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Collision-free trajectory planning for multi-robot simultaneous motion in preforms weaving

Published online by Cambridge University Press:  30 June 2022

Gaoping Xu Open the ORCID record for Gaoping Xu [Opens in a new window] ,
Zhuo Meng ,
Shuo Li  and
Yize Sun
Gaoping Xu
Affiliation:
College of Mechanical Engineering, Donghua University, Songjiang, Shanghai, 201620, China
Zhuo Meng
Affiliation:
College of Mechanical Engineering, Donghua University, Songjiang, Shanghai, 201620, China
Shuo Li
Affiliation:
College of Mechanical Engineering, Donghua University, Songjiang, Shanghai, 201620, China
Yize Sun*
Affiliation:
College of Mechanical Engineering, Donghua University, Songjiang, Shanghai, 201620, China
*
*Corresponding author. E-mail: [email protected]
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Abstract

In this paper, an automatic obstacle avoidance trajectory planning strategy is proposed for the simultaneous motion of multi-robots, which perform anthropomorphic skill operations in a large curved preformed three-dimensional (3D) weaving environment with multiple obstacles and limited space, to eliminate tedious manual calibration work of robot path in engineering. Firstly, an Adaptive Goal-guided Rapidly-exploring Random Trees (AGG-RRT) algorithm is proposed, combined with the robot obstacle avoidance strategy, to search the discrete position of the collision-free path of the end-effector gradually from the starting point to the ending point. Then the optimization of the path is completed by bidirectional pruning of redundant nodes and cubic non-uniform rational B-spline (NURBS) curve fitting. And finally, the robot trajectory is interpolated based on S-shaped acceleration/deceleration planning to ensure smooth robot joint motion. The simulation results demonstrate the superiority of the AGG-RRT algorithm over the basic RRT algorithm and related improved algorithms in terms of search time and success rate. The simulated experiments also achieve the smooth trajectory planning of multiple robotic arms with the synchronous obstacle avoidance motion, which shows that the AGG-RRT algorithm is successfully applied and the collision-free trajectory planning strategy is effective.

Keywords

AGG-RRTmulti-robotNURBSS-shaped acceleration/decelerationcollision-free trajectory planning
Type
Research Article
Information
Robotica , Volume 40 , Issue 12 , December 2022 , pp. 4218 - 4237
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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References

Luo, L., Wen, H., Lu, Q., Huang, H., Chen, W., Zou, X. and Wang, C., “Collision-free path-planning for six-DOF serial harvesting robot based on energy optimal and artificial potential field,” Complexity 2018(5), 1–12 (2018).Google Scholar
Hu, X., Chen, L., Tang, B., Cao, D. and He, H., “Dynamic path planning for autonomous driving on various roads with avoidance of static and moving obstacles,” Mech. Syst. Signal. Pr. 100(5), 482–500 (2018).CrossRefGoogle Scholar
Pham, T.-T., Mulhem, P., Maisonnasse, L., Gaussier, E. and Lim, J.-H., “Visual graph modeling for scene recognition and mobile robot localization,” Multimed. Tools Appl. 60(2), 419–441 (2012).CrossRefGoogle Scholar
Sánchez, G. and Latombe, J.-C., “On delaying collision checking in PRM planning: Application to multi-robot coordination,” Ind. Robot. 21(1), 5–26 (2002).Google Scholar
Shi, K., Denny, J. and Amato, N. M., “Spark PRM: Using RRTs within PRMs to Efficiently Explore Narrow Passages,” In: IEEE International Conference on Robotics & Automation(ICRA), vol. 3 (2014) pp. 4659–4666.Google Scholar
Kang, J. G., Lim, D. W., Choi, Y. S., Jang, W. J. and Jung, J. W., “Improved RRT-connect algorithm based on triangular inequality for robot path planning,” Sensors 21(2), 333 (2021).CrossRefGoogle ScholarPubMed
Zhu, B., Li, C., Li, S., Yong, S. and Li, Y., “A Algorithm of Global Path Planning Based on the Grid Map and V-graph Environmental Model for the Mobile Robot,” In: Chinese Automation Congress (CAC 2017), (2017) pp. 4973–4977.Google Scholar
Wang, W., Zhu, M., Wang, X., He, S., He, J. and Xu, Z., “An improved artificial potential field method of trajectory planning and obstacle avoidance for redundant manipulators,” Int. J. Adv. Robot. Syst. 15(5), 1–13 2018 (2018).CrossRefGoogle Scholar
Rostami, S. M. H., Sangaiah, A. K., Wang, J. and Liu, X., “Obstacle avoidance of mobile robots using modified artificial potential field algorithm,” Eurasip J. Wirel. Comm. 2019(1), 1 (2019).Google Scholar
Song, J., Cong, Y., Chen, H. and Xu, X., “An Escaping Method From Local Dead Region in Rolling Path Planning,” In: International Conference on Intelligent Computation Technology and Automation (ICICTA 2008) (2008) pp. 338–343.Google Scholar
Yue, R., Xiao, J., Wang, S. and Joseph, S. L., “Three-dimensional path planning of a climbing robot using mixed integer linear programming,” Adv. Robot. 24(15), 2087–2118 (2012).CrossRefGoogle Scholar
Liu, J., Yang, J., Liu, H., Tian, X. and Gao, M., “An improved ant colony algorithm for robot path planning,” Soft Comput. 21(19), 5829–5839 (2017).CrossRefGoogle Scholar
Wang, X. W., Tang, B., Zhou, X. and Gu, X. S., “Double-robot obstacle avoidance path optimization for welding process,” Math. Biosci. Eng. 16(5), 5697–5708 (2019).CrossRefGoogle ScholarPubMed
Tuncer, A. and Yildirim, M., “Dynamic path planning of mobile robots with improved genetic algorithm,” Comput. Electr. Eng. 38(6), 1564–1572 (2012).Google Scholar
Karami, A. H. and Hasanzadeh, M., “An adaptive genetic algorithm for robot motion planning in 2D complex environments,” Comput. Electr. Eng. 43(6), 317–329 (2015).CrossRefGoogle Scholar
Daachi, B., Madani, T. and Benallegue, A., “Adaptive neural controller for redundant robot manipulators and collision avoidance with mobile obstacles,” Neurocomputing 79(2), 50–60 (2012).CrossRefGoogle Scholar
Wei, K. and Ren, B., “A method on dynamic path planning for robotic manipulator autonomous obstacle avoidance based on an improved RRT algorithm,” Sensors 18(2), 571 (2018).CrossRefGoogle Scholar
Lin, D., Shen, B., Liu, Y., Alsaadi, F. E. and Alsaedi, A., “Genetic algorithm-based compliant robot path planning: An improved Bi-RRT-based initialization method,” Assembly Autom. 37(3), 261–270 (2017).CrossRefGoogle Scholar
Ryu, H. and Park, Y., “Improved informed RRT* using gridmap skeletonization for mobile robot path planning,” Int. J. Precis. Eng. Man. 20(11), 2033–2039 (2019).CrossRefGoogle Scholar
Liao, B., Wan, F., Hua, Y., Ma, R., Zhu, S. and Qing, X., “F-RRT*: An improved path planning algorithm with improved initial solution and convergence rate,” Expert Syst. Appl. 184, 115457 (2021).CrossRefGoogle Scholar
Cao, X., Zou, X., Jia, C., Chen, M. and Zeng, Z., “RRT-based path planning for an intelligent litchi-picking manipulator,” Comput. Electron. Agr. 156(11–12), 105–118 (2019).CrossRefGoogle Scholar
Yuan, C., Liu, G., Zhang, W. and Pan, X., “An efficient RRT cache method in dynamic environments for path planning,” Robot. Auton. Syst. 131(9), 103595 (2020).CrossRefGoogle Scholar
Yuan, C., Zhang, W., Liu, G., Pan, X. and Liu, X., “A heuristic rapidly-exploring random trees method for manipulator motion planning,” IEEE Access 8, 900–910 (2020).CrossRefGoogle Scholar
Gan, Y., Zhang, B., Ke, C., Zhu, X., He, W. and Ihara, T., “Research on robot motion planning based on RRT algorithm with nonholonomic constraints,” Neural Process. Lett. 53(4), 3011–3029 (2021).CrossRefGoogle Scholar
Dong, M., Yao, G., Li, J. and Zhang, L., “Research on attitude interpolation and tracking control based on improved orientation vector SLERP method,” Robotica 38(4), 719–731 (2020).CrossRefGoogle Scholar
Ahn, J.-s., Chung, W.-j. and Jung, C.-d., “Realization of orientation interpolation of 6-axis articulated robot using quaternion,” J. Cent. South Univ. 19(12), 3407–3414 (2012).CrossRefGoogle Scholar
, K. Shoemake, “Animating Rotation with Quaternion Curves,” In: SIGGRAPH ‘85: Proceedings of the 12th Annual Conference on Computer Graphics and Interactive Techniques (ACM (1985) pp. 245–254.Google Scholar
Qin, K., Liu, Z. and Liu, J., “Research on Collision Detection Algorithm of Multi-Robot Bonnet Polishing System,” In: Applied Optics and Photonics China (AOPC 2020) (2020).CrossRefGoogle Scholar
Khalil, W. and Kleinfinger, J. F., “Minimum operations and minimum parameters of the dynamic models of tree structure robots,” IEEE J. Robot. Autom. 3(6), 517–526 (1987).Google Scholar
Peiper, D. L., The kinematics of manipulators under computer control, Phd Thesis Stanford University (1968).Google Scholar
Zhou, X., Xian, Y., Chen, Y., Chen, T., Yang, L., Chen, S. and Huang, J., “An improved inverse kinematics solution for 6-DOF robot manipulators with offset wrists,” Robotica , 1–20 (2022).CrossRefGoogle Scholar
Shi, X., Fang, H., Gang, P., Xu, X. and Hua, C., “Time-energy-jerk Dynamic Optimal Trajectory Planning for Manipulators Based on Quintic NURBS,” In: 3rd International Conference on Robotics and Automation Engineering (ICRAE 2018) (2018) pp. 44–49.Google Scholar
Bird, R. and Mu, S.-C., “A greedy algorithm for dropping digits,” J. Funct. Program. 31, e29 (2021).CrossRefGoogle Scholar
Guo, P., Wu, Y., Yang, G., Shen, Z., Zhang, H., Zhang, P., Lou, F. and Li, H., “A feedrate planning method for the NURBS curve in CNC machining based on the critical constraint curve,” Appl. Sci. 11(11), 4959 (2021).CrossRefGoogle Scholar
Liu, M., Huang, Y., Yin, L., Guo, J., Shao, X. and Zhang, G., “Development and implementation of a NURBS interpolator with smooth feedrate scheduling for CNC machine tools,” Int. J. Mach. Tool. Manu. 87(1–2), 1–15 (2014).Google Scholar
Xu, G., Zhang, H., Meng, Z. and Sun, Y., “Automatic interpolation algorithm for NURBS trajectory of shoe sole spraying based on 7-DOF robot,” Int. J. Cloth. Sci. Tech. 34(3), 434–450 (2021).CrossRefGoogle Scholar