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Trajectory planning and tracking control for a robotic additive manufacturing system based on binocular vision guidance

Published online by Cambridge University Press:  14 November 2025

Delan Wei ,
Pengcheng Li Open the ORCID record for Pengcheng Li [Opens in a new window] ,
Jun Xiong ,
Yansheng Cao ,
Xuewen Wei ,
Wei Tian  and
Wenhe Liao
Delan Wei
Affiliation:
College of Mechanical & Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China
Pengcheng Li*
Affiliation:
College of Mechanical & Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China
Jun Xiong
Affiliation:
Hafei Aviation Industry Co., Ltd, Harbin, 150060, China
Yansheng Cao
Affiliation:
Beijing Xinfeng Aerospace Equipment Co., Ltd, Beijing, 100854, China
Xuewen Wei
Affiliation:
College of Mechanical & Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China
Wei Tian
Affiliation:
College of Mechanical & Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China
Wenhe Liao
Affiliation:
College of Mechanical & Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China
*
Corresponding author: Pengcheng Li; Email: lpccmee@nuaa.edu.cn
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Abstract

Robotic manufacturing systems offer significant advantages, including increased flexibility and reduced costs. However, challenges in trajectory planning, error compensation, and system integration hinder their broader application in additive manufacturing. To address these issues, this paper proposes a generalized non-parametric trajectory planning method tailored for robotic additive manufacturing. The proposed trajectory planner incorporates chord error and speed continuity constraints and integrates the look-ahead planning with real-time interpolation in a parallel structure to ensure smooth transitions in the robot’s trajectory. Additionally, a real-time path tracking control method is introduced, combining RBF neural network-based dynamic feedforward control with visual servoing-based feedback control. This control strategy significantly improves the robot’s tracking accuracy, particularly for complex additive manufacturing paths that involve multiple short connected line segments and frequent speed variations. The effectiveness of the proposed methods is validated through experiments on a robotic additive manufacturing platform. The experimental results (line segment, circular arc segment, and continuous path tracking) show that the robot’s tracking error remains within $\pm$0.15 mm and $\pm 0.05^{\circ }$.

Keywords

robot controltrajectory planningadditive manufacturingvisual servoingtracking accuracy

Information

Type
Research Article
Information
Robotica , First View , pp. 1 - 24
Copyright
© The Author(s), 2025. Published by Cambridge University Press

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