Robots grasping objects are critical capabilities in warehouse environments and industrial settings. A robotic grasp generally occurs in a scenario where it is unfeasible for a worker to efficiently complete a tedious task, such as picking food and drink cans (cylinder-shaped) and packaging boxes (cube-shaped). It is worth noting that the tops of cylinders and cubes can be represented by ellipses and rectangles in the two-dimensional (2D) space, respectively. Therefore, a robot can grasp cylinder-shaped and cube-shaped objects by ellipse and rectangle detection. However, it faces the challenge of how to accurately detect cylindrical and cubic objects in real-time for robot grasping. To tackle the above research problem, we propose a grasping system that enables a robot to grasp cylinder-shaped and cube-shaped objects in static and dynamic environments by the proposed ellipse and rectangle detector. An end-to-end learning model is constructed to first incorporate a one-stage detection backbone and then, accommodate the proposed adaptive multi-branch multi-scale net with a designed iterative feature pyramid network, local inception net, and multi-receptive-field feature fusion net to generate object detection recommendations. Employing depth information, the coordinates of detected objects are converted to the 3D space via sampling a series of registered depths and pixels on objects from the live video stream. Comparisons with recent detection methods on the same dataset indicate that the proposed ellipse and rectangle detectors present better performance. Abundant grasping experiments are conducted to illustrate that a robot, empowered by the proposed detector, has the capability of grasping cylindrical and cubic objects in dynamic scenarios. (Video on YouTube,https://youtu.be/KK1OtW6GvL0). Note to Practitioners—This paper is motivated by the problem of how to enable a robot to grasp objects with the basic geometric primitives-ellipses and rectangles in static and dynamic scenarios. Our target is to provide a potential solution for flexible industrial settings in operating moving cylinder-shaped and cube-shaped objects (food and drink cans and packaging boxes) in dynamic scenarios such as conveyors of production lines and logistics lines. We constructed a supervised learning model that can accurately and quickly detect ellipses and rectangles. Through the verification of the comparisons with recent methods and robotic grasping experiments, the behavior of the proposed method can be used in practical applications. In the future, we will deploy this robotic grasping system based on the proposed perception method to grasp food and drink cans and packaging boxes from moving conveyors on production lines and logistics lines.

Robotic kitting has attracted considerable attention in logistics and industrial settings. However, existing kitting methods encounter challenges such as low precision and poor efficiency, limiting their widespread applications. To address these issues, we present a novel kitting framework that improves both the precision and computational efficiency of complex kitting tasks. Firstly, our approach introduces a fine-grained orientation estimation technique in the picking module, significantly enhancing orientation precision while effectively decoupling computational load from orientation granularity. This technique combines an SO(2)-equivariant network with a group discretization operation to preciously predict discrete orientation distributions. Secondly, we develop the Hand-Tool Kitting Dataset (HTKD) to evaluate different solutions in handling orientation-sensitive kitting tasks. This dataset comprises a diverse collection of hand tools and synthetically created kits, which reflects the complexities of real-world kitting scenarios. Finally, a series of experiments is conducted to evaluate the performance of the proposed method. The results demonstrate that our approach offers an excellent balance between success rates and computational efficiency in high-precision robotic kitting tasks.

In our daily life, there are many objects represented by cylindrical shapes and ellipsoids. The tops of these objects are formed by elliptic shape primitives. Thus, it is available for a robot to manipulate these objects by ellipse detection. In this work, we propose a novel approach to generating ground truth for training the model based on domain randomization. Using synthetic data generated in this manner, we build an end-to-end deep neural network with a detection backbone and then, combine multiple branches archived from the backbone for sharing the multiple-scale features; further, after employing active rotation filters, the features pass through the region proposal net to form the prediction branches of the box, orientation regression, and object classification; finally, these branches are fused to do ellipse detection, allowing robotic manipulations of cylinders and ellipsoids. To demonstrate the capabilities of the proposed detector, we show the comparison results with the state-of-the-art detector on synthetic and public datasets. The proposed model for ellipse detection and data generation pipeline based on domain randomization in a simulation are evaluated by a series of robotic manipulations implemented in real application scenarios. The results illustrate a high success rate on real-world grasp attempts despite having only been trained on a synthetic dataset. (A video of some robotic experiments is available on YouTube: https://youtu.be/Ueg1XSI2S98).

Abstract With the continuous development of blockchain technology, blockchain gradually becomes to play an important role in the fields of finance, medicine, and new energy. In the certification of the membership of the blockchain, a third‐party certificate authority (CA) is used for certification. Considering the centralized structure of CA, and it is difficult for users to evaluate the credibility of CA. A decentralized blockchain membership authentication scheme and a key agreement protocol based on the elliptic curve are proposed by us. The protocol effectively addresses the credibility and single point of failure problems of centralized CAs in the traditional model. Through analysis, our scheme can effectively perform user registration and membership authentication instead of CAs. The security and correctness of the protocol was also analyzed using the formal protocol analysis tool ProVerif and the Ck model. The key authentication protocol we proposed can resist a variety of attacks, and the computational time consumption and Communication costs are relatively low.

We propose a high efficient learning approach to estimating 6D (Degree of Freedom) pose of the textured or texture-less objects for grasping purposes in a cluttered environment where the objects might be partially occluded. The method comprises three main steps. Given a single RGB-D image, we first deploy appropriate features and the random forest to deduce the object class probability and cast votes for the 6D pose in Hough space by joint regression and classification framework, adopting reservoir sampling and summarizing the pose distribution by clustering. Next, we integrate the auto-context into cascaded Hough forests to improve the efficiency of learning. Extensive experiments on various public datasets and robotic grasps indicate that our method presents some improvements over the state-of-art and reveals the capability for estimating poses in practical applications efficiently.