International audienceScene flow represents the 3-D motion of points in the scene, just as optical flow is related to their 2-D motion in the images. As opposed to classical methods which compute scene flow from optical flow, we propose to compute it by tracking 3-D points and surface elements (surfels) in a multi-camera setup (at least two cameras are needed). Two methods are proposed: in the first one, the translation of each 3-D point is found by matching the neighborhoods of its 2-D projections in each camera between two time steps; in the second one, the full pose of a surfel is recovered by matching the image of its projection with a texture template attached to the surfel, and visibility changes caused by occlusion or rotation of surfels are handled. Both methods detect lost or untrackable points and surfels. They were designed for real-time execution and can be used for fast extraction of scene flow from multi-camera sequences

No abstract

This paper deals with minimum time trajectory optimization along a specified path subject to thermal constraints. We point out here that robots are often integrated into complex robotic cells, and the interactions between the robot and its environment are often difficult or even impossible to model. The structure of the optimization problem allows us to decompose the optimization into two levels, the first being based on models and results of the theory of the calculus of variations, the second being based on measurements and derivative free algorithms. This decomposition allows us to optimize the velocity profiles efficiently without any advance knowledge of the interactions between the robot and its environment. We propose here two numerical algorithms for these two levels of the decomposition which show good convergence properties. The resulting optimal velocity profiles are 5-10% faster than classical profiles, and have been executed successfully on a real Stäubli Rx90 manipulator robot.

We propose here to deal with the optimization of velocity profiles of manipulator robots with a minimum time criterion subject to thermal constraints. This paper deals with the real impact of thermal limitations on optimal velocity profiles and the methods to calculate the corresponding optimal trajectories. We first calculate analytically the optimal solution in a simple case in order to verify the validity of the numerical algorithm and also to present a general methodology to calculate optimal trajectories in robotics using results from the theory of calculus of variations and not from the theory of optimal control. We derive then a numerical algorithm based on the discretization of the time law through an interpolation with non uniform cubic splines. This algorithm shows robust and efficient convergence properties and the trajectories thus generated were executed successfully on a Stäubli Rx90.

This paper deals with optimizing the task cycle time of industrial robots integrated in complex robot cells. Trajectory optimizers are usually based on models and can't properly deal with uncertainties due to interactions between the robot and its environment. We propose here a trajectory optimizer with hardware in the loop which can take into account constraints such as maximum authorized temperature and maximum authorized torque. Our approach is based on unconstrained optimization algorithms without derivatives and penalty methods. Experiments on real industrial applications showed good robustness properties of this algorithm even with a high number of parameters and with changes of the robot task.