Modelling and Identification in Robotics

Kozłowski, Krzysztof

doi:10.1007/978-1-4471-0429-2

Cited by 130 publications

(115 citation statements)

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“…An interesting aspect of this approach is that the same evolutionary algorithm which is used for gait learning can also be re-used to identify the simulated robot parameters, making the whole method more effective from the point of view of the programming effort required to cross the reality gap. Although there are many system identification techniques used in robotics (Kozlowski, 1998), which are based on exact mathematical models of robots, such a model of a multi-legged walking machine, even if identified successfully, would be infeasible for our purposes. We are searching for a set of parameters that enable the much simplified simulated model to mimic the real robot behaviour within the chosen class and range of control signals.…”

Section: Evolutionary Identification Of the Simulation Modelmentioning

confidence: 99%

A biologically inspired approach to feasible gait learning for a hexapod robot

Belter¹,

Skrzypczyński²

2010

International Journal of Applied Mathematics and Computer Science

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The objective of this paper is to develop feasible gait patterns that could be used to control a real hexapod walking robot. These gaits should enable the fastest movement that is possible with the given robot's mechanics and drives on a flat terrain. Biological inspirations are commonly used in the design of walking robots and their control algorithms. However, legged robots differ significantly from their biological counterparts. Hence we believe that gait patterns should be learned using the robot or its simulation model rather than copied from insect behaviour. However, as we have found tahula rasa learning ineffective in this case due to the large and complicated search space, we adopt a different strategy: in a series of simulations we show how a progressive reduction of the permissible search space for the leg movements leads to the evolution of effective gait patterns. This strategy enables the evolutionary algorithm to discover proper leg co-ordination rules for a hexapod robot, using only simple dependencies between the states of the legs and a simple fitness function. The dependencies used are inspired by typical insect behaviour, although we show that all the introduced rules emerge also naturally in the evolved gait patterns. Finally, the gaits evolved in simulations are shown to be effective in experiments on a real walking robot.

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Section: Evolutionary Identification Of the Simulation Modelmentioning

confidence: 99%

A biologically inspired approach to feasible gait learning for a hexapod robot

Belter¹,

Skrzypczyński²

2010

International Journal of Applied Mathematics and Computer Science

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“…For this purpose, the dynamic model can be developed basically by two methods (Kozlowski, 1998); the integral and the differential methods. The integral method is derived from the energy equation and requires measurements of positions, velocities and applied forces on the actuated joints.…”

Section: Dynamic Modelmentioning

confidence: 99%

Dynamic Parameter Identification for Parallel Manipulators

Mata¹,

Farhat²,

Díaz‐Rodríguez³

et al. 2008

Parallel Manipulators, Towards New Applications

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“…no stiction is considered. Lagrange equations are applied for each link i = 1, · · · , n, (Bona, 2002;Kozlowzki, 1998) d dt…”

Section: The Robot Modelmentioning

confidence: 99%

“…Since the robot producers very rarely provide this information, users need to follow a reliable procedure allowing to recover a model valid in the frequency range of interest. The dynamic model of a robot depends on such parameters as links inertia, mass and center of mass, but only a subset of them, called Base Parameters (Gautier and Khalil, 1990;Gautier and Khalil, 1988;Mayeda et al, 1990;Pham and Gautier, 1991;Kozlowzki, 1998), must be estimated to avoid a rapid rise in computational complexity when the number of degrees-of-freedom (DoF) grows. Many identification methods exist for system operating in open loop (Ljung, 1987;Forssell and Ljung, 1999;Forssell and Ljung, 2000;Sun et al, 2000;Welsh and Goodwin, 2002), but several of them fail when the system is under closed-loop action, as in the robot case, where it cannot operate without an active controller, for safety reasons.…”

Section: Introductionmentioning

confidence: 99%

Closed-Loop Rigid Model Identification of an Industrial Robot

Bona

Curatella

2005

IFAC Proceedings Volumes

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The identification process aimed at estimating the model parameters of a COMAU Smart S2 industrial robot for controller design purposes is presented. The principal challenges include the existence of a controller-in-the-loop and the absence of joint sensors for acceleration and velocity measurements. A method valid in this context has been applied, and suitable trajectories were generated to avoid the excitation of the unmodelled dynamics. This method was applied to an industrial robot, its parameters estimated and used for the design of a model-based controller.

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Modelling and Identification in Robotics

Cited by 130 publications

References 0 publications

A biologically inspired approach to feasible gait learning for a hexapod robot

A biologically inspired approach to feasible gait learning for a hexapod robot

Dynamic Parameter Identification for Parallel Manipulators

Closed-Loop Rigid Model Identification of an Industrial Robot

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