EtherCAT is one of the preferred real-time Ethernet technologies. However, EtherCAT is not applicable in high-end control fields due to real-time constraints. Clock synchronization and cycle time are the most representative limitations. In this paper, a novel Heterogeneous Parallel System Architecture (HPSA) with features of parallel computation and hard real-time is presented. An HPSA-based EtherCAT hard real-time master is developed to significantly improve clock synchronization and shorten cycle time. Traditional EtherCAT masters feature serial processing and run on a PC. This HPSA-based master consists of two parts: EtherCAT master stack (EMS) and EtherCAT operating system (EOS). EMS implements the parallel operation of EtherCAT to realize the shorter cycle time, and EOS brings a hard real-time environment to the HPSA-based master to improve clock synchronization. Furthermore, this HPSA-based master operates on a heterogeneous System-on-a-chip (SoC). EMS and EOS form a heterogeneous architecture inside this SoC to achieve low-latency process scheduling. Experimental results show that in our HPSA-based EtherCAT hard real-time master, the cycle time reaches the sub-50 μs range, and the synchronization error reduces to several nanoseconds. Thus, this HPSA-based master has great application value in high-performance control systems.

In this paper, the robot grasping for stacked objects is studied based on object detection and grasping order planning. Firstly, a novel stacked object classification network (SOCN) is proposed to realize stacked object recognition. The network takes into account the visible volume of the objects to further adjust its inverse density parameters, which makes the training process faster and smoother. At the same time, SOCN adopts the transformer architecture and has a self-attention mechanism for feature learning. Subsequently, a grasping order planning method is investigated, which depends on the security score and extracts the geometric relations and dependencies between stacked objects, it calculates the security score based on object relation, classification, and size. The proposed method is evaluated by using a depth camera and a UR-10 robot to complete grasping tasks. The results show that our method has high accuracy for stacked object classification, and the grasping order effectively and successfully executes safely.