–6392.
  26. [26] R. Wang, Q. Zhang, and T. Zhang, Decomposition-based algorithms using Pareto adaptive scalarizing methods, IEEE Transactions on Evolutionary Computation, 20(6), 2016, 821– 837.
  27. [27] W. Ning, B. Guo, Y. Yan, and J. Hou, Distance-dependent parameter adaption for multi-objective evolutionary algorithm based on decomposition, Soft Computing, 22(20), 2018, 6845– 6859.
  28. [28] K. Michalak, Using an outward selective pressure for improving the search quality of the MOEA/D algorithm, Computational Optimization Applications, 61, 2015, 571–607.
  29. [29] H. Ishibuchi, K. Doi, and Y. Nojima, On the effect of normalization in MOEA/D for multi-objective and many-objective optimization, Complex Intelligent Systems, 3, 2017, 279–294.
  30. [30] A.G. Joseph and S. Bhatnagar, An incremental off-policy search in a model-free Markov decision process using a single sample path, Machine Learning, 107, 2018, 969–1011.
  31. [31] X. Yu, X. Zhou, and Y. Zhang, Collision-free trajectory generation and tracking for UAVs using Markov decision process in a cluttered environment, Journal of Intelligent & Robotic Systems, 93, 2019, 17–32.
  32. [32] L.R. Nielsen, E. Jørgensen, and S. Højsgaard, Embedding a state space model into a Markov decision process, Annals of Operations Research, 190, 2011, 289–309.
  33. [33] J. Hölz, Markov chains and Markov decision processes in Isabelle/HOL, Journal of Automated Reasoning, 59, 2017, 345–387.
  34. [34] M. Onderwater, S. Bhulai, and R.v.d. Mei, Value function discovery in Markov decision processes with evolutionary algorithms, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 46(9), 2016, 1190–1201.

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