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AEB-RRT*: an adaptive extension bidirectional RRT* algorithm

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Abstract

Due to the probabilistic completeness and asymptotic optimality, the RRT* algorithm can find sub-optimal solutions and solve path planning problems effectively compared with other strategies. The B-RRT* algorithm introduces the bidirectional search to obtain faster convergence and shorter paths. To solve the problems of path planning in complex environments with concavities, convexities, narrow channels, mazes and multiple obstacles, an adaptive extension bidirectional RRT* (AEB-RRT*) algorithm is proposed. The AEB-RRT* algorithm simultaneously extends from both the starting point and the ending point, and adaptively adjusts the sampling probability and different expansion methods according to the number of collision detection failures. After acquiring a feasible path, it is then post-processed by interpolation and the principle of triangular inequality to obtain a shorter collision-free path. And the Manhattan distance replaces the Euclidean distance to calculate the distances between nodes in order to achieve higher computational efficiency. The experimental results show that the AEB-RRT* algorithm outperforms other path planning algorithms in stronger search ability, obtaining near-optimal or best paths with high efficiency and better robustness. Furthermore, it is successfully applied to solve the 6-DOF arc welding robot path planning problem.

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References

  • Baykal, C., Bowen, C., & Alterovitz, R. (2019). Asymptotically optimal kinematic design of robots using motion planning[J]. Autonomous Robots, 43(2), 345–357.

    Article  Google Scholar 

  • Bergen, G. (1997). Efficient collision detection of complex deformable models using AABB trees[J]. Journal of Graphics Tools, 2(4), 1–13.

    Article  MATH  Google Scholar 

  • Bohlin, R., Kavraki, L. E. (2000). Path planning using lazy PRM[C]//Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No. 00CH37065). IEEE, 1: 521–528.

  • Cao, X. M., Zou, X. J., Jia, C. Y., et al. (2019). RRT-based path planning for an intelligent litchi-picking manipulator[J]. Computers and Electronics in Agriculture, 156, 105–118.

    Article  Google Scholar 

  • Chaari, I., Koubâa, A., Trigui, S., et al. (2014). SmartPATH: An efficient hybrid ACO-GA algorithm for solving the global path planning problem of mobile robots[J]. International Journal of Advanced Robotic Systems, 11(7), 399–412.

    Article  Google Scholar 

  • Chao, N., Liu, Y. K., Xia, H., et al. (2018). Grid-based RRT* for minimum dose walking path-planning in complex radioactive environments[J]. Annals of Nuclear Energy, 115, 73–82.

    Article  Google Scholar 

  • Chen, L., Shan, Y. X., Tian, W., et al. (2018). A fast and efficient double-tree RRT*-like sampling-based planner applying on mobile robotic systems[J]. IEEE/ASME Transactions on Mechatronics, 23(6), 2568–2578.

    Article  Google Scholar 

  • Chen, X., Kong, Y. Y., Fang, X., et al. (2013). A fast two-stage ACO algorithm for robotic path planning[J]. Neural Computing and Applications, 22(2), 313–319.

    Article  Google Scholar 

  • Chen, Y., He, Z., & Li, S. L. (2019). Horizon-based lazy optimal RRT for fast, efficient re-planning in dynamic environment[J]. Autonomous Robots, 43(8), 2271–2292.

    Article  Google Scholar 

  • Chend, S. J., & Fend, Y. P. (2015). Fast collision detection algorithm of cylinders based on generatrices[J]. Journal of Jilin University (science Edition), 2, 291–296.

    Google Scholar 

  • Dong, Y., Camci, E., & Kayacan, E. (2018). Faster RRT-based non-holonomic path planning in 2D building environments using skeleton-constrained path biasing[J]. Journal of Intelligent & Robotic Systems, 89(3–4), 387–401.

    Article  Google Scholar 

  • Du, M. B., Mei, T., Chen, J. J., et al. (2015). RRT-based motion planning algorithm for intelligent vehicle in complex environments [J]. Robot, 37(4), 443–450.

    Google Scholar 

  • Gammell, J. D., Srinivasa, S. S., & Barfoot, T. D. (2014). Informed RRT*: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic[C]//2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2997–3004.

  • Geng, N., Gong, D. W., & Zhang, Y. (2014). PSO-based robot path planning for multi-survivor rescue in limited survival time[J]. Mathematical Problems in Engineering, 2014, 1–10.

    Google Scholar 

  • Hart, P. E., Nilsson, N. J., & Raphael, B. (1968). A formal basis for the heuristic determination of minimum cost paths[J]. IEEE Transactions on Systems Science and Cybernetics, 4(2), 100–107.

    Article  Google Scholar 

  • Howden, W. E. (1968). The sofa problem[J]. The Computer Journal, 11(3), 299–301.

    Article  MATH  Google Scholar 

  • Jeong, I. B., Lee, S. J., & Kim, J. H. (2019). Quick-RRT*: Triangular inequality-based implementation of RRT* with improved initial solution and convergence rate[J]. Expert Systems with Applications, 123, 82–90.

    Article  Google Scholar 

  • Jordan, M., Perez, A. (2013). Optimal bidirectional rapidly-exploring random trees[R]. Technical Report MIT-CSAIL-TR-2013–021, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.

  • Kabutan, R., & Nishida, T. (2018). Motion planning by T-RRT with potential function for vertical articulated robots[J]. Electrical Engineering in Japan, 204(2), 34–43.

    Article  Google Scholar 

  • Karaman, S., Frazzoli, E. (2010). Incremental sampling-based algorithms for optimal motion planning[C]// Proceedings of Robotics: Science and Systems VI. The MIT Press, 34–41.

  • Kavraki, L. E., Svestka, P., Latombe, J. C., et al. (1996). Probabilistic roadmaps for path planning in high-dimensional configuration spaces[J]. IEEE Transactions on Robotics and Automation, 12(4), 566–580.

    Article  Google Scholar 

  • Khatib, O. (1985). Real-time obstacle avoidance for manipulators and mobile robots[C]//Proceedings. 1985 IEEE International Conference on Robotics and Automation. IEEE, 2: 500–505.

  • Kim, M. C., & Song, J. B. (2018). Informed RRT* with improved converging rate by adopting wrapping procedure[J]. Intelligent Service Robotics, 11(1), 53–60.

    Article  Google Scholar 

  • Kuffner, J. J., LaValle, S. M. (2000). RRT-connect: An efficient approach to single-query path planning[C]//Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No. 00CH37065). IEEE, 2: 995–1001.

  • LaValle, S. M. (1998). Rapidly-exploring random trees: A new tool forpath planning [R]. Technical Report TR-98–11, Computer Science Department, Iowa State University, Ames, IA, USA.

  • Li, Y., Cui, R. X., Li, Z. J., et al. (2018). Neural network approximation based near-optimal motion planning with kinodynamic constraints using RRT[J]. IEEE Transactions on Industrial Electronics, 65(11), 8718–8729.

    Article  Google Scholar 

  • Lozano-Pérez, T., Wesley, M. A., & Fritsch, F. N. (1979). An algorithm for planning collision-free paths among polyhedral obstacles[J]. Communications of the Association for Computing Machinery, 22(10), 560–570.

    Article  Google Scholar 

  • Nasir, J., Islam, F., Malik, U., et al. (2013). RRT*-SMART: A rapid convergence implementation of RRT*[J]. International Journal of Advanced Robotic Systems, 10(7), 1651–1656.

    Article  Google Scholar 

  • Noreen, I., Khan, A., Ryu, H., et al. (2018). Optimal path planning in cluttered environment using RRT*-AB[J]. Intelligent Service Robotics, 11(1), 41–52.

    Article  Google Scholar 

  • Patle, B. K., Babu, L. G., Pandey, A., et al. (2019). A review: On path planning strategies for navigation of mobile robot[J]. Defence Technology, 15(4), 582–606.

    Article  Google Scholar 

  • Qureshi, A. H., & Ayaz, Y. (2015). Intelligent bidirectional rapidly-exploring random trees for optimal motion planning in complex cluttered environments[J]. Robotics and Autonomous Systems, 68, 1–11.

    Article  Google Scholar 

  • Ryu, H., & Park, Y. (2019). Improved Informed RRT* using grid map skeletonization for mobile robot path planning[J]. International Journal of Precision Engineering and Manufacturing, 20(11), 2033–2039.

    Article  Google Scholar 

  • Sintov, A., Borum, A., & Bretl, T. (2018). Motion planning of fully actuated closed kinematic chains with revolute joints: A comparative analysis[J]. IEEE Robotics and Automation Letters, 3(4), 2886–2893.

    Article  Google Scholar 

  • Taheri, E., Ferdowsi, M. H., & Danesh, M. (2018). Fuzzy greedy RRT path planning algorithm in a complex configuration space[J]. International Journal of Control, Automation and Systems, 16(6), 3026–3035.

    Article  Google Scholar 

  • Tahir, Z., Qureshi, A. H., Ayaz, Y., et al. (2018). Potentially guided bidirectionalized RRT* for fast optimal path planning in cluttered environments[J]. Robotics and Autonomous Systems, 108, 13–27.

    Article  Google Scholar 

  • Vatcha, R., & Xiao, J. (2014). Detection of robustly collision-free trajectories in unpredictable environments in real-time[J]. Autonomous Robots, 37(1), 81–96.

    Article  Google Scholar 

  • Wang, X. Y., Li, X. J., Guan, Y., et al. (2019). Bidirectional potential guided RRT* for motion planning[J]. IEEE Access, 7, 95034–95045.

    Google Scholar 

  • Wang, X. W., Shi, Y. P., Ding, D. Y., et al. (2016). Double global optimum genetic algorithm–particle swarm optimization-based welding robot path planning[J]. Engineering Optimization, 48(2), 299–316.

    Article  MathSciNet  Google Scholar 

  • Wang, X. W., Tang, B., Yan, Y. X., et al. (2018). Time-optimal path planning for dual-welding robots based on intelligent optimization strategy[M]//Transactions on Intelligent Welding Manufacturing (pp. 47–59). Springer.

    Google Scholar 

  • Wang, X. W., Zhou, X., Xia, Z. L., et al. (2021). A survey of welding robot intelligent path optimization[J]. Journal of Manufacturing Processes 63, 14–23.

    Article  Google Scholar 

  • Wang, Z. P., Li, G. B., Jiang, H. J., et al. (2018). Collision-free navigation of autonomous vehicles using convex quadratic programming-based model predictive control[J]. IEEE/ASME Transactions on Mechatronics, 23(3), 1103–1113.

    Article  Google Scholar 

  • Wei, K., & Ren, B. (2018). A method on dynamic path planning for robotic manipulator autonomous obstacle avoidance based on an improved RRT algorithm[J]. Sensors, 18(2), 571.

    Article  Google Scholar 

  • Willms, A. R., & Yang, S. X. (2008). Real-time robot path planning via a distance-propagating dynamic system with obstacle clearance[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (cybernetics), 38(3), 884–893.

    Article  Google Scholar 

  • Zhang, Z., Wu, D. F., Gu, J. D., et al. (2019). A path-planning strategy for unmanned surface vehicles based on an adaptive hybrid dynamic stepsize and target attractive force-RRT Algorithm[J]. Journal of Marine Science and Engineering, 7(5), 132.

    Article  Google Scholar 

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Acknowledgements

The authors appreciate the support of National Natural Science Foundation of China (62076095, 61973120).

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Authors and Affiliations

  1. Key Laboratory of Smart Manufacturing in Energy Chemical Process of Ministry of Education, East China University of Science and Technology, Shanghai, 200237, China

    Xuewu Wang, Jianbin Wei, Xin Zhou, Zelong Xia & Xingsheng Gu

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  1. Xuewu Wang
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  2. Jianbin Wei
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  3. Xin Zhou
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  4. Zelong Xia
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  5. Xingsheng Gu
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Correspondence to Xuewu Wang.

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Wang, X., Wei, J., Zhou, X. et al. AEB-RRT*: an adaptive extension bidirectional RRT* algorithm. Auton Robot 46, 685–704 (2022). https://doi.org/10.1007/s10514-022-10044-x

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