Research on motion planning for an indoor spray arm based on an improved potential field method

English version

Autoři: Dongjie Zhao ^aff001; Bin Zhang ^aff002; Ying Zhao ^aff001; Qun Sun ^aff001; Chuanjun Li ^aff002; Chong Wang ^aff001
Působiště autorů: School of Mechanical & Automotive Engineering, Liaocheng University, Liaocheng, China ^aff001; College of Engineering, China Agricultural University, Beijing, China ^aff002
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0226912

Souhrn

The target spraying effect of spray robots mainly depends on the control performance of the spraying arm during the processes of aiming and tracking. To further improve the robustness of the endpoint control and positioning accuracy of the spray arm, an improved potential field algorithm for the motion planning and control of the spray arm is proposed based on prophase research. The algorithm introduces a velocity potential field, visual field constraints and joint position limit constraints into the traditional artificial potential field method. The velocity potential field is used to ensure that the target state of the spraying arm is at the same velocity as the target crop (relative velocity) to achieve stable target tracking. The visual field constraints and joint position limit constraints are utilized to ensure the efficiency of the visual servo control and the movement of the spray arm. The algorithm can plan a feasible trajectory for the spraying arm in Cartesian space and image space, and use the speed controller to control the spraying arm movement along the trajectory for aiming and tracking. Simulation analysis shows that the algorithm can plan better motion trajectories than the servo controller based on image moments in previous studies. In addition, the experimental results show that the algorithm can effectively improve the robustness of targeting and tracking control for the spray robot.

Klíčová slova:

Simulation and modeling – Imaging techniques – Cameras – Crops – Image processing – Target detection – Robots – Prototypes

Zdroje

1. Qiu BJ, Yan R, Ma J, Guan XP, Ou MX. Research progress analysis of variable rate sprayer technology. Transactions of the Chinese Society for Agricultural Machinery. 2015; 46(3):59–72. doi: 10.6041/j.issn.1000-1298.2015.03.009

2. Hong SW, Zhao LY, Zhu HP. CFD simulation of pesticide spray from air-assisted sprayers in an apple orchard: Tree deposition and off-target losses. Atmospheric Environment. 2018;175:109–119. doi: 10.1016/j.atmosenv.2017.12.001

3. Liu XM, Li Y, Li M, Yuan J, Fang QZ, Hou JL. Design and test of smart-targeting spraying system on boom Sprayer. Transactions of the Chinese Society for Agricultural Machinery. 2016. 47(3):37–44. doi: 10.6041/j.issn.1000-1298.2016.03.006

4. Garcia-Santillan I, Peluffo-Ordonez D, Caranqui V, Pusdá M, Garrido F, Granda P. Computer vision-based method for automatic detection of crop rows in potato fields. Advances in Intelligent Systems and Computing, 2018,721:355–366. doi: 10.1007/978-3-319-73450-7_34

5. Garcia-Santillan I, Montalvo M, Guerrero J, Pajares G. Automatic detection of curved and straight crop rows from images in maize fields. Biosystems Engineering. 2017;156:61–79. doi: 10.1016/j.biosystemseng.2017.01.013

6. Zhao DJ, Zhang B, Wang XL, Guo HH, Xu SB. Automatic target research of indoor spray robot based on image moments. Transactions of the Chinese Society for Agricultural Machinery. 2016;47(12): 22–29. doi: 10.6041/j.issn.1000-1298.2016.12.004

7. Chang WC, Shao CK. Hybrid eye-to-hand and eye-in-hand visual servoing for autonomous robotic manipulation. Sice Conference. IEEE. 2010; 415~422.

8. Hua CC, Wang YQ, Guan XP. Visual tracking control for an uncalibrated robot system with unknown camera parameters. Robotics and Computer-Integrated Manufacturing. 2014; 30(1): 19–24. doi: 10.1016/j.rcim.2013.06.002

9. Baizid K, Yousnadj A, Meddahi A, Chellali R, Iqbal J. Time scheduling and optimization of industrial robotized tasks based on genetic algorithms. Robotics and Computer-integrated Manufacturing, 2015, 34:140–150. doi: 10.1016/j.rcim.2014.12.003

10. Zhao SP, Zhu ZX, Luo JJ. Multitask-based trajectory planning for redundant space robotics using improved genetic algorithm. Applied Sciences, 2019,9(11):1–22. doi: 10.3390/app9112226

11. Ning Q, Tao GP, Chen BC, Lei YJ, Yan H, Zhao CP. Multi-UAVs trajectory and mission cooperative planning based on the Markov model. Physical communication, 2019,35:1–10. doi: 10.1016/j.phycom.2019.100717

12. Zhang Q, Wang L, Zhou DS. Trajectory planning of 7-DOF space manipulator for minimizing base disturbance. International Journal of Advanced Robotic System, 2016,13: 1–10. doi: 10.5772/62123

13. De Momi E, Kranendonk L, Valenti M, Enayati N, Ferrigno G. A neural network-based approach for trajectory planning in robot-human handover tasks. Frontiers in robotics AI, 2016,3:1–10. doi: 10.3389/frobt.2016.00034

14. Abe A.Trajectory planning for flexible Cartesian robot manipulator by using artificial neural network: numerical simulation and experimental verification. Robotica, 2011,29:797–804. doi: 10.1017/S0263574710000767

15. Xie L, Xue SF, Zhang JF, Zhang MY, Tian WL, Haugen S. A path planning approach based on multi-direction A* algorithm for ships navigating within wind farm waters. Ocean Engineering, 2019,184:311–322. doi: 10.1016/j.oceaneng.2019.04.055

16. Xu X, Hu Y, Zhai JM, Li LZ, Guo PS. A novel non-collision trajectory planning algorithm based on velocity potential field for robotic manipulator. Intermational Journal of Advanced Robotic Systems, 2018,15(4):1–10. doi: 10.1177/1729881418787075

17. Liu T, Gong GF, Yang HY, Chen YX, Zhu Y. Trajectory control of tunnel boring machine based on adaptive rectification trajectory planning and multi-cylinders coordinated control. International Journal of Precision Engineering and Manufacturing, 2019,20(10): 1721–1733. doi: 10.1007/s12541-019-00073-5

18. Yang CG, Li ZJ, Li J. Trajectory planning and optimized adaptive control for a class of wheeled inverted pendulum vehicle models. IEEE Transactions on Cybernetics, 2012,43(1):24–36. doi: 10.1109/TSMCB.2012.2198813 22695357

19. Wang MM, Luo JJ, Walter U. Trajectory planning of free-floating space robot using Particle Swarm Optimization (PSO). ACTA Astronautica, 2015,112: 77–88. doi: 10.1016/j.actaastro.2015.03.008

20. Chen CT, Liao TT. A hybrid strategy for the time- and energy-efficient trajectory planning of parallel platform manipulators. Robotics and Computer-integrated Manufacturing, 2011,27(1):72–81. doi: 10.1016/j.rcim.2010.06.012

21. Ameni A, Khaled N. An advanced potential field method proposed for mobile robot path planning. Transactions of the Institute of Measurement and Control. 2019; 11:3132–3144. doi: 10.1177/0142331218824393

22. Kumar PB, Rawat H, Parhi DR. Path planning of humanoids based on artificial potential field method in unknown environments [J]. Expert Systems,2018,36(2):1–11. doi: 10.1111/exsy.12360

23. Chen GL, Liu J. Mobile Robot Path Planning Using Ant Colony Algorithm and Improved Potential Field Method. Computational Intelligence and Neuroscience. 2019:1–10. doi: 10.1155/2019/1932812 31198416

24. Wang WR, Zhu MC, Wang XM, He S, He JP, Xu ZB. An improved artificial potential field method of trajectory planning and obstacle avoidance for redundant manipulators. International Journal of Advanced Robotic Systems. 2018;15(5):1–13. doi: 10.1177/1729881418799562

25. Li WH, Yang CG, Jiang YM, Liu XF. Motion Planning for Omnidirectional Wheeled Mobile Robot by Potential Field Method. Journal of Advanced Transportation. 2017;1–11. doi: 10.1155/2017/4961383

26. Bence Kovács, Géza Szayer, Tajti F, Burdelis M, Péter Korondi. A novel potential field method for path planning of mobile robots by adapting animal motion attributes. Robotics and Autonomous Systems. 2016;82:24–34. doi: 10.1016/j.robot.2016.04.007

27. Mezouar Y, Chaumette F. Path planning for robust image-based control. IEEE Transactions on Robotics and Automation. 2002; 18(4): 534–549. doi: 10.1109/TRA.2002.802218

28. Lu X, Liu JT, Li HF, Li Y, Sun L. Attack and defense planning for competitive networked robots based on artificial potential field method. ROBOT. 2013; 35(2): 218–226. doi: 10.3724/SP.J.1218.2013.00218

Research on motion planning for an indoor spray arm based on an improved potential field method

Souhrn

Klíčová slova:

Zdroje

PLOS One

Aktuální možnosti diagnostiky a léčby litiáz