Automatic Robot Hand-Eye Calibration Enabled by Learning-Based 3D Vision

Hand-eye calibration, as a fundamental task in vision-based robotic systems, aims to estimate the transformation matrix between the coordinate frame of the camera and the robot flange. Most approaches to hand-eye calibration rely on external markers or human assistance. We proposed Look at Robot Base Once (LRBO), a novel methodology that addresses the hand-eye calibration problem without external calibration objects or human support, but with the robot base. Using point clouds of the robot base, a transformation matrix from the coordinate frame of the camera to the robot base is established as I=AXB. To this end, we exploit learning-based 3D detection and registration algorithms to estimate the location and orientation of the robot base. The robustness and accuracy of the method are quantified by ground-truth-based evaluation, and the accuracy result is compared with other 3D vision-based calibration methods. To assess the feasibility of our methodology, we carried out experiments utilizing a low-cost structured light scanner across varying joint configurations and groups of experiments. The proposed hand-eye calibration method achieved a translation deviation of 0.930 mm and a rotation deviation of 0.265 degrees according to the experimental results. Additionally, the 3D reconstruction experiments demonstrated a rotation error of 0.994 degrees and a position error of 1.697 mm. Moreover, our method offers the potential to be completed in 1 second, which is the fastest compared to other 3D hand-eye calibration methods. Code is released at github.com/leihui6/LRBO.Comment: 17 pages, 19 figures, 6 tables, submitted to MSS

Collaborative robot dynamics with physical human–robot interaction and parameter identification with PINN

Collaborative robots are increasingly being used in dynamic and semi-structured environments because of their ability to perform physical Human–Robot Interaction (pHRI) to ensure safety. Therefore, it is crucial to model the dynamics of collaborative robots during pHRI to gain valuable insights into the system's behavior when in contact with humans. In this work, a generic dynamic model is proposed for the quasi-static contact phase of pHRI, which considers the interaction dynamics and the complete structural dynamics of the collaborative robot. Moreover, a hybrid physics-informed neural network (PINN) is proposed, which utilizes a recurrent neural network (RNN) and the Runge–Kutta method to identify the joint dynamic parameters without complicated regressor construction. Experiments are conducted using a UR3e collaborative robot, and the PINN is trained using the acquired data. The results demonstrate the effectiveness of the PINN in identifying joint dynamics without prior knowledge, and the dynamic simulation of pHRI is consistent with the experimental results. The proposed model and PINN-based identification approach have the potential to improve safety and productivity in industrial environments by facilitating the control of pHRI

Research Progress on the Removal of Contaminants from Wastewater by Constructed Wetland Substrate: A Review

Constructed wetlands (CWs) primarily achieve efficient wastewater purification through synergistic interactions among substrates, plants, and microorganisms. Serving as the structural foundation of the entire wetland system, substrates not only provide a growth medium for plants, but also serve as adhesive carriers for microorganisms and habitats for animal activities. Research on substrates has attracted considerable attention; however, in practical engineering applications, the selection of substrates often depend on personal experience, which may lead to significant gaps in the effectiveness of wetland systems in treating different characteristic contaminants. Therefore, it is of great significance to investigate the influence of substrates on the removal of contaminants in sewage and identify substrate materials with good physical and chemical properties to optimize the design and operation of CWs-based sewage-treatment systems and improve their purification efficiency. In this review, bibliometric analysis was conducted to using the Web of Science database and VOSviewer_1.6.20 software to assess the progress of research on CWs. This article provides a comprehensive overview of substrate types and characteristics based on recent research advancements in the field. Additionally, it discusses removal methods and the influence of factors related to conventional contaminants (COD, nitrogen, and phosphorus), heavy metals (HMs), fluorinated compounds, pharmaceuticals, personal care products (PPCPs), and microplastics. A thorough evaluation was conducted on the economic costs of various substrates and their ability to remove major contaminants from water bodies, providing a reference for the further development of wetland technology

π-Net: A parallel information-sharing network for shared-account cross-domain sequential recommendations

Sequential Recommendation (SR) is the task of recommending the next item based on a sequence of recorded user behaviors. We study SR in a particularly challenging context, in which multiple individual users share a single account (shared-account) and in which user behaviors are available in multiple domains (cross-domain). These characteristics bring new challenges on top of those of the traditional SR task. On the one hand, we need to identify different user behaviors under the same account in order to recommend the right item to the right user at the right time. On the other hand, we need to discriminate the behaviors from one domain that might be helpful to improve recommendations in the other domains. We formulate the Shared-account Cross-domain Sequential Recommendation (SCSR) task as a parallel sequential recommendation problem. We propose a Parallel Information-sharing Network (πNet) to simultaneously generate recommendations for two domains where user behaviors on two domains are synchronously shared at each timestamp. π-Net contains two core units: a shared account filter unit (SFU) and a cross-domain transfer unit (CTU). The SFU is used to address the challenge raised by shared accounts; it learns user-specific representations, and uses a gating mechanism to filter out information of some users that might be useful for another domain. The CTU is used to address the challenge raised by the cross-domain setting; it adaptively combines the information from the SFU at each timestamp and then transfers it to another domain. After that, π-Net estimates recommendation scores for each item in two domains by integrating information from both domains. To assess the effectiveness of π-Net, we construct a new dataset HVIDEO from real-world smart TV watching logs. To the best of our knowledge, this is the first dataset with both shared-account and cross-domain characteristics. We release HVIDEO to facilitate future research. Our experimental results demonstrate that π-Net outperforms state-of-the-art baselines in terms of MRR and Recall