AUTHOR=Hou Shuxiao , Bdiwi Mohamad , Rashid Aquib , Krusche Sebastian , Ihlenfeldt Steffen 

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

JOURNAL=Frontiers in Robotics and AI

VOLUME=Volume 9 - 2022

YEAR=2023

URL=https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2022.1030668

DOI=10.3389/frobt.2022.1030668

ISSN=2296-9144

ABSTRACT=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 some industrial robot control systems. Some industrial robot systems provide interfaces to execute planned trajectories by external control loops as low-level control inputs. However, there is a geometric and temporal discrepancy between the executed and the planned motions due to the inaccurate estimation of the inaccessible robot dynamic behavior and controller parameters at the planning phase. This discrepancy becomes unacceptable and it can lead to collisions or dangerous situations, especially in heavy-duty industrial robot applications where high-speed and long-distance motions are widely used. We present a data-driven motion planning approach using a neural network structure to learn high-level motion commands and robot dynamics from acquired realistic collision-free trajectories simultaneously. The trained neural network can generate trajectory as 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 discrepancy between the executed and the planned motion by the proposed approach has been reduced, even if without access to the “black box” parameters of the robot. Furthermore, it can generate new collision-free trajectories up to 10 times faster than benchmark motion planners.