Generating Manipulation Trajectories Using Motion Harmonics
Document Type
Conference Proceeding
Publication Date
9-2015
Keywords
trajectory, harmonic analysis, hidden Markov models, eigenvalues and eigenfunctions, robots, computational modeling, principal component analysis, open motion planning library, manipulation trajectory generating algorithm, motion harmonics, user defined constraints, functional eigenanalysis, NAO robot, parabolic blend approach
Digital Object Identifier (DOI)
https://doi.org/10.1109/IROS.2015.7354073
Abstract
This paper presents a novel manipulation trajectory generating algorithm that constructs trajectories from learned motion harmonics and user defined constraints. The algorithm uses functional eigenanalysis to learn motion harmonics from demonstrated motions and then use the motion harmonics to compute the optimal trajectory that resembles the demonstrated motions and also satisfies the constraints. The algorithm has been tested on five real human motion data sets to obtain motion harmonics and then generate motions of each task for a NAO robot. The generated trajectories were compared with the trajectories generated using linear segment with parabolic blend approach and with the Open Motion Planning Library. The approach can also work with motion planners.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, p. 4949-4954.
Scholar Commons Citation
Huang, Yongqiang and Sun, Yu, "Generating Manipulation Trajectories Using Motion Harmonics" (2015). Computer Science and Engineering Faculty Publications. 80.
https://digitalcommons.usf.edu/esb_facpub/80
