Optimization of the robot and positioner motion in a redundant fiber placement workcell - Archive ouverte HAL Access content directly
Journal Articles Mechanism and Machine Theory Year : 2017

Optimization of the robot and positioner motion in a redundant fiber placement workcell

Abstract

The paper proposes a new methodology to optimize the robot and positioner motions in redundant robotic system for the fiber placement process. It allows user to find time-optimal smooth profiles for the joint variables while taking into account full capacities of the robotic system expressed by the maximum actuated joint velocities and accelerations. In contrast to the previous works, the proposed methodology possesses high computational efficiency and also takes into account the collision constraints. The developed technique is based on conversion of the original continuous problem into a discrete one, where all possible motions of the robot and the positioner are represented as a directed multi-layer graph and the desired time-optimal motions are generated using the dynamic programming that is applied sequentially for the rough and fine search spaces. To adjust the optimization results to the engineering requirements, the obtained trajectories are smoothed using the spline approximation. The advantages of the proposed methodology are confirmed by an application example that deals with a planar fiber placement robotic system.
Fichier principal
Vignette du fichier
MMT2017_Gao_Pashkevich_Caro_Optimization of the robot and positioner motion in a redundant fiber placement workcell.pdf (2.38 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01692417 , version 1 (02-03-2018)

Identifiers

Cite

Jiuchun Gao, Anatol Pashkevich, Stéphane Caro. Optimization of the robot and positioner motion in a redundant fiber placement workcell. Mechanism and Machine Theory, 2017, 114, pp.170 - 189. ⟨10.1016/j.mechmachtheory.2017.04.009⟩. ⟨hal-01692417⟩
275 View
582 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More