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In this study, diethylenetriaminepentaacetic acid (DTPA)-modified magnetic graphene oxide (MGO) was synthesized for removal of Cu­(II), Pb­(II), and Cd­(II) ions from acidic aqueous solutions. The prepared DTPA/MGO composites were characterized by scanning electron microscopy, X-ray diffraction, Fourier transform infrared and X-ray photoelectron spectroscopies, and zeta potential. The results showed that DTPA successfully functionalized MGO. Adsorption experiments indicated that DTPA/MGO composites exhibited excellent adsorption property in acidic aqueous solutions. The adsorption processes were applicable for the Langmuir adsorption isotherm and the pseudo-second-order model. The maximum adsorption capacities at pH 3.0 for Cu­(II), Pb­(II), and Cd­(II) ions were 131.4, 387.6, and 286.5 mg/g, respectively. The thermodynamic studies demonstrated that adsorption processes were endothermic and spontaneous. Moreover, the DTPA/MGO composites could selectively adsorb Pb­(II) from multimetal mixed systems. Adsorption–desorption results showed that the DTPA/MGO composites exhibited excellent reusability. These results suggested that DTPA/MGO composites have great potential in removing heavy metals from acidic wastewater, especially for Pb­(II).

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In logistics warehouse sorting, rubbish classification, and household services, scenarios exist in which rigid and soft objects are randomly piled together. In such situations, two major challenges arise in robotic picking tasks: the first is to distinguish rigid objects from soft objects, and the second is to grasp one object of each type at a time. In this study, we propose a novel robotic picking methodology for the grasping of objects mixed with towels. The proposed approach is based on a novel object detection method that can identify a rigid object placed in different directions using a rotational bounding box. Rigid objects can be separated from the mixed scene without object segmentation. Moreover, the grasping pose of a rigid object can be generated directly along its principal axis, without using a CAD model or specific pose detection method. The gripper opening width is determined according to the object size. Therefore, our method can detect whether other objects, particularly soft ones, exist around a rigid object. If no suitable grasping pose is available for the rigid objects, the grasping pose on a wrinkle of the towel is selected. The experiments demonstrate that our method can accomplish the picking task in scenes with mixed rigid and soft objects, thereby indicating its significance in robotic object detection and sorting. INDEX TERMS object detection, rotational bounding box, grasping pose, mixed objects, towels

This paper presents an efficient neural network model to generate robotic grasps with high resolution images. The proposed model uses fully convolution neural network to generate robotic grasps for each pixel using 400 × 400 high resolution RGB-D images. It first down-sample the images to get features and then up-sample those features to the original size of the input as well as combines local and global features from different feature maps. Compared to other regression or classification methods for detecting robotic grasps, our method looks more like the segmentation methods which solves the problem through pixel-wise ways. We use Cornell Grasp Dataset to train and evaluate the model and get high accuracy about 94.42% for image-wise and 91.02% for object-wise and fast prediction time about 8ms. We also demonstrate that without training on the multiple objects dataset, our model can directly output robotic grasps candidates for different objects because of the pixel wise implementation.