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Simulating Automotive Radar with Lidar and Camera Inputs

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arXiv 2025年

作者： Song, Peili Song, Dezhen Yang, Yifan Lan, Enfan Liu, Jingtai Institute of Robotics and Automatic Information System Nankai University Tianjin300350 China Tianjin Key Laboratory of Intelligent Robotics Tianjin300350 China TBI center Nankai University Tianjin300350 China Abu Dhabi United Arab Emirates

Low-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. © 2025, CC BY-NC-ND.

关键词： Automotive radar

NavG: Risk-Aware Navigation in Crowded Environments Based on Reinforcement Learning with Guidance Points

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arXiv 2025年

作者： Zhang, Qianyi Luo, Wentao Liu, Boyi Zhang, Ziyang Wang, Yaoyuan Liu, Jingtai Institute of Robotics and Automatic Information System Nankai University Tianjin Key Laboratory of Intelligent Robotics Tianjin China Advanced Computing and Storage Lab Huawei 2012 Lab China Department of Electronic and Computer Engineering The Kong Kong University of Science and Technology Hong Kong

Motion planning in navigation systems is highly susceptible to upstream perceptual errors, particularly in human detection and tracking. To mitigate this issue, the concept of guidance points—a novel directional cue within a reinforcement learning-based framework—is introduced. A structured method for identifying guidance points is developed, consisting of obstacle boundary extraction, potential guidance point detection, and redundancy elimination. To integrate guidance points into the navigation pipeline, a perception-to-planning mapping strategy is proposed, unifying guidance points with other perceptual inputs and enabling the RL agent to effectively leverage the complementary relationships among raw laser data, human detection and tracking, and guidance points. Qualitative and quantitative simulations demonstrate that the proposed approach achieves the highest success rate and near-optimal travel times, greatly improving both safety and efficiency. Furthermore, real-world experiments in dynamic corridors and lobbies validate the robot’s ability to confidently navigate around obstacles and robustly avoid pedestrians. © 2025, CC BY.

关键词： Travel time

Deep reinforcement learning-based trajectory planning for double pendulum cranes: Design and experiments

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Mechanical systems and Signal Processing 2025年 234卷

作者： Ding, Weili Zhang, Heng Hua, Changchun Lu, Biao Engineering Research Center of Intelligent Control System and Intelligent Equipment Ministry of Education Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province Institute of Electrical Engineering Yanshan University Qinhuangdao066004 China Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin300353 China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen518083 China

Since cranes usually possess double pendulum dynamics, the mass of the payload often changes and there are frequently lifting/lowering operations simultaneously. Moreover, most crane systems are driven by motors, whose velocities and accelerations are often limited. To solve the above problems, this paper proposes a deep reinforcement learning (DRL) reference trajectory generation method based on virtual–physical joint training. Firstly, a DRL module based on deep deterministic policy gradient (DDPG), along with a double pendulum crane dynamic model and an adaptive controller, are established within the virtual environment for training the reference trajectory. When the reward reaches the threshold, the training is switched to the physical environment to further optimize the reference trajectory, realizing the swing suppression of the hook and the payload. In addition, to satisfy the performance of the drive motors, velocity and acceleration thresholds are set to constrain the performance of the drive motors. Finally, in view of the fact that the operation process of the double pendulum crane is divided into payload transportation and payload loading/unloading, an event-triggering mechanism is designed to switch different control policies in accordance with different operation processes, thus reducing the consumption of computing resources. Through experiments on the actual double pendulum crane and comparison with the existing reference trajectories and input shapers, the superiority of this method is demonstrated. © 2025 Elsevier Ltd

关键词： Model reference adaptive control

Complete Gait Phases Recognition Based on Muscle Synergy Using PSO-CNN-LSTM Algorithm

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IEEE Sensors Journal 2025年第10期25卷 16775-16786页

作者： Zhang, Kewen Li, Xiaoling Chen, Xin Yu, Longjie Jin, Yinan Fan, Bingfei Du, Mingyu Bao, Guanjun Wu, Xinyu Cai, Shibo Zhejiang University of Technology College of Mechanical Engineering Key Laboratory of Special Purpose Equipment and Advanced Processing Technology Ministry of Education Hangzhou China Zhejiang University of Technology College of Mechanical Engineering Hangzhou China ZJUT Yinhu Research Institute of Innovation and Entrepreneurship ZJUT Yinhu Research Institute of Innovation and Entrepreneurship ZJUT Yinhu Research Institute of Innovation and Entrepreneurship Fuyang District Hangzhou311400 China Chinese Academy of Sciences Key Laboratory of Human-Machine-Intelligence Synergic Systems Shenzhen Institutes of Advanced Technology Shenzhen China Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Shenzhen China

Accurately recognizing gait phases, by applying proper instrumentation and measurement, is significant in walking rehabilitation training for patients with impaired mobility. In this study, seven phases of complete stand-walk-stand cycle as well as continuous daily walking were recognized based on muscle synergy and PSO-CNN-LSTM model. Firstly, the complete stand-walk-stand walking cycle were divided into starting phase, terminal swing phase, loading response phase, mid-stance phase, terminal stance phase, initial swing phase, and stopping phase, based on the features of surface electromyography (sEMG) collected by portable sEMG acquisition system and measured motion data of lower limb. Secondly, a muscle weight matrix and an activation sequence matrix were calculated by using non-negative matrix factorization (NMF). Finally, a PSO-CNN-LSTM network was designed to recognize the complete stand-walk-stand cycle by applying above mentioned matrices as input. Fourteen subjects volunteered to perform linear walking experiments under procedure to verify the feasibility of the proposed approach by comparing results with other classifiers and features. Experimental results show that the proposed approach was capable of achieving an average recognition accuracy of up to 85.384%. This work will offer promising gait recognition method for rehabilitation robots to achieve natural and flexible human-robot interaction. © 2001-2012 IEEE.

关键词： Non-negative matrix factorization

Influences of the ambient humidity and temperature on the material removal and wear behaviors on the soft-brittle KDP optics lithographed by the AFM probes

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Wear 2025年

作者： Chen, Guang Cheng, Jian Zhao, Linjie Chen, Mingjun Ding, Wenyu Lei, Hongqin Liu, Qi State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China Zhengzhou Research Institute Harbin Institute of Technology Zhengzhou450000 China Centre for Precision Manufacturing DMEM University of Strathclyde GlasgowG1 1XJ United Kingdom

KDP (K2HPO4) crystals possess excellent nonlinear electro-optical properties and are often employed as Pockels cells and frequency converters. Ink-free DPN (IF-DPN) provides a promising method to lithograph surface nano-patterns in KDP optics to improve their laser damage resistances. However, material removal and wear behaviors are greatly affected by humidity and temperature. In this work, theoretical analyses at atomic- and electronic-scales are performed based on molecular dynamics and ab initio to study lithography behaviors (indentation and scratching) for IF-DPN. Lithography experiments with different humidity and temperatures are also executed on KDP substrate, which align well with theoretical results. During indentation processes, surface materials are squeezed into substrate, leading to deep subsurface damages and significant material recoveries. While the scratching processes would cause intense surface material stripping, and sharp-edge of AFM probe facilitates this process. Numerous K-related chemical structure defects would emerge during the lithography processes, which is the predisposition of microstructure change inside KDP crystal. The binding processes of AFM probe, water, and KDP crystal are spontaneous exothermic reactions. Bonding reactions of Si-O, Si-H, and O-H covalent bonds would expedite the wear of AFM probe and KDP crystal. The humidity affects lithography behaviors mainly through chemical dissolution actions, reducing surface integrity and structural strength of KDP crystal and facilitating material stripping. The high humidity would intensify the hydroxylation reaction occurring on AFM probe and KDP crystal surfaces, enhancing their hydrophilicity. The high temperature would result in a reduction of the condensed water meniscus and adsorbed water film, weakening chemical actions of IF-DPN. But it is beneficial for the penetration of AFM probe into KDP crystal surface, material stripping, and minimization of subsurface damages. The hig

关键词： Molecular dynamics

SplatPose: Geometry-Aware 6-DoF Pose Estimation from Single RGB Image via 3D Gaussian Splatting

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arXiv 2025年

作者： Yang, Linqi Zhao, Xiongwei Sun, Qihao Wang, Ke Chen, Ao Kang, Peng State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150006 China Zhengzhou Research Institute Harbin Institute of Technology Zhengzhou450000 China Shenzhen518071 China Jianghuai Advance Technology Center Hefei230000 China

6-DoF pose estimation is a fundamental task in computer vision with wide-ranging applications in augmented reality and robotics. Existing single RGB-based methods often compromise accuracy due to their reliance on initial pose estimates and susceptibility to rotational ambiguity, while approaches requiring depth sensors or multi-view setups incur significant deployment costs. To address these limitations, we introduce SplatPose, a novel framework that synergizes 3D Gaussian Splatting (3DGS) with a dual-branch neural architecture to achieve high-precision pose estimation using only a single RGB image. Central to our approach is the Dual-Attention Ray Scoring Network (DARS-Net), which innovatively decouples positional and angular alignment through geometry-domain attention mechanisms, explicitly modeling directional dependencies to mitigate rotational ambiguity. Additionally, a coarse-to-fine optimization pipeline progressively refines pose estimates by aligning dense 2D features between query images and 3DGS-synthesized views, effectively correcting feature misalignment and depth errors from sparse ray sampling. Experiments on three benchmark datasets demonstrate that SplatPose achieves state-of-the-art 6-DoF pose estimation accuracy in single RGB settings, rivaling approaches that depend on depth or multi-view images. Copyright © 2025, The Authors. All rights reserved.

关键词： Gaussian distribution

Domain-Adaptive Power Profiling Analysis Strategy for the Metaverse

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Domain-Adaptive Power Profiling Analysis Strategy for the Me...

作者： Li, Xiang Yang, Ning Liu, Weifeng Chen, Aidong Zhang, Yanlong Wang, Shuo Zhou, Jing Beijing Key Laboratory of Information Service Engineering Beijing Union University Beijing China Institute of Semiconductors Chinese Academy of Sciences Beijing China College of Robotics Beijing Union University Beijing China Research Center for Multi-Intelligent Systems Beijing Union University Beijing China Information System Laboratory Beijing Microelectronics Technology Institution Beijing China

In the surge of the digital era, the metaverse, as a groundbreaking concept, has become a focal point in the technology sector. It is reshaping human work and life patterns, carving out a new realm of virtual and real interaction. However, the rapid development of the metaverse brings along novel challenges in security and privacy. In this multifaceted and complex technological environment, data protection is of paramount importance. The innovative capabilities of high-end devices and functions in the metaverse, owing to advanced integrated circuit technology, face unique threats from side-channel analysis (SCA), potentially leading to breaches in user privacy. Addressing the issue of domain differences caused by different hardware devices, which impact the generalizability of the analysis model and the accuracy of analysis, this paper proposes a strategy of portability power profiling analysis (PPPA). Combining domain adaptation and deep learning techniques, it models and calibrates the domain differences between the profiling and target devices, enhancing the model's adaptability in different device environments. Experiments show that our method can recover the correct key with as few as 389 power traces, effectively recovering keys across different devices. This paper underscores the effectiveness of cross-device SCA, focusing on the adaptability and robustness of analysis models in different hardware environments, thereby enhancing the security of user data privacy in the metaverse environment. © 2024 John Wiley & Sons Ltd.

关键词： Data privacy

Muscle Activation Estimation by Optimizing the Musculoskeletal Model for Personalized Strength and Conditioning Training

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arXiv 2025年

作者： Wu, Xi Li, Chenzui Zou, Kehan Xi, Ning Chen, Fei Department of Mechanical and Automation Engineering T-Stone Robotics Institute The Chinese University of Hong Kong Hong Kong Department of Industrial and Manufacturing System Engineering The University of Hong Kong Hong Kong

Musculoskeletal models are pivotal in the domains of rehabilitation and resistance training to analyze muscle conditions. However, individual variability in musculoskeletal parameters and the immeasurability of some internal biomechanical variables pose significant obstacles to accurate personalized modelling. Furthermore, muscle activation estimation can be challenging due to the inherent redundancy of the musculoskeletal system, where multiple muscles drive a single joint. This study develops a whole-body musculoskeletal model for strength and conditioning training and calibrates relevant muscle parameters with an electromyography-based optimization method. By utilizing the personalized musculoskeletal model, muscle activation can be subsequently estimated to analyze the performance of exercises. Bench press and deadlift are chosen for experimental verification to affirm the efficacy of this approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Electromyography

Modelling and experimental investigation of micro-dimpled structures milling with spiral trajectory tool reciprocating motion

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Chinese Journal of Aeronautics 2025年第02期 581-600页

作者： Guangzhou WANG Linjie ZHAO Qi LIU Xiguang LI Yazhou SUN Mingjun CHEN School of Mechatronics Engineering Harbin Institute of Technology Centre for Precision Manufacturing DMEMUniversity of Strathclyde State Key Laboratory of Robotics and System Harbin Institute of Technology

To mill fine and well-defined micro-dimpled structures, a machining manner of spiral trajectory tool reciprocating motion, where the tool repeats the process of ‘feed milling±retract±cutting feed±feed milling again' along the spiral trajectory, was proposed. From the kinematics analysis, it is found that the machining quality of micro-dimpled structures is highly dependent on the machining trajectory using spiral trajectory tool reciprocating motion. To reveal this causation, simulation modelling and experimental studies were carried out. A simulation model was developed to quantitatively and qualitatively investigate the influence of the trajectory discretization strategies(constant-angle and constant-arc length) and parameters(discrete angle, discrete arc length, and pitch) on surface texture and residual height of micro-dimpled structures. Subsequently, microdimpled structures were milled under different trajectory discretization strategies and parameters with spiral trajectory tool reciprocating motion. A comprehensive comparison between the milled results and simulation analysis was made based on geometry accuracy, surface morphology and surface roughness of milled dimples. Meanwhile, the errors and factors affecting the above three aspects were analyzed. The results demonstrate both the feasibility of the established simulation model and the machining capability of this machining way in milling high-quality micro-dimpled structures. Spiral trajectory tool reciprocating motion provides a new machining way for milling micro-dimpled structures and micro-dimpled functional surfaces. And an appropriate machining trajectory can be generated based on the optimized trajectory parameters, thus contributing to the improvement of machining quality and efficiency.

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