Exciting Trajectories for the Identification of Base Inertial Parameters of Robots

By Maxime Gautier, W. Khalil. 1991

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Citations in the corpus

Model-based reinforcement learning with parametrized physical models and optimism-driven exploration (2015, background): “citation trajectories for model identification [5], [14], [25], [28], [31], [32].”
Robust dynamic experimental identification of robots with set membership uncertainty (2005, methodology): “The joint position q and the current referenceV (control input) are collected at a 100Hz sample rate while the robot is tracking a fifth order polynomial trajectory, computed off-line through an optimization procedure to ensure a good condition number for the observation matrix [10].”
Optimal robot excitation and identification (1997, methodology): “Adjusting the trajectory to fit the motion constraints involves trial-and-error and is hardly discussed in [2] and [3].”
Model learning for robot control: a survey (2011, background): “In other approaches (Gautier and Khalil 1992; Otani and Kakizaki 1993), the generation of an optimal robot excitation trajectory involves nonlinear optimization with motion constraints (e.”
Adaptive Reactionless Motion and Parameter Identification in Postcapture of Space Debris (2013, background): “The issue of persistent excitation of input trajectories for a robotic manipulator has been tackled extensively in the context of robot dynamics identification [30–32].”
Robot excitation trajectories for dynamic parameter estimation using optimized B-splines (2012, background): “In [2], [3], [4] and [5] the condition number of the dynamic energy model is optimized off-line to obtain a set of optimum points.”
Physical feasibility of robot base inertial parameter identification: A linear matrix inequality approach (2014, background): “The regression matrix has a condition number of 99, therefore it can be considered well conditioned (Gautier and Khalil, 1992).”
Fast planning of well conditioned trajectories for model learning (2014, background): “[4] also deals with generating well conditioned trajectories for learning multibody dynamic models.”