Path planning of the fruit tree pruning manipulator based on improved RRT-Connect algorithm
Keywords:
manipulator, path planning, RRT-Connect, jujube pruning, obstacle avoidance, non-uniform B-splineAbstract
Aiming to realize the obstacle avoidance of the fruit tree pruning manipulator in unstructured complex natural environment, an improved bidirectional fast extended random tree (RRT-Connect) algorithm was presented in this study. The manipulator and obstacles were properly simplified based on their geometrical characteristics to build collision detection models taking account of the obstacles, ground, and manipulator itself and to carry out the obstacle avoidance path planning. Goal-biased strategy and adaptive step size adjustment principle were introduced to accelerate the path search speed. Bidirectional pruning optimal strategy and cubic non-uniform B-spline interpolation method were adopted to optimize the path generated by RRT-Connect. The simulation path planning experiment was carried out in the simulation system of the fruit tree pruning manipulator and the practical obstacle avoidance path planning experiment was carried out on the real fruit tree pruning manipulator path planning experiment platform. The results showed that the path planning time and the path length of the improved RRT-Connect algorithm reduced by about 55% and 60% respectively compared with the basic RRT-Connect algorithm. The path planning success rate of the improved RRT-Connect algorithm was 100%, and the planned path was smooth, continual and executable, which could effectively guide the manipulator to avoid obstacles and lead the end effector of the manipulator to the goal point. The proposed improved algorithm not only has certain application value for obstacle avoidance of the fruit tree pruning manipulator in fruit tree pruning environment, but also has theoretical reference value for path planning of other types of robots. Keywords: manipulator, path planning, RRT-Connect, jujube pruning, obstacle avoidance, non-uniform B-spline DOI: 10.25165/j.ijabe.20221502.6249 Citation: Chen Y Y, Fu Y X, Zhang B, Fu W, Shen C J. Path planning of the fruit tree pruning manipulator based on improved RRT-Connect algorithm. Int J Agric & Biol Eng, 2022; 15(2): 177–188.References
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[23] Liu H Y, Zhang X B, Wen J, Wang R H, Chen X. Goal-biased bidirectional RRT based on curve-smoothing. IFAC-PapersOnLine, 2019; 52(24): 255–260.
[24] Li Z Y, Zhao D J, Zhao J S. Structure synthesis and workspace analysis of a telescopic spraying robot. Mechanism and Machine Theory, 2019; 133: 295–310.
[25] Liu Y D. Design and research of profiling and pruning device for jujube tree. MS dissertation. Shihezi: Shihezi University, 2018. (in Chinese)
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[2] Peng F, Zheng H N, Lu S H, Shi Z T, Liu X X, Li L. Growth model and visualization of a virtual jujube tree. Computers and Electronics in Agriculture, 2019; 157: 146–153.
[3] Botterill T, Paulin S, Green R, Williams S, Lin J, Saxton V, et al. A robot system for pruning grape vines. Journal of Field Robotics, 2016; 34(6): 1100–1122.
[4] Feng L, Jia J H. Improved algorithm of RRT path planning based on comparison optimization. Computer Engineering & Applications, 2011; 47: 210–213.
[5] Khatib O. Real-time obstacle avoidance for manipulators and mobile robots. International Journal of Robotics Research, 1986; 5(1): 90–98.
[6] Van Henten E J, Hemming J, Van Tuijl B, Kornet J G, Bontsema J. Collision free motion planning for a cucumber picking robot. Biosystems Engineering, 2003; 86(2): 135–144.
[7] Lazarowska, Agnieszka. Ship's trajectory planning for collision avoidance at sea based on ant colony optimisation. Journal of Navigation, 2015; 68(2): 291–307.
[8] Van Henten E J, Hemming J, Van Tuijl B, Kornet J G, Meuleman J, Bontsema J, et al. An autonomous robot for harvesting cucumbers in greenhouses. Autonomous Robots, 2002; 13(3): 241–258.
[9] Rostami S M H, Sangaiah A K, Wang J, Liu X Z. Obstacle avoidance of mobile robots using modified artificial potential field algorithm. EURASIP Journal on Wireless Communications and Networking, 2019; 2019(1): 1–19.
[10] Ajeil F H, Ibraheem I K, Azar A T, Humaidi A J. Grid-based mobile robot path planning using aging-based ant colony optimization algorithm in static and dynamic environments. Sensors, 2020; 20(7): 1880. doi: 10.3390/s20071880.
[11] Cao X M, Zou X J, Jia C Y, Chen M Y, Zeng Z Q. RRT-based path planning for an intelligent litchi-picking manipulator. Computers and Electronics in Agriculture, 2019; 156: 105–118.
[12] Luo L F, Wen H J, Lu Q H, Huang H J, Chen W L, Zou X J, et al. Collision-free path-planning for six-DOF serial harvesting robot based on energy optimal and artificial potential field. Complexity, 2018; 2018: 1–12.
[13] Lavalle S M. Rapidly-exploring random trees: a new tool path planning. Iowa City: Computer Science Department of Iowa State University, 1998; 98p.
[14] Kulkarni P, Goswami D, Guha P, Dutta A. Path planning for a statically stable biped robot using PRM and reinforcement learning. Journal of Intelligent & Robotic Systems, 2006; 47(3): 197–214.
[15] Kuffner J J, Lavalle S M. RRT-connect: an efficient approach to single-query path planning. Proceedings of the 2000 IEEE International Conference on Robotics and Automation, California: IEEE, 2000; pp. 995–1001.
[16] Nguyen T T, Kayacan E, Baedemaeker J D, Saeys W. Task and motion planning for apple harvesting robot. IFAC Proceedings Volumes, 2013; 46(18): 247–252.
[17] Ma J T. Research on obstacle avoidance motion planning of citrus harvesting robot in unstructured environment. MS dissertation, Chongqing: Chongqing University of Technology, 2019; 96p. (in Chinese)
[18] Wei K, Ren B Y. A method on dynamic path planning for robotic manipulator autonomous obstacle avoidance based on an improved RRT algorithm. Sensors, 2018; 18(2): 571. doi: 10.3390/s18020571.
[19] Zhang H J, Wang Y K, Zheng J, Yu J Z. Path planning of industrial robot based on improved RRT algorithm in complex environments. IEEE Access, 2018; 6: 53296–53306.
[20] Wang K, Huang B, Zeng G H, Li X B. Fast path planning algorithm based on improved RRT-Connect. Journal of Wuhan University, 2019; 65(3): 283–289. (in Chinese)
[21] Zhao X L, Cao Z Q, Geng W J, Yu Y Y, Tan M, Chen X C. Path planning of manipulator based on RRT-Connect and Bezier curve. Proceedings of 9th IEEE International Conference on CYBER Technology in Automation, Suzhou: IEEE, 2019; pp. 649–653.
[22] Zhang D G, Xu Y, Yao X T. An improved path planning algorithm for unmanned aerial vehicle based on RRT-Connect. 2018 37th Chinese Control Conference, Wuhan: CCC, 2018; pp. 4854–4858.
[23] Liu H Y, Zhang X B, Wen J, Wang R H, Chen X. Goal-biased bidirectional RRT based on curve-smoothing. IFAC-PapersOnLine, 2019; 52(24): 255–260.
[24] Li Z Y, Zhao D J, Zhao J S. Structure synthesis and workspace analysis of a telescopic spraying robot. Mechanism and Machine Theory, 2019; 133: 295–310.
[25] Liu Y D. Design and research of profiling and pruning device for jujube tree. MS dissertation. Shihezi: Shihezi University, 2018. (in Chinese)
[26] Yuan C Y, Zhang W Q, Liu G F, Pan X L, Liu X H. A heuristic rapidly-exploring random trees method for manipulator motion planning. IEEE Access, 2020; 8: 900–910.
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Published
2022-04-23
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Chen, Y., Fu, Y., Zhang, B., Fu, W., & Shen, C. (2022). Path planning of the fruit tree pruning manipulator based on improved RRT-Connect algorithm. International Journal of Agricultural and Biological Engineering, 15(2), 177–188. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/6249
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Information Technology, Sensors and Control Systems
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