Kim Eunwoo, Choi Sungjoon, Oh Songhwai
Department of Electrical and Computer Engineering and ASRI, Seoul National University, Seoul 08826, Korea.
Sensors (Basel). 2018 Feb 14;18(2):582. doi: 10.3390/s18020582.
This paper considers two important problems for autonomous robot navigation in a dynamic environment, where the goal is to predict pedestrian motion and control a robot with the prediction for safe navigation. While there are several methods for predicting the motion of a pedestrian and controlling a robot to avoid incoming pedestrians, it is still difficult to safely navigate in a dynamic environment due to challenges, such as the varying quality and complexity of training data with unwanted noises. This paper addresses these challenges simultaneously by proposing a robust kernel subspace learning algorithm based on the recent advances in nuclear-norm and l 1 -norm minimization. We model the motion of a pedestrian and the robot controller using Gaussian processes. The proposed method efficiently approximates a kernel matrix used in Gaussian process regression by learning low-rank structured matrix (with symmetric positive semi-definiteness) to find an orthogonal basis, which eliminates the effects of erroneous and inconsistent data. Based on structured kernel subspace learning, we propose a robust motion model and motion controller for safe navigation in dynamic environments. We evaluate the proposed robust kernel learning in various tasks, including regression, motion prediction, and motion control problems, and demonstrate that the proposed learning-based systems are robust against outliers and outperform existing regression and navigation methods.
本文考虑了动态环境中自主机器人导航的两个重要问题，其目标是预测行人运动并利用该预测来控制机器人以实现安全导航。虽然有多种方法可用于预测行人运动以及控制机器人以避开迎面而来的行人，但由于存在诸如训练数据质量参差不齐且带有有害噪声、数据复杂等挑战，在动态环境中进行安全导航仍然困难重重。本文基于核范数和 l1 范数最小化的最新进展，提出了一种鲁棒核子空间学习算法，同时应对这些挑战。我们使用高斯过程对行人运动和机器人控制器进行建模。所提出的方法通过学习低秩结构化矩阵（具有对称半正定性）来找到正交基，从而有效地逼近高斯过程回归中使用的核矩阵，消除错误和不一致数据的影响。基于结构化核子空间学习，我们提出了一种用于动态环境中安全导航的鲁棒运动模型和运动控制器。我们在包括回归、运动预测和运动控制问题在内的各种任务中评估所提出的鲁棒核学习，并证明所提出的基于学习的系统对异常值具有鲁棒性，且优于现有的回归和导航方法。
