As there come to be more applications of intelligent robots, their task object is becoming more varied. However, it is still a challenge for a robot to handle unfamiliar objects. We review the recent work on the feature sensing and robotic grasping of objects with uncertain information. In particular, we focus on how the robot perceives the features of an object, so as to reduce the uncertainty of objects, and how the robot completes object grasping through the learning-based approach when the traditional approach fails. The uncertain information is classified into geometric information and physical information. Based on the type of uncertain information, the object is further classified into three categories, which are geometric-uncertain objects, physical-uncertain objects, and unknown objects. Furthermore, the approaches to the feature sensing and robotic grasping of these objects are presented based on the varied characteristics of each type of object. Finally, we summarize the reviewed approaches for uncertain objects and provide some interesting issues to be more investigated in the future. It is found that the object’s features, such as material and compactness, are difficult to be sensed, and the object grasping approach based on learning networks plays a more important role when the unknown degree of the task object increases.

The robotic hand plays an extremely significant role in completing various robotic tasks, especially for a robot to complete different grasping tasks. In this paper, we propose a novel multi-fingered bionic hand with variable stiffness for robotic grasp. First, based on the analysis of the human hand, we design the modular finger with variable stiffness mechanism and the flexible thumb with dual-link independent mechanism. Second, the multi-fingered bionic hand composed of three modular fingers and one flexible thumb is presented, which possesses 14 degrees-of-freedom (DoFs). Then, the parameters of the designed hand are analyzed to obtain the stiffness characteristics and working space. Furthermore, the grasping control method based on position control and force control is proposed for robotic grasp. Finally, a series of experiments are designed to verify the effectiveness of the designed hand and the proposed method. The experimental results show that (1) the designed variable stiffness mechanism can be used to adjust the stiffness of hand fingers, (2) the proposed method can effectively control the grasping force under different stiffness conditions, and (3) the designed hand can successfully grasp different types of objects by various grasping strategies.

Object in-hand reorientation is widely known to be crucial for robotic dexterous manipulation, and the stable reorientation of the in-hand object with the two-fingered gripper is challenging. In this paper, the object reorientation is completed using a commercial two-fingered gripper with integrated force/torque sensors. The identification models for the object in-hand status (slipping status and pivoting status) are built based on the friction theory. In-hand status identification provides an important guarantee for the object in-hand reorientation. Moreover, we propose a control framework for the object in-hand reorientation under the action of gravity. This framework is to make the object slip or pivot by continuously controlling the gripping force. The force control law is related to the object's kinematics parameters, and the gripping force is modified according to the object in-hand status and kinematics parameters. Finally, the effectiveness of the proposed method is validated by testing object slipping and object pivoting in experiments. Results show that the gripping point and pose of the in-hand object are adjusted through object slipping and object pivoting.

The security of industrial control systems (ICSs) has received a lot of attention in recent years. ICSs were once closed networks. But with the development of IT technologies, ICSs have become connected to the Internet, increasing the potential of cyberattacks. Because ICSs are so tightly linked to human lives, any harm to them could have disastrous implications. As a technique of providing protection, many intrusion detection system (IDS) studies have been conducted. However, because of the complicated network environment and rising means of attack, it is difficult to cover all attack classes, most of the existing classification techniques are hard to deploy in a real environment since they cannot deal with the open set problem. We propose a novel artificial neural network based-methodology to solve this problem. Our suggested method can classify known classes while also detecting unknown classes. We conduct research from two points of view. On the one hand, we use the openmax layer instead of the traditional softmax layer. Openmax overcomes the limitations of softmax, allowing neural networks to detect unknown attack classes. During training, on the other hand, a new loss function termed center loss is implemented to improve detection ability. The neural network model learns better feature representations with the combined supervision of center loss and softmax loss. We evaluate the neural network on NF-BoT-IoT-v2 and Gas Pipeline datasets. The experiments show our proposed method is comparable with the state-of-the-art algorithm in terms of detecting unknown classes. But our method has a better overall classification performance.

Robot measurement systems with a binocular planar structured light camera (3D camera) installed on a robot end-effector are often used to measure workpieces’ shapes and positions. However, the measurement accuracy is jointly influenced by the robot kinematics, camera-to-robot installation, and 3D camera measurement errors. Incomplete calibration of these errors can result in inaccurate measurements. This paper proposes a joint calibration method considering these three error types to achieve overall calibration. In this method, error models of the robot kinematics and camera-to-robot installation are formulated using Lie algebra. Then, a pillow error model is proposed for the 3D camera based on its error distribution and measurement principle. These error models are combined to construct a joint model based on homogeneous transformation. Finally, the calibration problem is transformed into a stepwise optimization problem that minimizes the sum of the relative position error between the calibrator and robot, and analytical solutions for the calibration parameters are derived. Simulation and experiment results demonstrate that the joint calibration method effectively improves the measurement accuracy, reducing the mean positioning error from over 2.5228 mm to 0.2629 mm and the mean distance error from over 0.1488 mm to 0.1232 mm.