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Item An electromagnetic indirect-driving scanning mirror for wide-field coaxial LiDAR applications(SPIE) Dezhen SongThis paper reports an electromagnetic indirect-driving scanning mirror with an enlarged mirror plate (17𝑚𝑚 × 17𝑚𝑚) supported by high-strength polymer hinges for wide-field coaxial LiDAR (Light Detection and Ranging) applications. An indirect-driving mechanism was developed to achieve large tilting angle through mechanical amplification, while maintaining a relatively high resonance frequency of the enlarged mirror plate. A prototype mirror was designed, fabricated, and tested. A Hall scan position sensor was integrated to monitor the pose of the mirror in real time. The testing results show a coupled resonance frequency of 54.9 𝐻𝑧 with an optical tilting angle of ±60°, corresponding to a field of view (FoV) of 120°. A wide-field coaxial LiDAR setup was also built based on the indirect-driving scanning mirror, and 2D imaging was demonstrated.Item Design, modeling and analysis of a variable camber wing based on initially curved beams(Elsevier) Ke WuIn recent years, variable camber wings (VCWs) have gained significant attention in the aviation industry due to their potential to enhance fuel efficiency, reduce noise, and improve the lift-to-drag ratio. Despite extensive efforts to design VCWs, achieving both large deformations and high load-bearing capacities remains challenging. This paper introduces a novel methodology for designing morphing trailing edge based on initially curved beams (ICBs) and develops a comprehensive mathematical model for its analysis and design. We perform a compliance analysis of ICBs with varying geometry to propose a conceptual design for the trailing edge structure. The flexible structure is modeled using geometrically nonlinear Euler-Bernoulli beam theory within the Frenet framework, and its validity is confirmed through finite element analysis. The structural design is formulated as a constrained optimization problem, solved with efficient numerical methods to ensure precise deformation, load-bearing capability, and low stress levels. An optimized prototype of the morphing trailing edge has been manufactured and experimentally tested, demonstrating a camber range of ±25 °, with theoretical analysis and experimental results showing high consistency.Item Simulating Automotive Radar with Lidar and Camera Inputs(arXiv) Dezhen SongLow-cost millimeter automotive radar has received more and more attention due to its ability to handle adverse weather and lighting conditions in autonomous driving. However, the lack of quality datasets hinders research and development. We report a new method that is able to simulate 4D millimeter wave radar signals including pitch, yaw, range, and Doppler velocity along with radar signal strength (RSS) using camera image, light detection and ranging (lidar) point cloud, and ego-velocity. The method is based on two new neural networks: 1) DIS-Net, which estimates the spatial distribution and number of radar signals, and 2) RSS-Net, which predicts the RSS of the signal based on appearance and geometric information. We have implemented and tested our method using open datasets from 3 different models of commercial automotive radar. The experimental results show that our method can successfully generate high-fidelity radar signals. Moreover, we have trained a popular object detection neural network with data augmented by our synthesized radar. The network outperforms the counterpart trained only on raw radar data, a promising result to facilitate future radar-based research and development.Item Human-Aware Reactive Task Planning of Sequential Robotic Manipulation Tasks(IEEE, 15-Jan-25) Anqing DuanThe recent emergence of Industry 5.0 underscores the need for increased autonomy in human-robot interaction (HRI), presenting both motivation and challenges in achieving resilient and energy-efficient production systems. To address this, in this article, we introduce a strategy for seamless collaboration between humans and robots in manufacturing and maintenance tasks. Our method enables smooth switching between temporary HRI (human-aware mode) and long-horizon automated manufacturing (fully automatic mode), effectively solving the human-robot coexistence problem. We develop a task progress monitor that decomposes complex tasks into robot-centric action sequences, further divided into three-phase subtasks. A trigger signal orchestrates mode switches based on detected human actions and their contribution to the task. In addition, we introduce a human agent coefficient matrix, computed using selected environmental features, to determine cut-points for reactive execution by each robot. To validate our approach, we conducted extensive experiments involving robotic manipulators performing representative manufacturing tasks in collaboration with humans. The results show promise for advancing HRI, offering pathways to enhancing sustainability within Industry 5.0. Our work lays the foundation for intelligent manufacturing processes in future societies, marking a pivotal step toward realizing the full potential of human-robot collaboration.Item Human-in-the-Loop Robot Learning for Smart Manufacturing: A Human-Centric Perspective(IEEE, 10-Jan-25) Anqing DuanRobot learning has attracted an ever-increasing attention by automating complex tasks, reducing errors, and increasing production speed and flexibility, which leads to significant advancements in manufacturing intelligence. However, its low training efficiency, limited real-time feedback, and challenges in adapting to untrained scenarios hinder its applications in smart manufacturing. Introducing a human role in the training loop, a practice known as human-in-the-loop (HITL) robot learning, can improve the performance of robots by leveraging human prior knowledge. Nonetheless, the exploration of HITL robot learning within the context of human-centric smart manufacturing remains in its infancy. This study provides a holistic literature review for understanding HITL robot learning within an industrial context from a human-centric perspective. A united structure is presented to encompass different aspects of human intelligence in HITL robot learning, highlighting perception, cognition, behavior, and notably, empathy. Then, the typical applications in manufacturing scenarios are analyzed to expand the research landscape for smart manufacturing. Finally, it introduces the empirical challenges and future directions for HITL robot learning in the next industrial revolution era.