Learning Inverse Kinodynamics for Accurate High-Speed Off-Road Navigation on Unstructured Terrain
Learning Inverse Kinodynamics for Accurate High-Speed Off-Road Navigation on Unstructured Terrain
复制标题
学习逆运动动力学以在非结构化地形上进行精确的高速越野导航
DOI：
10.1109/lra.2021.3090023
复制
发表时间：
2021
影响因子：
5.2
通讯作者：
P. Stone
中科院分区：
文献类型：
--
作者：
Xuesu Xiao;Joydeep Biswas;P. Stone
这封信提出了一种基于学习的方法来考虑不可观察的世界状态在Kinodynamic运动规划中的影响，以便在非结构化地形上实现准确的高速越野导航。现有的Kinodynamic运动规划器在结构化和均匀的环境中操作，因此不需要明确地考虑地形车辆的相互作用，或者假设一组离散的地形类。然而，当在非结构化地形上运行时，特别是在高速下，即使是环境的微小变化也会被放大，并导致计划执行不准确。在这封信中，捕捉复杂的Kinodynamic模型和数学上未知的世界状态，我们学习一个Kinodynamic规划器在数据驱动的方式与板载惯性观测。我们的方法在不同的室内和室外环境中的物理机器人上进行了测试，实现了快速准确的越野导航，并优于与环境无关的替代方案，在高速行驶时，计划执行成功率提高了52.4%至86.9%。
This letter presents a learning-based approach to consider the effect of unobservable world states in kinodynamic motion planning in order to enable accurate high-speed off-road navigation on unstructured terrain. Existing kinodynamic motion planners either operate in structured and homogeneous environments and thus do not need to explicitly account for terrain-vehicle interaction, or assume a set of discrete terrain classes. However, when operating on unstructured terrain, especially at high speeds, even small variations in the environment will be magnified and cause inaccurate plan execution. In this letter, to capture the complex kinodynamic model and mathematically unknown world state, we learn a kinodynamic planner in a data-driven manner with onboard inertial observations. Our approach is tested on a physical robot in different indoor and outdoor environments, enables fast and accurate off-road navigation, and outperforms environment-independent alternatives, demonstrating 52.4% to 86.9% improvement in terms of plan execution success rate while traveling at high speeds.
影响因子：
5.2
作者：
Liu, Bo;Xiao, Xuesu;Stone, Peter
通讯作者：
Stone, Peter