In this work, we introduce LazyBoE, a multi-query method for kinodynamic motion planning with forward propagation. This algorithm allows for the simultaneous exploration of a robot's state and control spaces, thereby enabling a wider suite of dynamic tasks in real-world applications. Our contributions are three-fold: i) a method for discretizing the state and control spaces to amortize planning times across multiple queries; ii) lazy approaches to collision checking and propagation of control sequences that decrease the cost of physics-based simulation; and iii) LazyBoE, a robust kinodynamic planner that leverages these two contributions to produce dynamically-feasible trajectories. The proposed framework not only reduces planning time but also increases success rate in comparison to previous approaches.

翻译：本文提出LazyBoE，一种基于前向传播的多查询运动动力学规划方法。该算法能够同时探索机器人的状态空间与控制空间，从而在实际应用中支持更广泛的动态任务。我们的贡献包括三个方面：i) 提出状态空间与控制空间的离散化方法，以分摊多查询场景下的规划时间；ii) 采用惰性碰撞检测与控制序列传播策略，降低基于物理仿真的计算开销；iii) 开发LazyBoE鲁棒运动动力学规划器，融合上述两项贡献以生成动态可行的轨迹。与现有方法相比，该框架不仅减少了规划时间，同时显著提升了任务成功率。