- author = "Seungsu Kim and Aude Billard",
- title = "Estimating the non-linear dynamics of free-flying objects",
- journal = "Robotics and Autonomous Systems",
- volume = "60",
- number = "9",
- pages = "1108--1122",
- year = "2012",
- ISSN = "0921-8890",
-
DOI = "
doi:10.1016/j.robot.2012.05.022",
-
URL = "
http://www.sciencedirect.com/science/article/pii/S092188901200084X",
- keywords = "genetic algorithms, genetic programming, Machine learning, Dynamical systems",
-
abstract = "This paper develops a model-free method to estimate
the dynamics of free-flying objects. We take a
realistic perspective to the problem and investigate
tracking accurately and very rapidly the trajectory and
orientation of an object so as to catch it in flight.
We consider the dynamics of complex objects where the
grasping point is not located at the centre of mass.
To achieve this, a density estimate of the translational and rotational velocity is built based on the trajectories of various examples. We contrast the performance of six non-linear regression methods (Support Vector Regression (SVR) with Radial Basis Function (RBF) kernel, SVR with polynomial kernel, Gaussian Mixture Regression (GMR), Echo State Network (ESN), Genetic Programming (GP) and Locally Weighted Projection Regression (LWPR)) in terms of precision of recall, computational cost and sensitivity to choice of hyper-parameters. We validate the approach for real-time motion tracking of 5 daily life objects with complex dynamics (a ball, a fully-filled bottle, a half-filled bottle, a hammer and a pingpong racket). To enable real-time tracking, the estimated model of the object's dynamics is coupled with an Extended Kalman Filter for robustness against noisy sensing.",
- notes = "GPTIPS",
