S2P2：基于RGB-D数据的轮椅机器人自监督目标导向路径规划
S2P2: Self-Supervised Goal-Directed Path Planning Using RGB-D Data for Robotic Wheelchairs
摘要：
路径规划是轮椅机器人实现自主导航的基本能力。近年来，随着深度学习技术的飞速发展，基于模仿学习的路径规划方法取得了很好的效果。然而，这些方法的缺点是双重的：1）它们可能需要大量的时间和劳动来记录专家演示作为训练数据;以及2）现有方法只能接收高级命令，例如向左/向右转弯。这些命令可能不足以用于移动机器人的导航（例如，机器人轮椅），这通常需要准确的姿势的目标。我们提出了S2 P2，一个自我监督的目标导向的路径规划方法，有助于解决这个问题。具体来说，我们开发了一个管道来自动生成作为输入RGB-D图像和目标姿势的规划路径标签。然后，我们提出了一个最佳拟合回归平面损失训练我们的数据驱动的路径规划模型的基础上产生的标签。我们的S2 P2不需要预先构建的地图，但它可以通过我们的框架集成到现有的基于地图的导航系统中。实验结果表明，我们的S2 P2优于传统的路径规划算法，并增加了现有的基于地图的导航系统的鲁棒性。我们的项目页面可在https://sites.google.com/view/s2p2上找到。
英文摘要：
Path planning is a fundamental capability for autonomous navigation of robotic wheelchairs. With the impressive development of deep-learning technologies, imitation learning-based path planning approaches have achieved effective results in recent years. However, the disadvantages of these approaches are twofold: 1) they may need extensive time and labor to record expert demonstrations as training data; and 2) existing approaches could only receive high-level commands, such as turning left/right. These commands could be less sufficient for the navigation of mobile robots (e.g., robotic wheelchairs), which usually require exact poses of goals. We contribute a solution to this problem by proposing S2P2, a self-supervised goal-directed path planning approach. Specifically, we develop a pipeline to automatically generate planned path labels given as input RGB-D images and poses of goals. Then, we present a best-fit regression plane loss to train our data-driven path planning model based on the generated labels. Our S2P2 does not need pre-built maps, but it can be integrated into existing map-based navigation systems through our framework. Experimental results show that our S2P2 outperforms traditional path planning algorithms, and increases the robustness of existing map-based navigation systems. Our project page is available at https://sites.google.com/view/s2p2.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
