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Journal of Advanced Transportation
Volume 2017, Article ID 6716820, 11 pages
https://doi.org/10.1155/2017/6716820
Research Article

Reactive Path Planning Approach for Docking Robots in Unknown Environment

School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China

Correspondence should be addressed to Weisheng Yan; nc.ude.upwn@naysw

Received 13 May 2017; Accepted 6 July 2017; Published 12 September 2017

Academic Editor: Cheng S. Chin

Copyright © 2017 Peng Cui et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. D. Floreano and R. J. Wood, “Science, technology and the future of small autonomous drones,” Nature, vol. 521, no. 7553, pp. 460–466, 2015. View at Publisher · View at Google Scholar · View at Scopus
  2. Z. Li, S. Zhao, J. Duan, C. Su, C. Yang, and X. Zhao, “Human cooperative wheelchair with brain machine interaction based on shared control strategy,” IEEE/ASME Transactions on Mechatronics, vol. 22, no. 1, pp. 185–195, 2017. View at Publisher · View at Google Scholar
  3. C. Chin and M. Lau, “Modeling and testing of hydrodynamic damping model for a complex-shaped remotely-operated vehicle for control,” Journal of Marine Science and Application, vol. 11, no. 2, pp. 150–163, 2012. View at Publisher · View at Google Scholar · View at Scopus
  4. R. Cui, C. Yang, Y. Li, and S. Sharma, “Adaptive Neural Network Control of AUVs With Control Input Nonlinearities Using Reinforcement Learning,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 47, no. 6, pp. 1019–1029, 2017. View at Publisher · View at Google Scholar
  5. J. Petit and S. E. Shladover, “Potential Cyberattacks on Automated Vehicles,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 2, pp. 546–556, 2015. View at Publisher · View at Google Scholar · View at Scopus
  6. C. S. Chin and S. H. Lum, “Rapid modeling and control systems prototyping of a marine robotic vehicle with model uncertainties using xPC Target system,” Ocean Engineering, vol. 38, no. 17-18, pp. 2128–2141, 2011. View at Publisher · View at Google Scholar · View at Scopus
  7. A. Flores-Abad, O. Ma, K. Pham, and S. Ulrich, “A review of space robotics technologies for on-orbit servicing,” Progress in Aerospace Sciences, vol. 68, pp. 1–26, 2014. View at Publisher · View at Google Scholar · View at Scopus
  8. C. W. Bac, E. J. Van Henten, J. Hemming, and Y. Edan, “Harvesting Robots for High-value Crops: State-of-the-art Review and Challenges Ahead,” Journal of Field Robotics, vol. 31, no. 6, pp. 888–911, 2014. View at Publisher · View at Google Scholar · View at Scopus
  9. D. Verstraete, A. Gong, D. D.-C. Lu, and J. L. Palmer, “Experimental investigation of the role of the battery in the AeroStack hybrid, fuel-cell-based propulsion system for small unmanned aircraft systems,” International Journal of Hydrogen Energy, vol. 40, no. 3, pp. 1598–1606, 2015. View at Publisher · View at Google Scholar · View at Scopus
  10. L. Wu, Y. Li, S. Su, P. Yan, and Y. Qin, “Hydrodynamic analysis of AUV underwater docking with a cone-shaped dock under ocean currents,” Ocean Engineering, vol. 85, pp. 110–126, 2014. View at Publisher · View at Google Scholar · View at Scopus
  11. N. Mathew, S. L. Smith, and S. L. Waslander, “Multirobot rendezvous planning for recharging in persistent tasks,” IEEE Transactions on Robotics, vol. 31, no. 1, pp. 128–142, 2015. View at Publisher · View at Google Scholar · View at Scopus
  12. Z. Yan, C. Deng, D. Chi, T. Chen, and S. Hou, “Path planning method for UUV homing and docking in movement disorders environment,” Scientific World Journal, vol. 2014, Article ID 246469, 2014. View at Publisher · View at Google Scholar · View at Scopus
  13. K. Teo, E. An, and P.-P. J. Beaujean, “A robust fuzzy autonomous underwater vehicle (AUV) docking approach for unknown current disturbances,” IEEE Journal of Oceanic Engineering, vol. 37, no. 2, pp. 143–155, 2012. View at Publisher · View at Google Scholar · View at Scopus
  14. D. Herrero, J. Villagr, and H. Martínez, “Self-configuration of waypoints for docking maneuvers of flexible automated guided vehicles,” IEEE Transactions on Automation Science Engineering, vol. 10, no. 2, pp. 470–475, 2013. View at Publisher · View at Google Scholar
  15. C. Yang, Z. Li, R. Cui, and B. Xu, “Neural network-based motion control of an underactuated wheeled inverted pendulum model,” IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 11, pp. 2004–2016, 2014. View at Publisher · View at Google Scholar · View at Scopus
  16. J. Zhang, S. Zhao, Y. Zhang, and Y. Li, “Optimal planning approaches with multiple impulses for rendezvous based on hybrid genetic algorithm and control method,” Advances in Mechanical Engineering, vol. 7, no. 3, pp. 1–11, 2015. View at Publisher · View at Google Scholar · View at Scopus
  17. D. Zhu, W. Li, M. Yan, and S. X. Yang, “The path planning of AUV based on D-S information fusion map building and bio-inspired neural network in unknown dynamic environment,” International Journal of Advanced Robotic Systems, vol. 11, no. 1, pp. 415–429, 2014. View at Publisher · View at Google Scholar · View at Scopus
  18. O. Motlagh, D. Nakhaeinia, S. H. Tang, B. Karasfi, and W. Khaksar, “Automatic navigation of mobile robots in unknown environments,” Neural Computing and Applications, vol. 24, no. 7-8, pp. 1569–1581, 2014. View at Publisher · View at Google Scholar · View at Scopus
  19. R. Cui, Y. Li, and W. Yan, “Mutual information-based multi-AUV path planning for scalar field sampling using multidimensional RRT,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 46, no. 7, pp. 993–1004, 2016. View at Publisher · View at Google Scholar
  20. B. Kovács, G. Szayer, F. Tajti, M. Burdelis, and P. Korondi, “A novel potential field method for path planning of mobile robots by adapting animal motion attributes,” Robotics and Autonomous Systems, vol. 82, pp. 24–34, 2016. View at Publisher · View at Google Scholar · View at Scopus
  21. Q. Huang and G. Zheng, “Route Optimization for Autonomous Container Truck Based on Rolling Window,” International Journal of Advanced Robotic Systems, vol. 13, no. 3, Article ID 64116, 2016. View at Publisher · View at Google Scholar · View at Scopus
  22. Á. V. F. M. De Oliveira and M. A. C. Fernandes, “Dynamic planning navigation strategy for mobile terrestrial robots,” Robotica, vol. 34, no. 3, pp. 568–583, 2016. View at Publisher · View at Google Scholar · View at Scopus
  23. M. Hoy, A. S. Matveev, and A. V. Savkin, “Algorithms for collision-free navigation of mobile robots in complex cluttered environments: A survey,” Robotica, vol. 33, no. 3, pp. 463–497, 2015. View at Publisher · View at Google Scholar · View at Scopus
  24. A. M. Shkel and V. Lumelsky, “Classification of the Dubins set,” Robotics and Autonomous Systems, vol. 34, no. 4, pp. 179–202, 2001. View at Publisher · View at Google Scholar · View at Scopus
  25. R. G. Sanfelice, S. Z. Yong, and E. Frazzoli, “On minimum-time paths of bounded curvature with position-dependent constraints,” Automatica. A Journal of IFAC, the International Federation of Automatic Control, vol. 50, no. 2, pp. 537–546, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  26. M. Shanmugavel, A. Tsourdos, B. White, and R. Zbikowski, “Co-operative path planning of multiple UAVs using Dubins paths with clothoid arcs,” Control Engineering Practice, vol. 18, no. 9, pp. 1084–1092, 2010. View at Publisher · View at Google Scholar · View at Scopus
  27. Y. Lin and S. Saripalli, “Path planning using 3D dubins curve for unmanned aerial vehicles,” in Proceedings of the 2014 International Conference on Unmanned Aircraft Systems, ICUAS 2014, pp. 296–304, Orlando, FL, USA, May 2014. View at Publisher · View at Google Scholar · View at Scopus
  28. O. Cetin, I. Zagli, and G. Yilmaz, “Establishing obstacle and collision free communication relay for UAVs with artificial potential fields,” Journal of Intelligent and Robotic Systems: Theory and Applications, vol. 69, no. 1-4, pp. 361–372, 2013. View at Publisher · View at Google Scholar · View at Scopus
  29. A. Melingui, R. Merzouki, J. B. Mbede, and T. Chettibi, “A novel approach to integrate artificial potential field and fuzzy logic into a common framework for robots autonomous navigation,” Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering, vol. 228, no. 10, pp. 787–801, 2014. View at Publisher · View at Google Scholar · View at Scopus
  30. O. Montiel, R. Sep, U. Orozco-Rosas, and R. Sepúlveda, “Optimal path planning generation for mobile robots using parallel evolutionary artificial potential field,” Journal of Intelligent Robotic Systems, vol. 79, no. 2, p. 21, 2015. View at Publisher · View at Google Scholar
  31. H. Min, Y. Lin, S. Wang, F. Wu, and X. Shen, “Path planning of mobile robot by mixing experience with modified artificial potential field method,” Advances in Mechanical Engineering, vol. 7, no. 11, Article ID 1687814015619276, 2015. View at Publisher · View at Google Scholar · View at Scopus
  32. M. Shanmugavel, A. Tsourdos, R. Zbikowski, and B. A. White, “3D dubins sets based coordinated path planning for swarm of UAVs,” in Proceedings of the AIAA Guidance, Navigation, and Control Conference 2006, pp. 1418–1437, August 2006.
  33. Y. Wang, S. Wang, M. Tan, C. Zhou, and Q. Wei, “Real-time dynamic Dubins-Helix method for 3-D trajectory smoothing,” IEEE Transactions on Control Systems Technology, vol. 23, no. 2, pp. 730–736, 2015. View at Publisher · View at Google Scholar · View at Scopus
  34. T. M. Howard, “Model-predictive motion planning: Several key developments for autonomous mobile robots,” IEEE Robotics Automation Magazine, vol. 21, no. 21, pp. 64–73, 2014. View at Publisher · View at Google Scholar
  35. Y. Lu, Z. Xi, and J.-M. Lien, “Online collision prediction among 2D polygonal and articulated obstacles,” International Journal of Robotics Research, vol. 35, no. 5, pp. 476–500, 2015. View at Publisher · View at Google Scholar · View at Scopus
  36. P. Mobadersany, S. Khanmohammadi, and S. Ghaemi, “A fuzzy multi-stage path-planning method for a robot in a dynamic environment with unknown moving obstacles,” Robotica, vol. 33, no. 9, pp. 1869–1885, 2015. View at Publisher · View at Google Scholar · View at Scopus
  37. J. Peng, W. Luo, W. Liu, W. Yu, and J. Wang, “A suboptimal and analytical solution to mobile robot trajectory generation amidst moving obstacles,” Autonomous Robots, vol. 39, no. 1, 2015. View at Publisher · View at Google Scholar · View at Scopus
  38. M. Otte and E. Frazzoli, “RRTX: Asymptotically optimal single-query sampling-based motion planning with quick replanning,” International Journal of Robotics Research, vol. 35, no. 7, pp. 797–822, 2016. View at Publisher · View at Google Scholar · View at Scopus
  39. B. H. Lee, J. D. Jeon, and J. H. Oh, “Velocity obstacle based local collision avoidance for a holonomic elliptic robot,” Autonomous Robots, pp. 1–17, 2016. View at Publisher · View at Google Scholar · View at Scopus
  40. Y.-b. Chen, G.-c. Luo, Y.-s. Mei, J.-q. Yu, and X.-l. Su, “UAV path planning using artificial potential field method updated by optimal control theory,” International Journal of Systems Science. Principles and Applications of Systems and Integration, vol. 47, no. 6, pp. 1407–1420, 2016. View at Publisher · View at Google Scholar · View at MathSciNet
  41. S. S. Ge and Y. J. Cui, “New potential functions for mobile robot path planning,” IEEE Transactions on Robotics and Automation, vol. 16, no. 5, pp. 615–620, 2000. View at Publisher · View at Google Scholar · View at Scopus
  42. U. Orozco-Rosas, O. Montiel, and R. Sepúlveda, “Pseudo-bacterial potential field based path planner for autonomous mobile robot navigation,” International Journal of Advanced Robotic Systems, vol. 12, 2015. View at Publisher · View at Google Scholar · View at Scopus