Abstract
As a simple and effective method, artificial potential field method is often used in robot path planning. Based on this, an improved artificial potential field model is proposed to solve the local minimum problem by using a subgoal strategy. In order to show the subgoal adaptive selection feature of the robot, an obstacle potential field function is established and the effectiveness of the adaptive feature is verified by path planning simulation. A double closed-loop control strategy is adopted to track the trajectory planned by the improved artificial potential field method, and simulation results show that the improved artificial potential method is reliable and the robot can well track the trajectory under the action of the controller.
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References
Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. Int. J. Robot. Res. 5, 90–98 (1986). https://doi.org/10.1007/978-1-4613-8997-2_29
Lee, M.C., Park, M.G.: Artificial potential field based path planning for mobile robots using a virtual obstacle concept. In: International Conference on Advanced Intelligent Mechatronics, pp. 735–740 (2003). https://doi.org/10.1109/AIM.2003.1225434
Li, G., et al.: Effective improved artificial potential field-based regression search method for autonomous mobile robot path planning. Int. J. Mechatron. Autom. 3(3), 141 (2013). https://doi.org/10.1504/ijma.2013.055612
Zhu, Y., Zhang, T., Song, J.: An improved wall following method for escaping from local minimum in artificial potential field based path planning. In: IEEE Conference on Decision Control, pp. 6017–6022 (2010). https://doi.org/10.1109/CDC.2009.5399854
Zhu, Q., Yan, Y., Xing, Z.: Robot path planning based on artificial potential field approach with simulated annealing. In: International Conference on Intelligent Systems Design Applications, vol. 2, p. 622 (2006). https://doi.org/10.1109/ISDA.2006.253908
Park, M.G., Jeon, J.H., Lee, M.C.: Obstacle avoidance for mobile robots using artificial potential field approach with simulated annealing. In: IEEE International Symposium on Industrial Electronics Proceedings, vol. 3, pp. 1530–1535 (2001). https://doi.org/10.1109/ISIE.2001.931933
Yue, M., Wang, S., Zhang, Y.: Adaptive fuzzy logic-based sliding mode control for a nonholonomic mobile robot in the presence of dynamic uncertainties. Proc. Inst. Mech. Eng. Part C: J. Mech. Eng. Sci. 229(11), 1979–1988 (2015). https://doi.org/10.1177/0954406214551625
Yue, M., Wu, G., Wang, S., An, C.: Disturbance observer-based trajectory tracking control for nonholonomic wheeled mobile robot subject to saturated velocity constraint. Appl. Artif. Intell. 28(8), 751–765 (2004). https://doi.org/10.1080/08839514.2014.952918
Acknowledgement
This work was supported by National Natural Science Foundation of China under Grant (Nos. 61873047 and 61573078), Fundamental Research Funds for the Central Universities (DUT19ZD205), and State Key Laboratory of Robotics and System Grant (SKLRS-2019-KF-17).
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Lin, Z., Yue, M., Wu, X., Tian, H. (2019). An Improved Artificial Potential Field Method for Path Planning of Mobile Robot with Subgoal Adaptive Selection. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2019. Lecture Notes in Computer Science(), vol 11740. Springer, Cham. https://doi.org/10.1007/978-3-030-27526-6_19
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DOI: https://doi.org/10.1007/978-3-030-27526-6_19
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