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，一种采用前向传播的多查询动力学运动规划方法。该算法能够同步探索机器人的状态空间与控制空间，从而在真实场景中实现更广泛的动态任务。我们的贡献包含三方面：i) 提出状态空间与控制空间的离散化方法，将规划时间分摊至多查询过程；ii) 采用惰性策略进行碰撞检测与控制序列传播，降低物理仿真计算成本；iii) 构建LazyBoE鲁棒动力学规划器，综合前两项贡献生成动力学可行轨迹。与现有方法相比，该框架不仅缩短规划时间，同时提升任务成功率。