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Related Experiment Video

Updated: Jul 16, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
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Published on: August 29, 2025

Trajectory Planning for a Spraying Robotic Arm Using a Digital Twin and an Improved SAC Algorithm.

Bo Gao1, Mingjun Xu1, Liangsong Huang1

  • 1Robot Research Center, Shandong University of Science and Technology, Qingdao 266590, China.

Sensors (Basel, Switzerland)
|July 15, 2026
PubMed
Summary

This study introduces an improved soft actor-critic (SAC) algorithm for robotic arm trajectory planning in shotcrete spraying. The digital twin-enhanced method significantly reduces trajectory tracking errors and improves spraying accuracy.

Keywords:
PERcomposite reward functiondigital twinimproved SAC algorithmspraying robotic armtrajectory planning

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Last Updated: Jul 16, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
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Published on: August 29, 2025

Operation of the Collaborative Composite Manufacturing (CCM) System
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Published on: October 1, 2019

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Digital Twins

Background:

  • Underground shotcrete spraying presents complex environmental challenges.
  • Physical robotic arm training is costly, risky, and struggles with integrating spraying constraints.
  • Traditional path planning methods are insufficient for nuanced spraying operations.

Purpose of the Study:

  • To develop an advanced trajectory planning method for a 4-DOF spraying robotic arm.
  • To leverage a digital twin platform and an improved soft actor-critic (SAC) algorithm.
  • To address limitations in physical training and constraint integration for spraying robots.

Main Methods:

  • A digital twin platform was constructed using Unity Editor for unified modeling of the robotic arm, environment, and spraying process.
  • An improved SAC algorithm was developed, incorporating a composite reward function and a prioritized experience replay (PER) mechanism.
  • Key performance indicators included trajectory tracking, spray distance, nozzle normal, spraying speed, safety, and motion smoothness.

Main Results:

  • The improved SAC algorithm reduced trajectory tracking root mean square error (RMSE) by 66.7% compared to standard SAC (from 24.0 ± 2.0 mm to 8.0 ± 0.7 mm).
  • Significant reductions were achieved in spray distance error (43.5%), nozzle normal error (49.1%), spraying speed error (47.3%), and motion smoothness index (48.7%).
  • The digital twin environment facilitated effective simulation and validation of the proposed method.

Conclusions:

  • The proposed digital twin-based trajectory planning method with the improved SAC algorithm effectively enhances robotic arm performance in spraying applications.
  • This approach offers a viable solution for complex spraying tasks, improving accuracy and efficiency while reducing risks associated with physical training.
  • The integration of a composite reward function and PER mechanism in SAC is crucial for optimizing spraying parameters and motion quality.