动态避障的图规划算法，让自动泊车更安全高效
Graph-based Path Planning with Dynamic Obstacle Avoidance for Autonomous Parking
- 基于改进的时序化混合A*算法，实时融合动态障碍物预测
- 仿真中相比现有方法效率提升显著，路径更安全无碰撞
- 适合需要实时避障的自动驾驶泊车系统开发
在充满静态与动态障碍物的复杂停车环境中，实现安全高效的路径规划仍是重大挑战。为此，我们提出一种新型且计算高效的规划策略，将动态障碍物预测无缝集成到规划过程中，确保生成无碰撞路径。该方法在传统混合A*算法基础上引入时序索引版本，在图节点探索阶段显式考虑动态障碍物的预测信息，从而实现动态避障。我们将时序化混合A*算法嵌入在线规划框架，在每个规划步骤中计算局部路径，并由自适应选择的中间目标引导。所提方法在垂直、斜角及平行泊车等多种场景下进行了验证。仿真结果表明，相比当前主流的基于样条的泊车规划方法，该方法在效率和安全性方面均有显著提升。
原文摘要 · Abstract (English)
Safe and efficient path planning in parking scenarios presents a significant challenge due to the presence of cluttered environments filled with static and dynamic obstacles. To address this, we propose a novel and computationally efficient planning strategy that seamlessly integrates the predictions of dynamic obstacles into the planning process, ensuring the generation of collision-free paths. Our approach builds upon the conventional Hybrid A star algorithm by introducing a time-indexed variant that explicitly accounts for the predictions of dynamic obstacles during node exploration in the graph, thus enabling dynamic obstacle avoidance. We integrate the time-indexed Hybrid A star algorithm within an online planning framework to compute local paths at each planning step, guided by an adaptively chosen intermediate goal. The proposed method is validated in diverse parking scenarios, including perpendicular, angled, and parallel parking. Through simulations, we showcase our approach's potential in greatly improving the efficiency and safety when compared to the state of the art spline-based planning method for parking situations.
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