Exciting trajectories for the identification of base inertial parameters of robots

Gautier, Maxime; Khalil, Wisama

doi:10.1109/cdc.1991.261353

Cited by 115 publications

(124 citation statements)

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“…For learning inverse dynamics, for example, rich data can be sampled from trajectories by approximately executing desired random point-to-point and rhythmic movements (Schaal and Sternad 1998;Swevers et al 1997). 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.g., constraints on joint angles, velocity and acceleration). These approaches distinguish in the way of the trajectory parameterization.…”

Section: Data Challengesmentioning

confidence: 98%

Model learning for robot control: a survey

2011

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Models are among the most essential tools in robotics, such as kinematics and dynamics models of the robot's own body and controllable external objects. It is widely believed that intelligent mammals also rely on internal models in order to generate their actions. However, while classical robotics relies on manually generated models that are based on human insights into physics, future autonomous, cognitive robots need to be able to automatically generate models that are based on information which is extracted from the data streams accessible to the robot. In this paper, we survey the progress in model learning with a strong focus on robot control on a kinematic as well as dynamical level. Here, a model describes essential information about the behavior of the environment and the influence of an agent on this environment. In the context of model-based learning control, we view the model from three different perspectives. First, we need to study the different possible model learning architectures for robotics. Second, we discuss what kind of problems these architecture and the domain of robotics imply for the applicable learning methods. From this discussion, we deduce future directions of real-time learning algorithms. Third, we show where these scenarios have been used successfully in several case studies.

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Section: Data Challengesmentioning

confidence: 98%

Model learning for robot control: a survey

2011

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“…Two well-accepted criteria for choosing the objective function are: 1) The condition number of the observation matrix κ(W) [24], 2) To maximize a scalar measurement of the information matrix, or 3) Multicriteria approach [27]. For the proposed methodology the condition number of the observation matrix κ(W) was selected as the objective function.…”

Section: Design Of Trajectoriesmentioning

confidence: 99%

“…Thus, an important step in the identification process is the selection of the trajectory to be performed by the robot. The design of trajectories can be seen as an optimization process where a parameterized trajectory is optimized considering as an objective function a criterion for improving the excitation of the dynamic parameters [24]. Constraint equations caused by the joint movements and reachable robot workspace are included as restrictions in the optimization process.…”

Section: Design Of Trajectoriesmentioning

confidence: 99%

A methodology for dynamic parameters identification of 3-DOF parallel robots in terms of relevant parameters

Díaz‐Rodríguez

Mata²,

Valera

et al. 2010

Mechanism and Machine Theory

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“…As previously mentioned, the condition number of a matrix can be considered as an upper limit for inputoutput error transmissibility, see [11,12],…”

Section: Trajectory Optimizationmentioning

confidence: 99%

“…It is important to emphasize that the configurations for which measurements are taken must correspond to a well-conditioned reduced observation matrix since the condition number represents an upper limit for input/ output error transmissibility [11,12]. It must be emphasized that the use of the condition number of the observation matrix is subject to statistical assumptions as was pointed out in Presse and Gautier [13].…”

Section: Introductionmentioning

confidence: 99%