An End-to-End Multi-Dimensional Perception Network Architecture for Robotic Grasp Detection With Target Edge Collision-Aware Strategy

This paper investigates the feasibility of robotic grasping of various objects in complex scenarios, with our method aiming to achieve grasping capabilities for any scene and any object. Firstly, a Target Edge Collision-Avoidance Strategy is proposed that systematically incorporates the edge features of grasping objects. This strategy is specifically designed to address two critical challenges: bridging the significant performance gap between offline training data and real-world operating conditions, and effectively preventing collision incidents between the robotic end-effector and target objects during physical grasping operations. Furthermore, the Grasp Detection Network based on Global and Local Information Perception (GLIP-Net) is proposed, featuring two intricately designed components: the Global Information Perception and Local Aggregation Module, and the Multi-dimensional Multi-scale Attention and Adaptive Feature Fusion Module. The GLIP-Net enhances the network’s perceptiveness to global information, strengthening the correlation between features and the spatial parameters of grasping. To validate the effectiveness of the presented method, extensive tests and grasping experiments is conducted on the Cornell Dataset and Jacquard Dataset, as well as in practical scenarios. The empirical outcomes indicate an accuracy level of 99.2% on the Cornell Dataset and 96.8% on the Jacquard Dataset, respectively. Furthermore, by employing the Kinova robot in both single-object and multi-object complex scenarios within real-world environments, the grasping success rates of 97.0% and 95.8% is achieved.