Features and perspectives of application of the rapidly exploring random tree method for motion planning of autonomous robotic manipulators

Objectives. The work analyzes features of one of the most promising approaches to solve the problems for motion planning of autonomous robotic manipulators of various types and purposes using the rapidly exploring random tree (RRT) method. The development of modern robotics is shown to be inextricably linked with the improvement of the designs of the created samples, for which the placement of a manipulator on platform becomes a typical layout option. Prospects for using the RRT method as a constructive basis for creating a universal motion planner are evaluated for mobile and robotic manipulators, including autonomous robotic systems with a manipulator on a moving platform.

Methods. The object of the research is the RRT method and its well-known modifications RRT* and RRT-Connect. The effectiveness of applying such methods for solving problems associated with planning the motions of robotic manipulators of various types was evaluated using computer and natural simulation methods.

Results. Based on a review of the literature and the results of the research, the wide possibilities of the RRT method can be used for solving motion planning problems not only for mobile and robotic manipulators, but also for robotic systems on whose transport platform an onboard manipulator has been installed (including those having a redundant or reconfigurable structure). The effectiveness of the applied application of the RRT method is confirmed by examples of modeling a mobile platform with an onboard manipulator and the results of full-scale experiments with a prototype of the ARAKS reconfigurable mechatronic-modular robotic manipulators (RTU MIREA, Russia). It can be experimentally demonstrated and theoretically substantiated that the final dimension of the exploring tree, and hence the time of its construction up to reaching a given target state, is largely determined by the value of the growth factor.

Conclusions. The generalization of the results obtained opens up real prospects for using the RRT method as a constructive basis not only for creating universal means for motion planning mobile robotic systems with an onboard manipulator, but also for solving the problems of automating the docking of autonomous mobile platforms.

1. Wang X., Luo X., Han B., Chen Y., Liang G., Zheng K. Collision-free path planning method for robots based on an improved rapidly-exploring random tree algorithm. Appl. Sci. 2020;10(4):1381. http://doi.org/10.3390/app10041381

2. Noreen I., Khan A., Habib Z. Optimal path planning using RRT* based approaches: a survey and future directions. Int. J. Adv. Computer Sci. Appl. 2016;7(11). https://dx.doi.org/10.14569/IJACSA.2016.071114

3. LaValle S.M., Kuffner J.J. Rapidly-exploring random trees: Progress and prospects. Workshop on the Algorithmic Foundations of Robotics. 2000;293–308. URL: http://lavalle.pl/papers/LavKuf01.pdf

4. LaValle S.M., Kuffner J.J. Randomized Kinodynamic Planning. Int. J. Rob. Res. 2001;20(5):378–400. https://doi.org/10.1177/02783640122067453

5. Cao X., Zou X., Jia Ch., Chen M., Zeng Z. RRT-based path planning for an intelligent litchi-picking manipulator. Computers and Electronics in Agriculture. 2019;156:105–118. https://doi.org/10.1016/j.compag.2018.10.031

6. Rybus T. Point-to-Point Motion Planning of a Free-Floating Space Manipulator Using the Rapidly-Exploring Random Trees (RRT) Method. Robotica. 2020;38(6):957–982. https://doi.org/10.1017/S0263574719001176

7. Karaman S., Frazzoli E. Sampling-based algorithms for optimal motion planning. Int. J. Robotics Res. 2011;30(7): 846–894. https://doi.org/10.1177/0278364911406761

8. Solovey K., Janson L., Schmerling E., Frazzoli E., Pavone M. Revisiting the Asymptotic Optimality of RRT*. 2020. arXiv: 1909.09688v2. 13 p. https://doi.org/10.48550/arXiv.1909.09688

9. Noreen I., Khan A., Habib Z. Optimal path planning using RRT* based approaches: a survey and future directions. Int. J. Adv. Computer Sci. Appl. 2016;7(11):97–107. https://doi.org/10.14569/IJACSA.2016.071114

10. Adiyatov O., Varol H.A. A novel RRT*-based algorithm for motion planning in Dynamic environments. In: 2017 IEEE International Conference on Mechatronics and Automation (ICMA). 2017. P. 1416–1421. https://doi.org/10.1109/ICMA.2017.8016024

11. Yuan Ch., Liu G., Zhang W., Pan X. An efficient RRT cache method in dynamic environments for path planning. Rob. Auton. Syst. 2020;131(9):103595. https://doi.org/10.1016/j.robot.2020.103595

12. Khatib O. Real-time obstacle avoidance for manipulators and mobile robots. The Int. J. Rob. Res. 1986;5(1): 90–98.

13. Mashayekhi R., Idris M.Y.I., Anisi M.H., Ahmedy I., Ali I. Informed RRT*-Connect: An Asymptotically Optimal Single-Query Path Planning Method. IEEE Access. 2020;8:19842–19852. http://doi.org/10.1109/ACCESS.2020.2969316