Related Experiment Video
Updated: Mar 7, 2026
05:47
Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
Published on: August 29, 2025
502
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.
Plos One
|March 5, 2026
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.
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.
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