A 3D object grasping point learning system is proposed in this paper, which contains object coordinate construction and grasping point learning. A RGBD Convolutional Neural Network (RGBD-CNN) is proposed to classify the orientation type of objects. An object model and the iterative closest point algorithm (ICP) are then applied to estimate the object pose. Hence, the object coordinate can be constructed in the end. The object's normal vector and depth images are obtained first for learning the object grasping region. Then, the grasping range of the end effector (palm) will be simulated on these images. Finally, Convolutional Autoencoder (CAE) is applied to encode the physical characteristics of the simulated palm image. The proposed method can evaluate the grasping points by comparing the features of the simulated palm in the database through a 3D KD-tree. The robot plans a suitable grasping point based on the appointed task by integrating object coordinates and learned grasping points. It is worth mentioning that most research only emphasizes object orientation justification or grasp points generation. However, this research considers the problem of object pose estimation and grasping points generation together. Therefore, the robot can adapt to different task situations in real time. Real experiments demonstrate that the proposed method can recognize various kinds of shapes.

A weakly-hard fault model can be captured by an (m,k) constraint, where 0≤ m ≤ k , meaning that there are at most m bad events (faults) among any k consecutive events. In this article, we use a weakly-hard fault model to constrain the occurrences of faults in system inputs. We develop approaches to verify properties for all possible values of (m,k) , where k is smaller than or equal to a given K , in an exact and efficient manner. By verifying all possible values of (m,k) , we define weakly-hard requirements for the system environment and design a runtime monitor based on counting the number of faults in system inputs. If the system environment satisfies the weakly-hard requirements, then the satisfaction of desired properties is guaranteed; otherwise, the runtime monitor can notify the system to switch to a safe mode. This is especially essential for cyber-physical systems that need to provide guarantees with limited resources and the existence of faults. Experimental results with discrete second-order control, network routing, vehicle following, and lane changing demonstrate the generality and the efficiency of the proposed approaches.

We introduce SAW, a tool for safety analysis of weakly-hard systems, in which traditional hard timing constraints are relaxed to allow bounded deadline misses for improving design flexibility and runtime resiliency. Safety verification is a key issue for weakly-hard systems, as it ensures system safety under allowed deadline misses. Previous works are either for linear systems only, or limited to a certain type of nonlinear systems (e.g., systems that satisfy exponential stability and Lipschitz continuity of the system dynamics). In this work, we propose a new technique for infinite-time safety verification of general nonlinear weakly-hard systems. Our approach first discretizes the safe state set into grids and constructs a directed graph, where nodes represent the grids and edges represent the reachability relation. Based on graph theory and dynamic programming, our approach can effectively find the safe initial set (consisting of a set of grids), from which the system can be proven safe under given weakly-hard constraints. Experimental results demonstrate the effectiveness of our approach, when compared with the state-of-the-art. An open source implementation of our tool is available at https://github.com/551100kk/SAW . The virtual machine where the tool is ready to run can be found at https://www.csie.ntu.edu.tw/~r08922054/SAW.ova .

In this paper, we adopt a realistic approach to modeling a full-duplex heterogeneous cellular network (HetNet) consisting of a tier of small cell base stations (BSs) that can perform half-duplex (HD) or full-duplex (FD) transmission depending on which duplex transmission is needed by their associated users and a tier of macro BSs that only can perform HD transmission due to their strong self-interference (SI). Two realistic issues, void cell and imperfect SI cancellation, that could exist in this HetNet are also considered in the interference model of this two-tier HetNet. We first study the Laplace transforms of the reciprocals of the signal-to- interference power ratios of users and small cell BSs and derive their explicit expressions. These Laplace transforms lead to the derivations of the ergodic rates of users and small cell BSs found in a very compact form for general power-law channel models. We show that FD does not always outperform HD in terms of ergodic rates and demonstrate how to make FD#x002F;HD transmission benefits the ergodic rates by deploying macro and small cell BSs with appropriate intensities.