Abstract
In the field of obstacle avoidance path planning, the traditional Rapidly-Exploring Random Tree (RRT) algorithm has many problems, such as no direction and low efficiency. So it is often used to adjust the growth direction of random tree nodes by introducing a target bias strategy to decrease the search blindness. On this basis, the end movement distance and the variation range of each joint during the manipulator trajectory planning process have been focused on in this paper. Considering the requirements of the speed of the planning executable trajectory and the smoothness of the moving process, a cost function about the path length and the smooth change of the joints has been designed. Then, under the premise of the stability of the path planning results, an improved RRT algorithm on dynamical adjustment of the new nodes generation has been proposed to increase the planning efficiency obviously. Its feasibility and effectiveness have been verified fully by a series of simulation experiments based on MATLAB platform.
Supported by Beijing University of Technology.
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References
LaValle, Rapidly-Exploring Random Trees: A New Tool for Path Planning. Technical Report Computer Science Department, Iowa State University, pp. 67–74 (1998)
Lee, M.C., Park, M.G.: Artificial potential field based path planning for mobile robots using a virtual obstacle concept. In: Proceedings 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2003), vol. 2, pp. 735–740. Kobe, Japan (2003) . https://doi.org/10.1109/AIM.2003.1225434
Huadong, Z., Chaofan, L., Nan, J.: A path planning method of robot arm obstacle avoidance based on dynamic recursive ant colony algorithm. In: 2019 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS), pp. 549–552. Shenyang, China (2019) . https://doi.org/10.1109/ICPICS47731.2019.8942495
Ju, C., Luo, Q., Yan, X.: Path planning using an improved a-star algorithm. In: 2020 11th International Conference on Prognostics and System Health Management (PHM-2020 Jinan), pp. 23–26. Jinan, China (2020) . https://doi.org/10.1109/PHM-Jinan48558.2020.00012
Hui, G., Lv, Z.: Research on inaccessible aim of artificial potential field method. Foreign Electron. Measur. Technol. 37(01), 29–33 (2018)
Aravindan, A., Zaheer, S., Gulrez, T.: An integrated approach for path planning and control for autonomous mobile robots. In: 2016 International Conference on Next Generation Intelligent Systems (ICNGIS), pp. 1–6. Kottayam (2016). https://doi.org/10.1109/ICNGIS.2016.7854041
Wang, Z., Shan, L., Chang, L., Qiu, B., Qi, Z.: Variable sampling domain and map compression based on greedy RRT algorithm for robot path planning. In: 2020 39th Chinese Control Conference (CCC), pp. 3915–3919. Shenyang, China (2020) . https://doi.org/10.23919/CCC50068.2020.9188379
Chen, L., Yu, L., Libin, S., Jiwen, Z.: Greedy BIT* (GBIT*): greedy search policy for sampling-based optimal planning with a faster initial solution and convergence. In: 2021 International Conference on Computer, Control and Robotics (ICCCR), pp. 30–36. Shanghai, China (2021). https://doi.org/10.1109/ICCCR49711.2021.9349403
Lin, N., Zhang, Y.: An adaptive RRT based on dynamic step for UAVs route planning. In: 2014 IEEE 5th International Conference on Software Engineering and Service Science, pp. 1111–1114. Beijing, China (2014). https://doi.org/10.1109/ICSESS.2014.6933760
Xue, Y., Zhang, X., Jia, S., Sun, Y., Diao, C.: Hybrid bidirectional rapidly-exploring random trees algorithm with heuristic target graviton. In: 2017 Chinese Automation Congress (CAC), pp. 4357–4361. Jinan (2017). https://doi.org/10.1109/CAC.2017.8243546
Mashayekhi, R., Idris, M.Y.I., Anisi, M.H., Ahmedy, I., Ali, I.: Informed RRT*-connect: an asymptotically optimal single-query path planning method. In: IEEE Access, vol. 8, pp. 19842–19852 (2020). https://doi.org/10.1109/ACCESS.2020.2969316
Ayob, M.A., Zakaria, W.N.W., Jalani, J.: Forward kinematics analysis of a 5-axis RV-2AJ robot manipulator. In: 2014 Electrical Power, Electronics, Communicatons, Control and Informatics Seminar (EECCIS), pp. 87–92. Malang (2014). https://doi.org/10.1109/EECCIS.2014.7003725
Rafsanzani, A.R., Hidayat, R.C., Cahyadi, A.I., Herdjunanto, S.: Omnidirectional sensing for escaping local minimum on potential field mobile robot path planning in corridors environment. In: 2018 3rd International Seminar on Sensors, Instrumentation, Measurement and Metrology (ISSIMM), pp. 79–83. Depok, Indonesia (2018) . https://doi.org/10.1109/ISSIMM.2018.8727639
Otani, T., Koshino, M.: Applying a path planner based on RRT to cooperative multirobot box-pushing. Artif. Life Robot. 13(2), 418–422 (2009). https://doi.org/10.1007/s10015-008-0592-7
Zhang, J., Wisse, M., Bharatheesha, M.: Guided RRT: a greedy search strategy for kinodynamic motion planning. In: 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), pp. 480–485. Singapore (2014). https://doi.org/10.1109/ICARCV.2014.7064352
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Hu, T. (2021). Research on Obstacle Avoidance Path Planning of Manipulator Based on Improved RRT Algorithm. In: Fang, L., Chen, Y., Zhai, G., Wang, J., Wang, R., Dong, W. (eds) Artificial Intelligence. CICAI 2021. Lecture Notes in Computer Science(), vol 13070. Springer, Cham. https://doi.org/10.1007/978-3-030-93049-3_25
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