论文标题
通过内省学习和推理增强基于格子的运动计划
Enhancing Lattice-based Motion Planning with Introspective Learning and Reasoning
论文作者
论文摘要
基于晶格的运动计划是一种混合计划方法，其中由离散动作组成的计划同时是一种物理上可行的轨迹。该计划同时考虑了离散的方面和连续方面，例如在配置空间中的行动前条件和无碰撞动作效果。安全运动策划依靠精心校准的安全 - 利润来进行碰撞检查。轨迹跟踪控制器必须进一步能够可靠地执行此安全余量内的动议，以使执行安全。在这项工作中，我们关注的是随着时间的推移，内省的学习和关于控制器绩效的推理。使用可靠和不确定性的机器学习技术学习了正常的控制器执行不同动作。通过纠正执行偏见，我们设法大大降低了运动动作的安全范围。进行推理是为了验证学习模型是否安全并通过使用更准确的执行预测和较小的安全保证金来提高运动计划者的碰撞检查效果。提出的方法允许在正常情况下明确了解控制器性能，并及时检测在异常情况下的性能。使用模拟对3D中四轮驱动器的非线性动力学进行评估。视频：https：//youtu.be/stmzduvsumm
Lattice-based motion planning is a hybrid planning method where a plan made up of discrete actions simultaneously is a physically feasible trajectory. The planning takes both discrete and continuous aspects into account, for example action pre-conditions and collision-free action-duration in the configuration space. Safe motion planing rely on well-calibrated safety-margins for collision checking. The trajectory tracking controller must further be able to reliably execute the motions within this safety margin for the execution to be safe. In this work we are concerned with introspective learning and reasoning about controller performance over time. Normal controller execution of the different actions is learned using reliable and uncertainty-aware machine learning techniques. By correcting for execution bias we manage to substantially reduce the safety margin of motion actions. Reasoning takes place to both verify that the learned models stays safe and to improve collision checking effectiveness in the motion planner by the use of more accurate execution predictions with a smaller safety margin. The presented approach allows for explicit awareness of controller performance under normal circumstances, and timely detection of incorrect performance in abnormal circumstances. Evaluation is made on the nonlinear dynamics of a quadcopter in 3D using simulation. Video: https://youtu.be/STmZduvSUMM
