Yang Jiang, Xuejiao Zhang, and Bin Zhao
Robot grasping detection, ACCSNet, Adan, Multi-target objects grasping dataset
With the advent of the Industry 4.0 era, grasping technology has gradually become an essential skill of robot. Due to the existing problems, such as cross-domain adaptability, algorithm accuracy, speed, robustness to be improved, the lack of multi-object grasping datasets, and some networks have a poor performance in detecting small targets, this paper researches multi-object grasping detection based on the improved ShuffleNet network. In this paper, we independently make multi-target objects grasping dataset first. Then, We focus on the ShuffleNet and design the atrous spatial pyramid pooling (ASPP) + channel attention module and spatial attention module (CBAM) + CEASC + ShuffleNet (ACCSNet) based on the ShuffleNet model, and Adan is cited as the optimisation function when training the network. Finally, based on the constructed multi-target objects grasping dataset, the paper verify grasping experiments using the Kinova mico2 six-degree-of-freedom robotic arm in the complex multi- target scene. The experimental results show that the accuracy and speed of the ACCSNet are improved in the grasping process. Specifically, the experimental loss rate is only 1%, 4.8% lower than ShuffleNet, and the speed is 1 min faster than ShuffleNet each epoch.
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