  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.
