Enhancing Robotic Grasp Detection with a Novel Two-Stage Approach: From Conceptualization to Implementation

Zhe Chu; Mengkai Hung; Xiangyu Chen

doi:10.62762/TIUS.2024.777385

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Volume 1, Issue 1, IECE Transactions on Intelligent Unmanned Systems

Volume 1, Issue 1, 2024

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Jinchao Chen

School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China

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IECE Transactions on Intelligent Unmanned Systems, 2024, Volume 1, Issue 1: 44-54

Research Article | 27 July 2024

Enhancing Robotic Grasp Detection with a Novel Two-Stage Approach: From Conceptualization to Implementation

Zhe Chu 1

Mengkai Hung 2

Xiangyu Chen 3 *

1 School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, China

2 School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China

3 School of Mechanics, Civil Engineering and Architecture, Northwestern Polytechnical University, Xi'an 710129, China

* Corresponding author: Xiangyu Chen, email: chxy95@mail.nwpu.edu.cn

DOI: 10.62762/TIUS.2024.777385

Received: 02 June 2024, Accepted: 13 July 2024, Published: 27 July 2024

Abstract

This study introduces a novel two-stage approach for robotic grasp detection, addressing the challenges faced by end-to-end deep learning methodologies, particularly those based on convolutional neural networks (CNNs) that require extensive and often impractical datasets. Our method first leverages a particle swarm optimizer (PSO) as a candidate estimator, followed by CNN-based verification to identify the most probable grasp points. This approach represents a significant advancement in the field, achieving an impressive accuracy of 92.8% on the Cornell Grasp Dataset. This positions it among the leading methods while maintaining real-time operational capability. Furthermore, with minor modifications, our technique can predict multiple grasp points per object, offering diverse grasping strategies. This adaptability and high performance suggest substantial potential for practical applications in robotic systems, enhancing their efficiency and reliability in dynamic environments.

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Keywords

Robotic grasp detection

Convolutional neural network

Two-stage cascaded system

Particle swarm optimizer

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APA Style

Chu, Z., Hung, M. & Chen, X. (2024). Enhancing Robotic Grasp Detection with a Novel Two-Stage Approach: From Conceptualization to Implementation. IECE Transactions on Intelligent Unmanned Systems, 1(1), 44–54. https://doi.org/10.62762/TIUS.2024.777385

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