Abstract
Motion planning is a very important part of robot technology, where the quality of planning directly affects the energy consumption and safety of robots. Focusing on the shortcomings of traditional RRT methods such as long, unsmooth paths, and uncoupling with robot control system, an automatic robot motion planning method was proposed based on Rapid Exploring Random Tree called AM-RRT* (automatic motion planning based on RRT*). First, the RRT algorithm was improved by increasing the attractive potential fields of the target points of the environment, making it more directional during the sampling process. Then, a path optimization method based on a dynamic model and cubic B-spline curve was designed to make the planned path coupling with the robot controller. Finally, an RRT speed planning algorithm was added to the planned path to avoid dynamic obstacles in real time. To verify the feasibility of AM-RRT*, a detailed comparison was made between AM-RRT* and the traditional RRT series algorithms. The results showed that AM-RRT* improved the shortcomings of RRT and made it more suitable for robot motion planning in a dynamic environment. The proposal of AM-RRT* can provide a new idea for robots to replace human labor in complex environments such as underwater, nuclear power, and mines.
Supported by Guangdong Provincial Science and Technology Plan Project (Grant number 2021B1515420006, Grant number 2021B1515120026); Guangdong Province Marine Economic Development Special Fund Project(Six Major Marine Industries) (GDNRC [2021]46); National Natural Science Foundation of China (Grant number U2141216, Grant number 51875212); Shenzhen Technology Research Project (JSGG20201201100401005, JSGG20201201100400001).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Hichri, B., Gallala, A., Giovannini, F., Kedziora, S.: Mobile robots path planning and mobile multirobots control: a review. Robotica 40(12), 4257–4270 (2022)
Tae, N., Jae, S., Young, C.: A 2.5d map-based mobile robot localization via cooperation of aerial and ground robots. Sensors 17(12), 2730 (2017)
Lozano-Perez, T., Wesley, M.: An algorithm for planning collision-free paths among polyhedral obstacles. Commun. ACM 22, 560–570 (1979)
Takahashi, O., Schilling, R.: Motion planning in a plane using generalized voronoi diagrams. IEEE Trans. Robot. Autom. 5, 143–150 (1989)
Kavraki, L.E., Svestka, P.: Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Trans. Rob. Autom. 12(4), 566–580 (1996)
Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. In: 1985 IEEE International Conference on Robotics and Automation (Cat. No. 85CH2152-7), pp. 500–505 (1984)
Hwang, J., Kim, J., Lim, S., Park, K.: A fast path planning by path graph optimization. IEEE Trans. Syst. Man Cybern. Part A-Syst. Humans 33(1), 121–128 (2003)
Kushleyev, A., Likhachev, M.: Time-bounded lattice for efficient planning in dynamic environments. In: ICRA: 2009 IEEE International Conference Robotics and Automation, vol. 1–7. pp. 4303–4309 (2009)
Likhachev, M., Gordon, G., Thrun, S.: Ara*: anytime a* with provable bounds on sub-optimality. In: Thrun, S., Saul, K., Scholkopf, B. (eds.) Advances in Neural Information Processing Systems, vol. 16, pp. 767–774 (2004)
Lavalle, S.M.: Rapidly-exploring random trees: a new tool for path planning. Research Report (1999)
LaValle, S., Kuffner, J.: Rapidly-exploring random trees: progress and prospects. In: Donald, B., Lynch, K., Rus, D. (eds.) Algorithmic and Computational Robotics: New Directions, pp. 293–308 (2001)
Reeds, J.A., Shepp, R.A.: Optimal paths for a car that goes both forward and backward. Pac. J. Math. 145 (1991)
Brezak, M., Petrovic, I.: Real-time approximation of clothoids with bounded error for path planning applications. IEEE Trans. Rob. 30(2), 507–515 (2014)
Jolly, K.G., Kumar, R.S., Vijayakumar, R.: A bezier curve based path planning in a multi-agent robot soccer system without violating the acceleration limits. Rob. Auton. Syst. 57(1), 23–33 (2009)
Koyuncu, E., Inalhan, G.: A probabilistic b-spline motion planning algorithm for unmanned helicopters flying in dense 3d environments. In: 2008 IEEE/RSJ International Conference on Robots and Intelligent Systems, Conference Proceedings, vol. 1–3, pp. 815–821 (2008)
Luo, M., Hou, X., Yang, S.X.: A multi-scale map method based on bioinspired neural network algorithm for robot path planning. IEEE Access 7, 142682–142691 (2019)
Xin, J., Zhong, J., Sheng, J., Li, P., Cui, Y.: Improved genetic algorithms based on data-driven operators for path planning of unmanned surface vehicle. Int. J. Rob. Autom. 34(6), 713–722 (2019)
Luo, Q., Wang, H., Zheng, Y., He, J.: Research on path planning of mobile robot based on improved ant colony algorithm. Neural Comput. Appl. 32(6SI), 1555–1566 (2020)
Karaman, S., Frazzoli, E.: Incremental sampling-based algorithms for optimal motion planning. Rob. Sci. Syst. 104, 267–274 (2010)
Gammell, J.D., Srinivasa, S.S., Barfoot, T.D.: Informed rrt*: optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), pp. 2997–3004 (2014)
Jeong, I.B., Lee, S.J., Kim, J.H.: Quick-rrt*: triangular inequality-based implementation of rrt* with improved initial solution and convergence rate. Expert Syst. Appl. 123(JUN.), 82–90 (2019)
Qureshi, A.H., Ayaz, Y.: Intelligent bidirectional rapidly-exploring random trees for optimal motion planning in complex cluttered environments. Rob. Auton. Syst. 68, 1–11 (2015)
Jordan, M., Perez, A.: Optimal bidirectional rapidly-exploring random trees (2013)
Li, Y., Wei, W., Gao, Y., Wang, D., Fan, Z.: Pq-rrt*: an improved path planning algorithm for mobile robots. Expert Syst. Appl. 152 (2020)
Shi, Y., Li, Q., Bu, S., Yang, J., Zhu, L.: Research on intelligent vehicle path planning based on rapidly-exploring random tree. Math. Prob. Eng. 2020 (2020)
Jiang, C., et al.: R2-rrt*: reliability-based robust mission planning of off-road autonomous ground vehicle under uncertain terrain environment. IEEE Trans. Autom. Sci. Eng. 19(2), 1030–1046 (2022)
Pepy, R., Lambert, A., Mounier, H.: Path planning using a dynamic vehicle model. In: 2006 2nd International Conference on Information & Communication Technologies, vol. 1, pp. 781–786 (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chi, P. et al. (2024). AM-RRT*: An Automatic Robot Motion Planning Algorithm Based on RRT. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14447. Springer, Singapore. https://doi.org/10.1007/978-981-99-8079-6_8
Download citation
DOI: https://doi.org/10.1007/978-981-99-8079-6_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8078-9
Online ISBN: 978-981-99-8079-6
eBook Packages: Computer ScienceComputer Science (R0)