Mobile Robot Path Planning Based on Enhanced Dynamic Window Approach and Improved A∗ Algorithm
Path planning is one of the most popular researches on mobile robots, and it is the key technology to realize autonomous navigation of robots. Aiming at the problem that the mobile robot may collide or fail along the planned path in an environment with random obstacles, a robot path planning scheme...
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| Published in | Journal of Robotics Vol. 2022; pp. 1 - 9 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Hindawi
22.03.2022
John Wiley & Sons, Inc Wiley |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1687-9600 1687-9619 |
| DOI | 10.1155/2022/2183229 |
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| Abstract | Path planning is one of the most popular researches on mobile robots, and it is the key technology to realize autonomous navigation of robots. Aiming at the problem that the mobile robot may collide or fail along the planned path in an environment with random obstacles, a robot path planning scheme that combines the improved A∗ algorithm with an enhanced dynamic window method is proposed. In the improved A∗ algorithm, in order to improve the algorithm efficiency, so that a single planning path can pass through multiple target points, the search point selection strategy and evaluation function are optimized. In order to achieve local obstacle avoidance and pursuit of dynamic target points in dynamic and complex environments, an online path planning method combining enhanced dynamic window algorithm and global path planning information is proposed. The preview deviation angle tracking method is used to successfully capture moving target points. It also improves the efficiency of path planning and ensures that on the basis of the global optimal path, the random obstacle can be avoided in real time so that the robot can reach the target point smoothly. The simulation results show that compared with other methods, the proposed method achieves excellent global and local path planning performance, the planned trajectory is smoother, and the search efficiency is higher in complex environments. |
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| AbstractList | Path planning is one of the most popular researches on mobile robots, and it is the key technology to realize autonomous navigation of robots. Aiming at the problem that the mobile robot may collide or fail along the planned path in an environment with random obstacles, a robot path planning scheme that combines the improved A∗ algorithm with an enhanced dynamic window method is proposed. In the improved A∗ algorithm, in order to improve the algorithm efficiency, so that a single planning path can pass through multiple target points, the search point selection strategy and evaluation function are optimized. In order to achieve local obstacle avoidance and pursuit of dynamic target points in dynamic and complex environments, an online path planning method combining enhanced dynamic window algorithm and global path planning information is proposed. The preview deviation angle tracking method is used to successfully capture moving target points. It also improves the efficiency of path planning and ensures that on the basis of the global optimal path, the random obstacle can be avoided in real time so that the robot can reach the target point smoothly. The simulation results show that compared with other methods, the proposed method achieves excellent global and local path planning performance, the planned trajectory is smoother, and the search efficiency is higher in complex environments. Path planning is one of the most popular researches on mobile robots, and it is the key technology to realize autonomous navigation of robots. Aiming at the problem that the mobile robot may collide or fail along the planned path in an environment with random obstacles, a robot path planning scheme that combines the improved A[sup.∗] algorithm with an enhanced dynamic window method is proposed. In the improved A[sup.∗] algorithm, in order to improve the algorithm efficiency, so that a single planning path can pass through multiple target points, the search point selection strategy and evaluation function are optimized. In order to achieve local obstacle avoidance and pursuit of dynamic target points in dynamic and complex environments, an online path planning method combining enhanced dynamic window algorithm and global path planning information is proposed. The preview deviation angle tracking method is used to successfully capture moving target points. It also improves the efficiency of path planning and ensures that on the basis of the global optimal path, the random obstacle can be avoided in real time so that the robot can reach the target point smoothly. The simulation results show that compared with other methods, the proposed method achieves excellent global and local path planning performance, the planned trajectory is smoother, and the search efficiency is higher in complex environments. Path planning is one of the most popular researches on mobile robots, and it is the key technology to realize autonomous navigation of robots. Aiming at the problem that the mobile robot may collide or fail along the planned path in an environment with random obstacles, a robot path planning scheme that combines the improved A ∗ algorithm with an enhanced dynamic window method is proposed. In the improved A ∗ algorithm, in order to improve the algorithm efficiency, so that a single planning path can pass through multiple target points, the search point selection strategy and evaluation function are optimized. In order to achieve local obstacle avoidance and pursuit of dynamic target points in dynamic and complex environments, an online path planning method combining enhanced dynamic window algorithm and global path planning information is proposed. The preview deviation angle tracking method is used to successfully capture moving target points. It also improves the efficiency of path planning and ensures that on the basis of the global optimal path, the random obstacle can be avoided in real time so that the robot can reach the target point smoothly. The simulation results show that compared with other methods, the proposed method achieves excellent global and local path planning performance, the planned trajectory is smoother, and the search efficiency is higher in complex environments. |
| Audience | Academic |
| Author | Teng, Xingqiang Yang, Hongxia |
| Author_xml | – sequence: 1 givenname: Hongxia orcidid: 0000-0002-3100-3082 surname: Yang fullname: Yang, Hongxia organization: College of BusinessYantai Nanshan UniversityShandongLongkou 265701China – sequence: 2 givenname: Xingqiang orcidid: 0000-0003-2050-4794 surname: Teng fullname: Teng, Xingqiang organization: College of BusinessYantai Nanshan UniversityShandongLongkou 265701China |
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| Cites_doi | 10.1016/j.asoc.2019.02.044 10.1166/jctn.2016.5419 10.1016/j.asoc.2020.106581 10.1016/j.cogr.2021.06.001 10.1177/1729881419839596 10.1108/wje-03-2018-0092 10.5391/ijfis.2020.20.1.26 10.1016/j.compeleceng.2017.12.011 10.1016/j.ifacol.2016.07.610 10.1109/TASE.2016.2533418 10.1016/j.dt.2019.04.011 10.1109/access.2018.2868339 10.1016/j.robot.2019.02.013 10.1007/s10846-019-01112-z 10.1007/s10514-020-09947-4 10.3390/s20010188 |
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| Copyright | Copyright © 2022 Hongxia Yang and Xingqiang Teng. COPYRIGHT 2022 John Wiley & Sons, Inc. Copyright © 2022 Hongxia Yang and Xingqiang Teng. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 |
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| References | 11 22 13 14 17 18 A. Özdemir (12) G. Fragapane (3) 2020; 308 1 2 M. Kusuma (10) 4 A. Kaplan (15) 2016; 14 5 D. Kiss (16) 7 8 9 H. Jahanshahi (6) 2018 Q. Wu (19) 2020; 20 Z. Ma (21) 20 |
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| SubjectTerms | Algorithms Artificial intelligence Autonomous navigation Efficiency Energy consumption Heuristic Methods Moving targets Obstacle avoidance Optimization Planning Robots Trajectory planning |
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| Title | Mobile Robot Path Planning Based on Enhanced Dynamic Window Approach and Improved A∗ Algorithm |
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