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Related Experiment Video

Updated: Mar 7, 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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Vision-Controlled autonomous navigation in unstructured environments: Integrating image processing, path planning,

Pengyuan Wang1, Haipeng Yu2, Shuqing Wang3

  • 1Zhengzhou University of Light Industry, Zhengzhou, China.

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Summary

This study presents a vision-controlled system for robot autonomous navigation in complex, unstructured environments. It enhances robot perception, path planning, and trajectory control for safer, more accurate movement without external markers.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Vision

Background:

  • Autonomous navigation in unstructured environments is a significant challenge for robots.
  • Existing systems often rely on external markers or complex sensor suites.

Purpose of the Study:

  • To develop a vision-controlled autonomous navigation framework for robots.
  • To enable robots to navigate complex terrains using only vision sensors and image processing.

Main Methods:

  • Semantic mapping and localization using a target detection network for markerless perception.
  • Refined RRT-connect algorithm for adaptive path planning in unpredictable terrains.
  • Soft Actor-Critic (SAC) model for precise trajectory control and reduced tracking errors.

Main Results:

  • The framework successfully generated semantic maps from local views for global localization.
  • The refined path planning algorithm demonstrated safer navigation in irregular terrains.
  • The SAC-based controller achieved high path-following accuracy with minimal errors.
  • Empirical validation with a miniature robot confirmed the system's robustness and accuracy.

Conclusions:

  • The proposed vision-controlled framework significantly advances robot autonomous navigation capabilities.
  • It provides a robust and accurate solution for robots operating in complex, unstructured environments.
  • This research paves the way for more resilient and autonomous robotic systems.