融合自适应A*算法与轨迹优化的果园移动机器人路径规划方法

Path Planning Method of Orchard Mobile Robot Based on Adaptive A* Algorithm and Trajectory Optimization

摘要:

果园移动机器人自主安全作业的实现依赖于高效的路径规划技术。 针对现有方法在复杂果园环境中普遍存在的规划效率低、路径折点多、平滑性差等问题，提出一种融合自适应A*算法与轨迹优化的自主路径规划方法，以提升果园移动机器人的自主导航与作业性能。首先，构建果园栅格地图模型作为全局规划基础；其次，采用实时优化的代价权重-中心线偏移双函数协同机制增强自适应A*算法，并设计动态5邻域搜索策略进行全局路径搜索；随后，应用三阶贝塞尔曲线进行路径自适应平滑处理，生成满足果园机器人作业要求的曲率连续导航轨迹。基于典型果园环境的仿真试验结果表明，相较于改进A*算法，本方法在无障碍与有障碍场景下，平均路径规划时间分别降低了23.8%(17.15ms)和23.1%(16.09ms)，路径平均曲率分别降低了10.7%(0.011m-1)和15.8%(0.028m-1)。实地试验表明，平均搜索规划时间分别减少了26.4%(19.01ms)和27.4%(21.28ms)，路径平均曲率分别降低了7.3%(0.009m-1)和8.7%(0.013m-1)。该方法显著提升了路径规划效率与平滑性，有效满足果园机器人实际作业需求，具备良好的实用价值。

Abstract:

The realization of autonomous and safe operation for orchard mobile robots relies critically on efficient path planning technology. To address the prevalent challenges of low planning efficiency, excessive path turning points, and poor smoothness in existing path planning methods within complex orchard environments, an autonomous path planning method was proposed, integrating an adaptive A* algorithm with trajectory optimization, improving the autonomous navigation and operation performance of robots. Firstly, an orchard grid map model was constructed as the foundation for global planning. Secondly, a real-time optimization mechanism combining cost-weighted and centerline offset functions enhanced the adaptive A* algorithm, and a dynamic five-neighborhood search strategy was introduced for comprehensive global path searching. Subsequently, third-order Bézier curves were applied for adaptive path smoothing, generating a curvature-continuous navigation trajectory that met the operational requirements of orchard robots. Simulation and field experiments conducted in representative orchard environments demonstrated that compared with an improved A* algorithm, the proposed method significantly reduced the average path planning time by 23.8% (17.15ms) and 23.1% (16.09ms) in obstacle-free and obstacle-present scenarios, respectively, while achieving a reduction in path average curvature by 10.7% (0.011m-1) and 15.8% (0.028m-1). Field tests further validated the reductions in average planning time by 26.4% (19.01ms) and 27.4% (21.28ms), along with decreases in path average curvature by 7.3% (0.009m-1) and 8.7% (0.013m-1) under the respective scenarios. The proposed method significantly enhanced path planning efficiency and smoothness, effectively meeting the practical operational demands of orchard robots and demonstrating strong potential for practical application.