MULTI-OBJECTIVE TRAJECTORY PLANNING OF ROBOT MANIPULATOR IN A MOVING OBSTACLE ENVIRONMENT

Ying Huang, Minrui Fei, and Wenju Zhou

References

  1. [1] F. Liu and F. Lin, Time-jerk optimal planning of industrialrobot trajectories, International Journal of Robotics and Au-tomation, 31(1), 2016, 1–7.
  2. [2] L. Deng, X. Ma, J. Gu, Y. Li, Z. Xu, and Y. Wang, Artificialimmune network-based multi-robot formation path planningwith obstacle avoidance, International Journal of Robotics andAutomation, 31(3), 2016, 233–242.
  3. [3] J. Wu, G. Yu, Y. Gao, and L. Wang, Mechatronics modelingand vibration analysis of a 2-DOF parallel manipulator in a5-DOF hybrid machine tool, Mechanism and Machine Theory,121, 2018, 430–445.
  4. [4] J. Wu, J. Wang, L. Wang, and T. Li, Dynamics and control of aplanar 3-DOF parallel manipulator with actuation redundancy,Mechanism and Machine Theory, 44(4), 2009, 835–849.
  5. [5] J. Vannoy and J. Xiao, Real-time adaptive motion planning(RAMP) of mobile manipulators in dynamic environments withunforeseen changes, IEEE Transactions on Robotics, 24(5),2004, 1199–1212.
  6. [6] J. Ni, K. Wang, Q. Cao, Z. Khan, and X. Fan, A memeticalgorithm with variable length chromosome for robot pathplanning under dynamic environments, International Journalof Robotics and Automation, 32(4), 2017, 414–424.
  7. [7] J. Wu, J. Wang, and Z. You, An overview of dynamic parameteridentification of robots, Robotics and Computer-IntegratedManufacturing, 26(5), 2010, 414–419.
  8. [8] P. Chen, C. Shan, J. Xiang, and W. Wei, Moving obstacleavoidance for redundant manipulator via weighted least normmethod, The 27th Chinese Control and Decision Conference(2015 CCDC), Qingdao, China, 2015, 6181–6186.
  9. [9] M. Cefalo and G. Oriolo, Dynamically feasible task-constrainedmotion planning with moving obstacles, 2014 IEEE Interna-tional Conf. on Robotics & Automation (ICRA), Hong Kong,China, May 31–June 7, 2014, 2045–2050.
  10. [10] Z. Mohamed, M. Kitani, and G. Capi, Adaptive arm motiongeneration of humanoid robot operating in dynamic environ-ments, Industrial Robot, 41(2), 2014, 124–134.
  11. [11] M. Mediavilla, J.R. Per´an, and L.J. Miguel, On-line pathplanning for robot manipulators in dynamic environments,2001 European Control Conference (ECC) Porto, Portugal,4–7 September, 2001, 1169–1173.
  12. [12] H. Deng, Z. Xia, and J. Xiong, Robotic manipulation planningusing dynamic RRT, 2016 IEEE International Conf. on Real-time Computing and Robotics (RCAR), Siem Reap, Cambodia,2016, 500–504.
  13. [13] S.M. Lavalle and J. Kuffner, Rapidly-exploring random trees:Progress and prospects, in B.R. Donald, K.M. Lynch, D. Rus(eds.), Algorithmic and computational robotics: New directions,Wellesley, Massachusetts, 2000, 293–308.
  14. [14] P.D.H. Nguyen, M. Hoffmann, U. Pattacini, and G. Metta,A fast heuristic Cartesian space motion planning algorithmfor many-DOF robotic manipulators in dynamic environments,2016 IEEE-RAS 16th International Conf. on Humanoid Robots(Humanoids) Cancun, Mexico, Nov 15–17, 2016, 884–891.
  15. [15] Z. Yan, B. Hao, W. Zhang, and S.X. Yang, Dubins-RRTpath planning and heading-vector control guidance for a UUVrecovery, International Journal of Robotics and Automation,31(3), 2016, 251–262.
  16. [16] Z. Qu and J. Wang, A new analytical solution to mobile robottrajectory generation in the presence of moving obstacles,IEEE Transactions on Robotics, 20(6), 2004, 978–993.
  17. [17] R. Vatcha and J. Xiao, Detection of robustly collision-freetrajectories in unpredictable environments in real-time, Au-tonomous Robots, 37(1), 2014, 81–96.
  18. [18] H. Yu and T. Su, Destination driven motion planning viaobstacle motion prediction and multi-state path repair, Journalof Intelligent and Robotic Systems, 36(2), 2003, 149–173.
  19. [19] J. Park, J.S. Choi, J. Kin, Lee, and B.H. Lee, Moving ob-stacle avoidance for a mobile robot, 2009 IEEE InternationalConf. on Control and Automation, Christchurch, New Zealand,December 9–11, 2009, 367–372.
  20. [20] T. Mercy, W.V. Loock, and G. Pipeleers, Real-time motionplanning in the presence of moving obstacles, 2016 EuropeanControl Conference (ECC), Aalborg, Denmark, June 29–July1, 2016, 1586–1591.
  21. [21] H. Ishihara and E. Hashimoto, Moving obstacle avoidancefor the mobile robot using the probabilistic inference, Pro-ceedings of 2013 IEEE International Conf. on Mechatronicsand Automation, IEEE ICMA 2013, Takamatsu, Japan, 2013,1771–1776.
  22. [22] T.A.V. Teatro, J.M. Eklund, and R. Milman, Nonlinear modelpredictive control for omnidirectional robot motion planningand tracking with avoidance of moving obstacles, CanadianJournal of Electrical and Computer Engineering, 37(3), 2014,151–156.
  23. [23] C. Liu and Y. Wang, Dynamic multi-objective optimizationevolutionary algorithm, Third International Conf. on NaturalComputation (ICNC 2007), Haikou, China, 4, 2007, 456–459.
  24. [24] A.K.M.K.A. Talukder and M. Kirley, A Pareto following vari-ation operator for evolutionary dynamic multi-objective opti-mization, 2008 IEEE Congress on Evolutionary Computation,Hong Kong, China, 2008, 2270–2277.
  25. [25] R. Liu, X. Niu, J. Fan, C. Mu, and L. Jiao, An orthogonalpredictive model-based dynamic multi-objective optimizationalgorithm, Soft Computing, 19(11), 2014, 3083–3107.
  26. [26] M. Rong, D. Gong, and Y. Zhang, A multi-direction predictionapproach for dynamic multi-objective optimization, LectureNotes in Computer Science, 9773, 2016, 629–636.
  27. [27] K. Deb, U.B. Rao N., and S. Karthik, Dynamic multi-objectiveoptimization and decision-making using modified NSGA-II:A case study on hydro-thermal power scheduling, Lecture Notesin Computer Science, 4403 LNCS, 2007, 803–817.
  28. [28] W. Wang, Y. Du, Q. Li, and Z. Fang, Chaotic GEP algorithmfor dynamic multi-objective optimization, 2011 Seventh Inter-national Conf. on Natural Computation, ICNC 2011, Shanghai,China, 2, 2011, 1067–1071.
  29. [29] A. Zhou, Y. Jin, Q. Zhang, B. Sendhoff, and E. Tsang,Prediction-based population re-initialization for evolutionarydynamic multi-objective optimization, Lecture Notes in Com-puter Science, 4403 LNCS, 2007, 832–846.
  30. [30] S. Sahmoud and H.R. Topcuoglu, A memory-based NSGA-IIalgorithm for dynamic multi-objective optimization problems,Applications of Evolutionary Computation, Part II, LNCS9598, 2016, 296–310.
  31. [31] A.H. Beg and M.Z. Islam, Clustering by genetic algorithm-high quality chromosome selection for initial population, 2015IEEE 10th Conf. on Industrial Electronics and Applications(ICIEA), Auckland, New Zealand, 2015, 129–134.
  32. [32] D. He, H. Chang, Q. Chang, and Y. Liu, Particle swarmoptimization based on the initial population of clustering, 2010Sixth International Conf. on Natural Computation (ICNC2010), Yantai, China, 5, 2010, 2664–2667.
  33. [33] N. Geng, X. Sun, D. Gong, and Y. Zhang, Solving robot pathplanning in an environment with terrains based on intervalmulti-objective PSO, International Journal of Robotics andAutomation, 31(2), 2016, 100–110.
  34. [34] T. Hayashida, I. Nishizaki, S. Sekizaki, and S. Koto, Distance-based clustering of population and intergroup cooperativeparticle swarm optimization, 2016 IEEE International Conf.on Systems, Man, and Cybernetics (SMC), Budapest, Hungary,2016, 1359–1364.
  35. [35] M.A. Lones and A.M. Tyrrell, Regulatory motif discovery usinga population clustering evolutionary algorithm, IEEE/ACMTransactions on Computational Biology and Bioinformatics,4(3), 2007, 403–414.
  36. [36] M. Helbig and A.P. Engelbrecht, Issues with performancemeasures for dynamic multi-objective optimization, 2013 IEEESymposium on Computational Intelligence in Dynamic andUncertain Environments (CIDUE), Singapore, 2013, 17–24.
  37. [37] M. C´amara, J. Ortega, and F. de Toro, Performance measuresfor dynamic multi-objective optimization, Lecture Notes inComputer Science, 5517 LNCS, 2009, 760–767.
  38. [38] B.I. Kazem, A.I. Mahdi, and A.T. Oudah, Motion planningfor a robot arm by using genetic algorithm, Jordan Journal ofMechanical and Industrial Engineering, 2(3), 2008, 131–136.
  39. [39] Q. Zhang, A. Zhou, and Y. Jin, RM-MEDA: A regularity model-based multi-objective estimation of distribution algorithm,IEEE Transactions on Evolutionary Computation, 12(1), 2008,41–63.
  40. [40] X. Li, J. Branke, and M. Kirley, On performance metricsand particle swarm methods for dynamic multi-objective opti-mization problems, Proc. of the 2007 Congress on Evolution-ary Computation (CEC 2007), Singapore: IEEE Press, 2007,576–583.
  41. [41] A.J. Nebro, F. Luna, E. Alba, and B. Dorronsoro, AbYSS:Adapting scatter search to multi-objective optimization, IEEETransactions on Evolutionary Computation, 12(4), 2008, 439–457.

Important Links:

Go Back