English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In this study we present an autonomous grasping system that uses a vision-guided hand–eye coordination policy with closed-loop vision-based control to ensure a sufficient task success rate while maintaining acceptable manipulation precision. When facing a diversity of tasks with complex environments, an autonomous robot should use the concept of task precision, including the accuracy of perception and precision of manipulation, as opposed to just the grasping success rate typically used in previous works. Task precision combines the advantages of grasping behaviors observed in humans and a grasping method applied in existing works. A visual servoing approach and a subtask decomposition strategy are proposed here to obtain the desired level of task precision. Our system performs satisfactorily on a tangram puzzle task. The experiments highlight the accuracy of perception, precision of manipulation, and robustness of the system. Moreover, the system is of great significance for improving the adaptability and flexibility of autonomous robots.

Vision-Guided Hand–Eye Coordination for Robotic Grasping and Its Application in Tangram Puzzles