A data-driven approach for motion planning of industrial robots controlled by high-level motion commands.

Autor:	Hou S; Fraunhofer Institute for Machine Tools and Forming Technology (Fraunhofer IWU), Chemnitz, Germany., Bdiwi M; Fraunhofer Institute for Machine Tools and Forming Technology (Fraunhofer IWU), Chemnitz, Germany., Rashid A; Fraunhofer Institute for Machine Tools and Forming Technology (Fraunhofer IWU), Chemnitz, Germany., Krusche S; Fraunhofer Institute for Machine Tools and Forming Technology (Fraunhofer IWU), Chemnitz, Germany., Ihlenfeldt S; Fraunhofer Institute for Machine Tools and Forming Technology (Fraunhofer IWU), Chemnitz, Germany.
Jazyk:	angličtina
Zdroj:	Frontiers in robotics and AI [Front Robot AI] 2023 Jan 12; Vol. 9, pp. 1030668. Date of Electronic Publication: 2023 Jan 12 (Print Publication: 2022).
DOI:	10.3389/frobt.2022.1030668
Abstrakt:	Most motion planners generate trajectories as low-level control inputs, such as joint torque or interpolation of joint angles, which cannot be deployed directly in most industrial robot control systems. Some industrial robot systems provide interfaces to execute planned trajectories by an additional control loop with low-level control inputs. However, there is a geometric and temporal deviation between the executed and the planned motions due to the inaccurate estimation of the inaccessible robot dynamic behavior and controller parameters in the planning phase. This deviation can lead to collisions or dangerous situations, especially in heavy-duty industrial robot applications where high-speed and long-distance motions are widely used. When deploying the planned robot motion, the actual robot motion needs to be iteratively checked and adjusted to avoid collisions caused by the deviation between the planned and the executed motions. This process takes a lot of time and engineering effort. Therefore, the state-of-the-art methods no longer meet the needs of today's agile manufacturing for robotic systems that should rapidly plan and deploy new robot motions for different tasks. We present a data-driven motion planning approach using a neural network structure to simultaneously learn high-level motion commands and robot dynamics from acquired realistic collision-free trajectories. The trained neural network can generate trajectory in the form of high-level commands, such as Point-to-Point and Linear motion commands, which can be executed directly by the robot control system. The result carried out in various experimental scenarios has shown that the geometric and temporal deviation between the executed and the planned motions by the proposed approach has been significantly reduced, even if without access to the "black box" parameters of the robot. Furthermore, the proposed approach can generate new collision-free trajectories up to 10 times faster than benchmark motion planners. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2023 Hou, Bdiwi, Rashid, Krusche and Ihlenfeldt.)
Databáze:	MEDLINE
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