论文标题
Deep-Panther：动态环境中基于学习的感知感知轨迹计划者
Deep-PANTHER: Learning-Based Perception-Aware Trajectory Planner in Dynamic Environments
论文作者
论文摘要
本文介绍了Deep-Panther，这是一种基于学习的感知感知的轨迹计划者，用于动态环境中无人驾驶汽车（UAV）。鉴于无人机的当前状态以及障碍物的预测轨迹和大小，深层生成了多个轨迹，以避免动态障碍物，同时同时最大限度地在车载相机的视野（FOV）中最大化。为了获得可计算上的实时解决方案，使用基于多模式优化的专家提供的演示来利用模仿学习来训练深层策略。广泛的模拟显示，重建时间比基于优化的专家快两个数量级，同时达到了类似的成本。通过确保将每个专家轨迹分配到损失函数中的一个不同的学生轨迹中，Deep-Panther还可以捕获问题的多模式，并相对于专家的平均平方误差（MSE）损失，而专家的损失（MSE）是最长达18倍的胜利（放松）的赢家赢家。还证明了深层钢丝与训练中使用的障碍轨迹有很好的概括。
This paper presents Deep-PANTHER, a learning-based perception-aware trajectory planner for unmanned aerial vehicles (UAVs) in dynamic environments. Given the current state of the UAV, and the predicted trajectory and size of the obstacle, Deep-PANTHER generates multiple trajectories to avoid a dynamic obstacle while simultaneously maximizing its presence in the field of view (FOV) of the onboard camera. To obtain a computationally tractable real-time solution, imitation learning is leveraged to train a Deep-PANTHER policy using demonstrations provided by a multimodal optimization-based expert. Extensive simulations show replanning times that are two orders of magnitude faster than the optimization-based expert, while achieving a similar cost. By ensuring that each expert trajectory is assigned to one distinct student trajectory in the loss function, Deep-PANTHER can also capture the multimodality of the problem and achieve a mean squared error (MSE) loss with respect to the expert that is up to 18 times smaller than state-of-the-art (Relaxed) Winner-Takes-All approaches. Deep-PANTHER is also shown to generalize well to obstacle trajectories that differ from the ones used in training.
