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A Voronoi path planning extracted from improved skeleton for dynamic environments

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Abstract

Aiming at the problems that the robot being in the process of navigation cannot meet the requirements of real-time and accuracy at the same time, moreover is too close to obstacles and lacks the initiative to avoid obstacles, a Voronoi diagram algorithm for improved skeleton extraction suitable for dynamic environment is proposed. On the one hand, firstly the grid map is preprocessed by binarization, corrosion and expansion, so the reduced skeleton map suitable for navigation is obtained, then the reduced skeleton map is extracted for searching the global path, finally the improved cubic spline smoothing algorithm is used to optimize the global path each planned, thus overcoming the defects of bloated and tortuous in the path obtaining by original Voronoi diagram algorithm. On the other hand, the position information of all obstacles is obtained by a single scan lidar. Firstly, segmenting and linearly fitting all laser point clouds to remove the known obstacles in the map. Then to mark new possible dynamic obstacles with circles of appropriate size. Secondly detecting dynamic obstacles by the alteration of their center coordinates, moreover, solving their motion equations. Finally expanding the cost map along the speed direction of dynamic obstacles and combining DWA dynamic window method to realize dynamic obstacle avoidance. Compared with the original DWA algorithm, it can predict the motion state of dynamic obstacles in advance, which improves the safety of the robot in the dynamic environment. Moreover, the effectiveness of the algorithm is verified by many simulation experiments and real environment experiments.

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Acknowledgements

This work was partially supported by the National Key R&D Program of China grant #2019YFB1310000, the Wuhan Application Foundation Frontier Project #2019010701011404 and Hubei Provincial Key R&D Program Project # 2020BAB098.

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Authors and Affiliations

  1. Key Education Laboratory of Ministry of Metallurgical Equipment and Control, Wuhan University of Science and Technology, Wuhan, 430081, China

    Lin Jiang, Jun Li, Yuxin Hu, Feng Pan & Jianyang Zhu

  2. Institute of Robotics and Intelligent Systems, Wuhan University of Science and Technology, Wuhan, 430081, China

    Lin Jiang, Jianyang Zhu & Bin Lei

  3. Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, 430081, China

    Bin Lei

  4. College of Mechanical and Electrical Engineering, Soochow University, Suzhou, Jiangsu, 215021, China

    Rui Lin

Authors
  1. Lin Jiang
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  2. Jun Li
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  3. Yuxin Hu
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  4. Feng Pan
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  5. Jianyang Zhu
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  6. Bin Lei
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  7. Rui Lin
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Corresponding author

Correspondence to Lin Jiang.

Additional information

Lin Jiang graduated from the Harbin Institute of Technology in 2008 and received his Ph.D. Now he works in Wuhan University of Science and Technology as a Professor. He is also a doctoral supervisor. His research interests include mobile robot mapping, localization, and navigation.

Jun Li graduated from the Wuhan University of Science and Technology in 2021 and received his Master’s degree. His research interests include mobile robot mapping, localization, and navigation.

Yuxin Hu was Master degree candidate in Wuhan University of Science and Technology in 2021. Her research interests include mobile robot localization and navigation.

Feng Pan graduated from the Wuhan University of Science and Technology in 2021 and received his Master’s degree. His research interests include mobile robot mapping and localization.

Jianyang Zhu graduated from the Harbin Institute of Technology in 2014 and received his Ph.D. Now he works in Wuhan University of Science and Technology as an Associate Professor. He is a doctoral supervisor. His research interests include bionic robots, low-speed wind/water energy capture technology, etc.

Bin Lei graduated from the Wuhan University of Technology in 2009 and received his Ph.D. Now he works in Wuhan University of Science and Technology, as an Associate Professor. His research interests include swarm robot system and wireless sensor network.

Rui Lin, Member of IEEE, received the B.A. degree in Mechatronic Engineering from the China University of Geosciences, Wuhan, in 2005, and the S.M. and Ph.D. degrees in Robotics from the Harbin Institute of Technology in 2007 and 2011, respectively. He is currently an Associate Professor in Soochow University. His research interests include autonomous navigation of intelligent mobile robot, motion control, and commercial service robot.

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Jiang, L., Li, J., Hu, Y. et al. A Voronoi path planning extracted from improved skeleton for dynamic environments. J Mech Sci Technol 37, 2019–2032 (2023). https://doi.org/10.1007/s12206-023-0338-4

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  • DOI: https://doi.org/10.1007/s12206-023-0338-4

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