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Open AccessArticle

Structured Kernel Subspace Learning for Autonomous Robot Navigation

Department of Electrical and Computer Engineering and ASRI, Seoul National University, Seoul 08826, Korea
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in the 2015 IEEE International Conference on Robotics and Automation (ICRA).
Sensors 2018, 18(2), 582;
Received: 9 January 2018 / Revised: 6 February 2018 / Accepted: 12 February 2018 / Published: 14 February 2018
(This article belongs to the Special Issue Smart Sensors for Mechatronic and Robotic Systems)
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. View Full-Text
Keywords: kernel subspace learning; low-rank approximation; Gaussian processes; motion prediction; motion control kernel subspace learning; low-rank approximation; Gaussian processes; motion prediction; motion control
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Kim, E.; Choi, S.; Oh, S. Structured Kernel Subspace Learning for Autonomous Robot Navigation. Sensors 2018, 18, 582.

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