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Adaptive Grasp Pose Optimization for Robotic Arms Using Low-Cost Depth Sensors in Complex Environments.

Aiguo Chen^1,2, Xuanfeng Li^1,2, Kerui Cen^1,2

¹Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, Macau SAR 999078, China.

Sensors (Basel, Switzerland)

|February 13, 2025

Summary

This study introduces an efficient grasp pose estimation algorithm for robotic arms using ellipsoidal modeling. It achieves high success rates and improved efficiency, even with low-cost sensors and noisy data.

Keywords:

ellipsoidal modeling grasp pose estimation nonlinear optimization robotic arm systems

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Area of Science:

Robotics
Computer Vision
Artificial Intelligence

Background:

Traditional deep learning grasp pose estimation methods struggle with data dependency and low-precision point clouds.
Efficient and reliable grasp pose estimation is crucial for robotic arm applications.

Purpose of the Study:

To develop an efficient grasp pose estimation algorithm for robotic arms using ellipsoidal modeling.
To overcome the limitations of deep learning methods in terms of data dependency and efficiency.

Main Methods:

The algorithm segments the target object and employs a three-stage optimization process.
Ellipsoidal modeling is used for initial estimation of principal axes.
Nonlinear optimization refines the six-degree-of-freedom grasp pose.

Main Results:

Achieved a target grasp success rate (TGSR) over 83% in simulations, with significant improvements over GPD and PointNetGPD.
Demonstrated 95-100% success rates in real-world tests.
Improved computational efficiency by 56.3% compared to deep learning methods.

Conclusions:

The proposed ellipsoidal modeling approach offers stable and reliable grasping performance.
The algorithm is practical for real-time applications, even with low-cost sensors and noisy environments.