Letteratura scientifica selezionata sul tema "Transfert d'effort"
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Articoli di riviste sul tema "Transfert d'effort":
Mitton, C., Y. C. MacNab, N. Smith e L. Foster. "Données relatives aux blessures en Colombie‑Britannique : opinion des décideurs en ce qui concerne le transfert de connaissances". Maladies chroniques au Canada 29, n. 2 (2009): 78–88. http://dx.doi.org/10.24095/hpcdp.29.2.05f.
Garfinkel, Irwin, e Sara S. McLanahan. "Les enfants des mères seules : précarité économique et politiques sociales". Articles 23, n. 2 (25 marzo 2004): 179–206. http://dx.doi.org/10.7202/010170ar.
Tesi sul tema "Transfert d'effort":
Nazeer, Sebastien. "Conception et réalisation de micro-capteurs de pression pour l'instrumentation d'interface à retour d'effort". Phd thesis, Université Paris Sud - Paris XI, 2012. http://tel.archives-ouvertes.fr/tel-00717751.
Ranc, Guillaume. "Résistance en fatigue des dalles de béton armé fissurées des ponts mixtes". Marne-la-vallée, ENPC, 1999. http://www.theses.fr/1999ENPC9930.
Ponce, Quiroga Carlos Wilfrido. "Improving the productivity of Collaborative Robots in the context of manufacturing companies : identification and control improvement". Electronic Thesis or Diss., Université de Lorraine, 2022. http://www.theses.fr/2022LORR0063.
The combination of human capabilities and dexterity with the machine efficiency has been a dream for decades. Technology plays a vital role in helping humans work more efficiently. Thus, collaborative robots have attracted much interest in manufacturing companies.The first part of the thesis is dedicated on determining the influence of bending moment on friction and torque transfer in transmissions used in common collaborative robots: Harmonic Drive gearboxes. Due to their great capabilities, they are widely applied in the field of collaborative robots. However, despite all the great advantages they offer, they present several problematic behaviors that affect their overall performance. Therefore, two experimental setups are designed and developed to study three types of Harmonic Drive transmissions. Friction laws are obtained using force transfer diagrams, which are a parametric representation of the equilibrium of a transmission subjected to input and output torque. The results present new parabolic friction laws for this type of transmissions.The essence of the second part is the improvement of the collaboration to reduce the danger of the robot to the human working nearby by avoiding obstacles at maximum operating speed. This improvement is achieved by proper obstacle avoidance and collision-free path planning, focusing on path continuity, final motion times, and average end-effector speed. We propose an offline path planning approach, considering the obstacle model, given an initial linear path from pose A to pose B. The obstacle model allows to examine in detail the performance of the trajectory in terms of time and traveled distance.The trajectory planning aims to avoid the obstacle using a series of arcs. A variation of Dubins curves defines the path planning. A set of low-computation equations (using radius, sagittas, tangents, etc.) meets the following requirements: (i) C1 continuity at the meeting points of the different segments (equal direction and magnitude of the tangents), (ii) minimum orthogonal and longitudinal distance of the new path, relative to the original path and the obstacle (main arrow and chord). From the general study in 2D, we develop the corresponding equations in 3D.The path planning equations are computed and validated in MATLAB. Then, the libraries and modules are transferred to the Python language, language supported by the robotics simulation framework: ROS and MoveIt, with application to the UR10e cobot.The simulation results validate the feasibility of the obstacle avoidance planner. These are then adapted to the real robot and improved to address certain issues when using the simulation methodology. The control of the end-effector speed is achieved and trajectories are evaluated in terms of total distance traveled by the end effector, total task time, average linear velocity of the movement, and resulting joint positions and velocities. The evaluation allows a quantitative comparison to rank the best trajectories