| 杨航， 栗淼楠， 蔚佳俊.A*算法和进化策略相融合的路径规划[J].轻工机械,2025,43(5):58-67 |
| A*算法和进化策略相融合的路径规划 |
| Path Planning Based on Integration of A* Algorithm and Evolutionary Strategy |
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| DOI：10.3969/j.issn.1005 2895.2025.05.008 |
| 中文关键词: 自动导引车 路径规划 A*算法 进化策略 样条曲线路径 罚函数 |
| 英文关键词:AGV path planning A* algorithm evolutionary strategy spline curve path penalty function |
| 基金项目:山西省高等学校教学改革创新项目(J20241480)；山西省省级大学生创新创业训练计划项目（TYX2024039）。 |
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| 摘要点击次数: 1 |
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| 中文摘要: |
| 针对自动导引车（Automated Guided Vehicle，AGV）路径规划采用传统A*算法生成基本路径时，在密集障碍环境中忽略机器人体积和转弯半径等因素，导致路径规划效果不佳的问题，课题组提出了一种基于A*算法和进化策略相融合的路径优化方法。首先，采用A*算法生成基本路径，通过节点优化去除冗余节点，并在考虑障碍物碰撞风险和路径宽度等约束条件下，插值生成样条曲线路径，以构建初始优化路径；随后，融合进化策略对路径进行迭代优化，通过罚函数形式充分考虑转弯半径、路径宽度及避障等约束条件；改进后的路径规划方法以样条曲线的路径形式直接进行迭代优化，避免了后期平滑处理的复杂性和不确定性，使路径更加符合AGV的运动特性。仿真实验结果表明：该方法能够减少路径实际长度，提高路径的安全性和平滑性，验证了该方法的有效性。该研究为其他智能算法与A*算法的结合构建了一个通用优化框架，通过直接迭代优化样条曲线路径，实现了更为高效的路径规划。 |
| 英文摘要: |
| A path planning method based on the integration of A* algorithm and evolutionary strategy was proposed to address the problem of poor path planning performance when using traditional A* algorithm to generate basic paths for Automated Guided Vehicles (AGV), which ignored factors such as robot volume and turning radius in dense obstacle environment. A* algorithm was used to generate a basic path and then redundant nodes were removed by nodes optimization.By considering constraints such as obstacle collision risk and path width, spline curves were interpolated to construct the initial optimized path. Subsequently, evolutionary strategy was integrated to iteratively optimize the path, using penalty functions to fully account for turning radius, path width, and obstacle avoidance constraints. The improved path planning method directly iterated on the spline curve path, avoiding the complexity and uncertainty of post smoothing processes, making the path more compatible with the motion characteristics of AGV. Simulation results demonstrats that the proposed method can reduce the actual path length and improve path safety and smoothness, validating its effectiveness. This approach establishes a general optimization framework by integrating other intelligent algorithms with A* algorithm, enabling more efficient path planning through direct iterative optimization of spline paths. |
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