TY  - EJOU
AU  - Li, Xi 
AU  - Nie, Runpu 
AU  - Fan, Zhaoyong 
AU  - Zou, Lianying 
AU  - Xiao, Zhenhua 
AU  - Dong, Kaile 

TI  - CMACF-Net: Cross-Multiscale Adaptive Collaborative and Fusion Grasp Detection Network
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 85
IS  - 2
SN  - 1546-2226

AB  - With the rapid development of robotics, grasp prediction has become fundamental to achieving intelligent physical interactions. To enhance grasp detection accuracy in unstructured environments, we propose a novel Cross-Multiscale Adaptive Collaborative and Fusion Grasp Detection Network (CMACF-Net). Addressing the limitations of conventional methods in capturing multi-scale spatial features, CMACF-Net introduces the Quantized Multi-scale Global Attention Module (QMGAM), which enables precise multi-scale spatial calibration and adaptive spatial-channel interaction, ultimately yielding a more robust and discriminative feature representation. To reduce the degradation of local features and the loss of high-frequency information, the Cross-scale Context Integration Module (CCI) is employed to facilitate the effective fusion and alignment of global context and local details. Furthermore, an Efficient Up-Convolution Block (EUCB) is integrated into a U-Net architecture to effectively restore spatial details lost during the downsampling process, while simultaneously preserving computational efficiency. Extensive evaluations demonstrate that CMACF-Net achieves state-of-the-art detection accuracies of 98.9% and 95.9% on the Cornell and Jacquard datasets, respectively. Additionally, real-time grasping experiments on the RM65-B robotic platform validate the framework’s robustness and generalization capability, underscoring its applicability to real-world robotic manipulation scenarios.
KW  - Robot grasp; grasp detection; convolutional neural network; vision transformer; attention mechanism

DO  - 10.32604/cmc.2025.066740