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首页> 外文期刊>IEEE Robotics and Automation Letters >Enhancing Lattice-Based Motion Planning With Introspective Learning and Reasoning
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Enhancing Lattice-Based Motion Planning With Introspective Learning and Reasoning

机译：用内省学习和推理提高基于格子的运动规划

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摘要

Lattice-based motion planning is a hybrid planning method where a plan is made up of discrete actions, while simultaneously also being 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 planning 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 machine learning techniques with explicit uncertainty quantification, for safe usage in safety-critical applications. By increasing the model accuracy the safety margins can be reduced while maintaining the same safety as before. Reasoning takes place to both verify that the learned models stays safe and to improve collision checking effectiveness in the motion planner using more accurate execution predictions with a smaller safety margin. The presented approach allows for explicit awareness of controller performance under normal circumstances, and detection of incorrect performance in abnormal circumstances. Evaluation is made on the nonlinear dynamics of a quadcopter in 3D using simulation.
机译：基于格子的运动规划是一种混合计划方法，其中计划由离散行动组成，同时也是一种物理可行的轨迹。规划考虑了离散和连续的方面，例如在配置空间中的动作预处理和无碰撞动作持续时间。安全运动规划依赖于校准的安全性边缘进行碰撞检查。轨迹跟踪控制器必须进一步能够可靠地在本安全范围内执行动作，以便执行安全。在这项工作中，我们关注的是关于控制器性能随着时间的推移的内省学习和推理。使用具有明确不确定性量化的机器学习技术来学习正常控制器的执行，以便安全使用安全关键应用。通过提高模型精度，可以减少安全边距，同时保持与之前相同的安全性。验证学习模式是否安全地进行推理，并使用具有较小安全裕度的更准确的执行预测来改善运动计划者中的碰撞检查效能。所提出的方法允许在正常情况下明确对控制器性能的认识，并在异常情况下检测不正确的性能。使用模拟在3D中的四桥器的非线性动态进行评估。

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