Gaussian mixture spline trajectory: learning from a dataset, generating trajectories without one. (19th May 2018)

• Record Type: Journal Article
• Authors: Barbié, T.; Kabutan, R.; Tanaka, R.; Nishida, T.
• Is Part Of: Advanced robotics. Volume 32:Number 10(2018)
• Page Start: 547
• Page End: 558
• Publication Date: 2018-05-19
• Subjects: Trajectory prediction -- Gaussian mixture model -- dataset learning; Robotics -- Periodicals; Robotics -- Japan -- Periodicals; Robotics; Japan; Periodicals; 629.89205
• DOI: 10.1080/01691864.2018.1465849
• Languages: English
• ISSNs: 0169-1864

Abstract

Abstract : Most optimization-based motion planners use a naive linear initialization, which does not use previous planning experience. We present an algorithm called 'Gaussian mixture spline trajectory' (GMST) that leverages motion datasets for generating trajectories for new planning problems. Unlike other trajectory prediction algorithms, our method does not retrieve trajectories from a dataset. Instead, it first uses a Gaussian mixture model (GMM) to modelize the likelihood of the trajectories to be inside the dataset and then uses the GMM's parameters to generate new trajectories. As the use of the dataset is restricted only to the learning phase it can take advantage of very large datasets. Using both abstract and robot system planning problems, we show that the GMST algorithm decreases the computation time and number of iterations of optimization-based planners while increasing their success rates as compared to that obtained with linear initialization. GRAPHICAL ABSTRACT: