Paper Info
Title
Real-time nonparametric reactive navigation of mobile robots in dynamic environments.
Abstract
In this paper, we propose a nonparametric motion controller using Gaussian process regression for autonomous navigation in a dynamic environment. Particularly, we focus on its applicability to low-cost mobile robot platforms with low-performance processors. The proposed motion controller predicts future trajectories of pedestrians using the partially-observable egocentric view of a robot and controls a robot using both observed and predicted trajectories. Furthermore, a hierarchical motion controller is proposed by dividing the controller into multiple sub-controllers using a mixture-of-experts framework to further alleviate the computational cost. We also derive an efficient method to approximate the upper bound of the learning curve of Gaussian process regression, which can be used to determine the required number of training samples for the desired performance. The performance of the proposed method is extensively evaluated in simulations and validated experimentally using a Pioneer 3DX mobile robot with two Microsoft Kinect sensors. In particular, the proposed baseline and hierarchical motion controllers show over 65% and 51% improvements over a reactive planner and predictive vector field histogram, respectively, in terms of the collision rate.
Year
DOI
Venue
2017
10.1016/j.robot.2016.12.003
Robotics and Autonomous Systems
Keywords
Field
DocType
Autonomous navigation,Dynamic environments,Gaussian process regression,And learning curve of Gaussian process regression
Kriging,Vector Field Histogram,Computer vision,Control theory,Upper and lower bounds,Simulation,Computer science,Nonparametric statistics,Artificial intelligence,Motion controller,Robot,Mobile robot
Journal
Volume
Issue
ISSN
91
C
0921-8890
Citations 
PageRank 
References 
5
0.47
19
Authors
4
Name
Order
Citations
PageRank
Sungjoon Choi17412.52
Eunwoo Kim2385.81
Kyungjae Lee3218.23
Songhwai Oh475567.68