GO-GASA: Grid Optimization Genetic A* Algorithm for Mobile Robots Path Planning Utilizing Grid Optimization
Path planning, as a central challenge in mobile robotics, directly impacts the operational efficiency and safety of robotic systems. To enhance navigation performance in complex environments, this paper proposes a two-stage optimization framework: First, a Grid-Optimized Genetic A<inline-formula&...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11075758/ |
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| Summary: | Path planning, as a central challenge in mobile robotics, directly impacts the operational efficiency and safety of robotic systems. To enhance navigation performance in complex environments, this paper proposes a two-stage optimization framework: First, a Grid-Optimized Genetic A<inline-formula> <tex-math notation="LaTeX">${}^{\ast }$ </tex-math></inline-formula> Algorithm (GO-GASA) is developed by integrating the global search capability of genetic algorithms with an enhanced A<inline-formula> <tex-math notation="LaTeX">${}^{\ast }$ </tex-math></inline-formula> algorithm for optimizing the generation of initial populations. This integration not only improves the initialization performance of genetic algorithms but also enhances their overall optimization capacity. Second, a Grid-Optimized Redundant Path Removal Algorithm (GO-RPRA) is introduced to perform secondary optimization on the paths generated by GO-GASA, further improving path smoothness and efficiency. Simulation results demonstrate that in initialization experiments, the individual diversity of GO-GASA is 7.8 times higher than that of traditional genetic algorithms (GA). Under identical testing conditions, compared to six other popular metaheuristic algorithms, GO-GASA reduces the number of turns by 53.85%, the max turning angle by 50%, and the total distance by 25.24%. Overall, these improvements significantly enhance the quality of path planning and strengthen the capability of mobile robots to handle complex tasks. |
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| ISSN: | 2169-3536 |