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Abstract

This paper proposes a unified optimization framework to solve the time-parameterization problem of humanoid-robot paths. Even though the time-parameterization problem is well known in robotics, the application to humanoid robots has not been addressed. This is because of the complexity of the kinematical structure as well as the dynamical motion equation. The main contribution of this paper is to show that the time parameterization of a statically stable path to be transformed into a dynamically stable trajectory within the humanoid-robot capacities can be expressed as an optimization problem. Furthermore, we propose an efficient method to solve the obtained optimization problem. The proposed method has been successfully validated on the humanoid robot HRP-2 by conducting several experiments. These results have revealed the effectiveness and the robustness of the proposed method.

Abstract

Magnetometers and accelerometers are sensors that are now integrated in objects of everyday life like automotive applications, mobile phones and so on. Some applications need information of acceleration and attitude with a high accuracy. For example, MEMS magnetometers and accelerometers can be integrated in embedded like mobile phones and GPS receivers. The parameters of such sensors must be precisely estimated to avoid drift and biased values. Thus, calibration is an important step to correctly use these sensors and get the expected measurements. This paper presents the theoretical and experimental steps of a method to compute gains, bias and non orthogonality factors of magnetometer and accelerometer sensors. This method of calibration can be used for automatic calibration in embedded systems. The calibration procedure involves arbitrary rotations of the sensors platform and a visual 2D projection of measurements.

Abstract

This paper explores the possibilities of using monocular simultaneous localization and mapping (SLAM) algorithms in systems with more than one camera. The idea is to combine in a single system the advantages of both monocular vision (bearings-only, infinite range observations but no 3-D instantaneous information) and stereovision (3-D information up to a limited range). Such a system should be able to instantaneously map nearby objects while still considering the bearing information provided by the observation of remote ones. We do this by considering each camera as an independent sensor rather than the entire set as a monolithic supersensor. The visual data are treated by monocular methods and fused by the SLAM filter. Several advantages naturally arise as interesting possibilities, such as the desynchronization of the firing of the sensors, the use of several unequal cameras, self-calibration, and cooperative SLAM with several independently moving cameras. We validate the approach with two different applications: a stereovision SLAM system with automatic self-calibration of the rig's main extrinsic parameters and a cooperative SLAM system with two independent free-moving cameras in an outdoor setting.

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Abstract

In this paper, the imitation of human recorded motions by a humanoid robot is considered. The main objective is to reproduce an imitated motion as closely as possible to the original human recored motion. To achieve this goal, the imitation problem is formulated as an optimization problem and the physical limits of the humanoid robot is considered as constraints. The optimization problem is then solved recursively by using an efficient dynamics algorithm, which allows the calculation of the gradient function with respect to the control parameters analytically. The simulation results using OpenHRP platform, which is a dynamical simulator for humanoid robot motions, have pointed out that the imitated motions preserve the salient characteristics of the original human captured motion and the optimization procedure converges well thanks to the analytical calculation of gradient function.

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