Multitarget Flexible Grasping Detection Method for Robots in Unstructured Environments

In present-day industrial settings,where robot arms performtasks in an unstructured environment,theremay exist numerousobjects of various shapes scattered in randompositions,making it challenging for a robot armtoprecisely attain the ideal pose to grasp the object.To solve this problem,a multistage robotic arm flexible grasp detection method based on deep learning is proposed.This method first improves the Faster RCNN target detection model,which significantly improves the detection ability of the model for multiscale grasped objects in unstructured scenes.Then,a Squeeze-and-Excitation module is introduced to design a multitarget grasping pose generation network based on a deep convolutional neural network to generate a variety of graspable poses for grasped objects.Finally,a multiobjective IOU mixed area attitude evaluation algorithm is constructed to screen out the optimal grasping area of the grasped object and obtain the optimal grasping posture of the robotic arm.The experimental results show that the accuracy of the target detection network improved by the method proposed in this paper reaches 96.6%,the grasping frame accuracy of the grasping pose generation network reaches 94%and the flexible grasping task of the robotic arm in an unstructured scene in a real environment can be efficiently and accurately implemented.

Magnetically Induced Anisotropic Interaction in Colloidal Assembly

The wide accessibility to nanostructures with high uniformity and controllable sizes and morphologies provides great opportunities for creating complex superstructures with unique functionalities.Employing anisotropic nanostructures as the building blocks significantly enriches the superstructural phases,while their orientational control for obtaining long-range orders has remained a significant challenge.One solution is to introduce magnetic components into the anisotropic nanostructures to enable precise control of their orientations and positions in the superstructures by manipulating magnetic interactions.Recognizing the importance of magnetic anisotropy in colloidal assembly,we provide here an overview of magnetic field-guided self-assembly of magnetic nanoparticles with typical anisotropic shapes,including rods,cubes,plates,and peanuts.The Review starts with discussing the magnetic energy of nanoparticles,appreciating the vital roles of magneto-crystalline and shape anisotropies in determining the easy magnetization direction of the anisotropic nanostructures.It then introduces superstructures assembled from various magnetic building blocks and summarizes their unique properties and intriguing applications.It concludes with a discussion of remaining challenges and an outlook of future research opportunities that the magnetic assembly strategy may offer for colloidal assembly.