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 industrial robot trajectories, International Journal of Robotics and Automation, 31(1), 2016, 1-7.
  2. [2] 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.
  3. [3] J. Wu, G. Yu, Y. Gao, and L. Wang, Mechatronics modeling and vibration analysis of a 2-DOF parallel manipulator in a 5-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 a planar 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 with unforeseen changes, IEEE Transactions on Robotics, 24(5), 2004, 1199-1212.
  6. [6] 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.
  7. [7] J. Wu, J. Wang, and Z. You, An overview of dynamic parameter identification of robots,Robotics and Computer-Integrated Manufacturing, 26(5), 2010, 414-419.
  8. [8] P. Chen, C. Shan, J. Xiang, and W. Wei, Moving obstacle avoidance for redundant manipulator via weighted least norm method, The 27th Chinese Control and Decision Conference (2015 CCDC). Pages: 6181 - 6186.
  9. [9] M. Cefalo and G. Oriolo, Dynamically Feasible Task-Constrained Motion Planning with Moving Obstacles, 2014 IEEE International Conference on Robotics & Automation (ICRA), May 31-June 7, 2014. Hong Kong, China, pp. 2045-2050.
  10. [10] Z. Mohamed, M. Kitani, and G. Capi, Adaptive arm motion generation of humanoid robot operating in dynamic environments, Industrial Robot, 41(2), 2014, 124-134.
  11. [11] M. Mediavilla, J. R. Perán, and L. J. Miguel, On-line path planning for robot manipulators in dynamic environments, 2001 European Control Conference (ECC) Porto, Portugal, 4-7 September, 2001. Pages: 1169 – 1173.
  12. [12] H. Deng, Z. Xia, and J. Xiong, Robotic Manipulation Planning Using Dynamic RRT [C], 2016 IEEE International Conference on Real-time Computing and Robotics (RCAR). Pages: 500 – 504.
  13. [13] S. M. Lavalle and J. Kuffner, Rapidly-exploring random trees: Progress and prospects, In Algorithmic and Computational Robotics: New Directions, pages 293-308, 2000.
  14. [14] P. D. H. Nguyen, M. Hoffmann, U. Pattacini, and G. Metta, A fast heuristic Cartesian space motion planning algorithm for many-DOF robotic manipulators in dynamic environments, 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids) Cancun, Mexico, Nov 15-17, 2016. Pages: 884 - 891.
  15. [15] Z. Yan, B. Hao, W. Zhang, and S. X. Yang, Dubins-RRT path planning and heading-vectorcontrol guidance for a UUV recovery, International Journal of Robotics and Automation, 31(3), 2016, 251-262.
  16. [16] Z. Qu and J. Wang, A New Analytical Solution to Mobile Robot Trajectory 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-free trajectories in unpredictable environments in real-time, Autonomous Robots, 37(1), 2014, 81-96
  18. [18] H. Yu and T. Su, Destination Driven Motion Planning via Obstacle Motion Prediction and Multi-State Path Repair, Journal of Intelligent and Robotic Systems, 36(2), 2003, 149-173
  19. [19] J. Park, J. S. Choi, J. Kin, Lee, and B. H. Lee, Moving obstacle avoidance for a mobile robot [C], 2009 IEEE International Conference on Control and Automation, Christchurch, New Zealand, December 9-11, 2009. pp. 367-372.
  20. [20] T. Mercy, W. V. Loock, and G. Pipeleers, Real-time motion planning in the presence of moving obstacles, 2016 European Control Conference (ECC), June 29-July 1, 2016. Aalborg, Denmark, pp. 1586-1591.
  21. [21] H. Ishihara and E. Hashimoto, Moving Obstacle Avoidance for the Mobile Robot using the Probabilistic Inference, Proceedings of 2013 IEEE International Conference on Mechatronics and Automation, August 4 - 7, Takamatsu, Japan, pp.1771-1776.
  22. [22] T. A. V. Teatro, J. M. Eklund, and R. Milman, Nonlinear Model Predictive Control for Omnidirectional Robot Motion Planning and Tracking With Avoidance of Moving Obstacles, Canadian Journal of Electrical and Computer Engineering, 37(3), 2014, 151-156.
  23. [23] C. Liu and Y. Wang, Dynamic Multi-objective Optimization Evolutionary Algorithm, Third International Conference on Natural Computation (ICNC 2007), Year: 2007, Volume: 4, Pages:456-459.
  24. [24] A. K. M. K. A. Talukder and M. Kirley, A Pareto following variation operator for evolutionary dynamic multi-objective optimization, 2008 IEEE Congress on Evolutionary Computation, Year: 2008, Pages: 2270-2277.
  25. [25] R. Liu, X. Niu, J. Fan, C. Mu, and L. Jiao, An orthogonal predictive model-based dynamic multi-objective optimization algorithm, Soft Computing, 19(11), 2014, 3083-3107.
  26. [26] M. Rong, D. Gong, and Y. Zhang, A Multi-direction Prediction Approach for Dynamic Multi-objective Optimization, Lecture Notes in Computer Science, vol. 9773, 2016, pp. 629-636.
  27. [27] K. Deb, U. B. Rao N., and S. Karthik, Dynamic Multi-objective Optimization and Decision-Making Using Modified NSGA-II: A Case Study on Hydro-thermal Power Scheduling, Lecture Notes in Computer Science, vol. 4403 LNCS, 2007, pp. 803-817.
  28. [28] W. Wang, Y. Du, Q. Li and Z. Fang, Chaotic GEP Algorithm for Dynamic Multi-objective Optimization, 2011 Seventh International Conference on Natural Computation, ICNC 2011, vol. 2, 2011, pp. 1067-1071.
  29. [29] A. Zhou, Y. Jin, Q. Zhang, B. Sendhoff, and E. Tsang, Prediction-Based Population Re-initialization for Evolutionary Dynamic Multi-objective Optimization, Lecture Notes in Computer Science, v 4403 LNCS, 2007, p 832-846.
  30. [30] S. Sahmoud and H. R. Topcuoglu, A Memory-Based NSGA-II Algorithm for Dynamic Multi-objective Optimization Problems, Applications of Evolutionary Computation, Part II, LNCS 9598, 2016, pp. 296-310.
  31. [31] A. H. Beg and M. Z. Islam, Clustering by Genetic Algorithm- High Quality Chromosome Selection for Initial Population, 2015 IEEE 10th Conference on Industrial Electronics andApplications (ICIEA), Year: 2015, pp. 129-134.
  32. [32] D. He, H. Chang, Q. Chang, and Y. Liu, Particle Swarm Optimization Based on the Initial Population of Clustering, 2010 Sixth International Conference on Natural Computation(ICNC 2010), vol. 5, pp. 2664-2667.
  33. [33] N. Geng, X. Sun, D. Gong, and Y. Zhang, Solving robot path planning in an environment with terrains based on interval multi-objective PSO, International Journal of Robotics and Automation, 31(2), 2016, 100-110.
  34. [34] T. Hayashida, I. Nishizaki, S. Sekizaki, and S. Koto, Distance-based Clustering of Population and Intergroup Cooperative Particle Swarm Optimization, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Year: 2016, pp: 001359-001364.
  35. [35] M. A. Lones and A. M. Tyrrell, Regulatory Motif Discovery Using a Population Clustering Evolutionary Algorithm, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 4(3), 2007, 403-414.
  36. [36] M. Helbig and A. P. Engelbrecht, Issues with performance measures for dynamic multi-objective optimization, 2013 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), Year: 2013, Pages: 17-24.
  37. [37] M. C mara, J. Ortega, and F. de Toro, Performance Measures for Dynamic Multi-Objective Optimization, Lecture Notes in Computer Science, v 5517 LNCS, 2009, 760-767.
  38. [38] 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.
  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 metrics and particle swarm methods for dynamic multi-objective optimization problems, In: Proc. of the 2007 Congress on Evolutionary 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, IEEE Transactions on Evolutionary Computation, 12(4), 2008, 439-457.

Important Links:

Go Back