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Attention Mechanism based Pipe Recognition Network with a Hybrid Dataset

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IEEE Transactions on Automation Science and Engineering 2025年

作者： Tian, Yang Lin, Yueh Feng Ma, Shugen Shinshu University Department of Engineering Japan Seiko Instruments Inc Japan Robotics & Autonomous Systems Thrust China

A building information model for existing pipes is essential for maintenance tasks such as repairs, reconstruction, and modernization. However, current automatic recognition methods are not well-suited for handling complex piping systems on mobile devices. To address this, an attention mechanism-based recognition network is proposed. First, a decoder network is designed to minimize information loss during feature extraction. Its ability to integrate with various encoder networks allows for flexible network design tailored to different applications. Second, an extended convolution pyramid, tailored to the geometric characteristics of pipes, is introduced to enhance computational efficiency. The proposed network’s performance was evaluated through ablation studies and comparison experiments with state-of-the-art methods. To address the challenge of data scarcity, a hybrid dataset was developed using neural style transfer techniques, and an evaluation experiment demonstrated its effectiveness. © 2004-2012 IEEE.

关键词： Object recognition

Interactive Navigation for Legged Manipulators with Learned Arm-Pushing Controller

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arXiv 2025年

作者： Bi, Zhihai Chen, Kai Zheng, Chunxin Li, Yulin Li, Haoang Ma, Jun Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology Guangzhou China

Interactive navigation is crucial in scenarios where proactively interacting with objects can yield shorter paths, thus significantly improving traversal efficiency. Existing methods primarily focus on using the robot body to relocate large obstacles (which could be comparable to the size of a robot). However, they prove ineffective in narrow or constrained spaces where the robot’s dimensions restrict its manipulation capabilities. This paper introduces a novel interactive navigation framework for legged manipulators, featuring an active arm-pushing mechanism that enables the robot to reposition movable obstacles in space-constrained environments. To this end, we develop a reinforcement learning-based arm-pushing controller with a two-stage reward strategy for large-object manipulation. Specifically, this strategy first directs the manipulator to a designated pushing zone to achieve a kinematically feasible contact configuration. Then, the end effector is guided to maintain its position at appropriate contact points for stable object displacement while preventing toppling. The simulations validate the robustness of the arm-pushing controller, showing that the two-stage reward strategy improves policy convergence and long-term performance. Real-world experiments further demonstrate the effectiveness of the proposed navigation framework, which achieves shorter paths and reduced traversal time. The open-source project can be found at https://***/Zhihaibi/ Interactive-Navigation-for-legged-manipulator. git. Copyright © 2025, The Authors. All rights reserved.

关键词： End effectors

Order-Reduced Nonlinear Semi-Analytical Multiphysics Model for Axial-Flux Motors

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IEEE Transactions on Transportation Electrification 2025年

作者： Su, Xiangdong Li, Fang Yin, Zhenxiao Liu, Xuyang Zhao, Hang Thrust of Robotics and Autonomous Systems Guangzhou510000 China Direct Drive Technology Research and Development Center Dongguan523000 China

Electromagnetic-thermal analysis is crucial to guarantee the axial flux motors' (AFMs) reliable operation as their compact size and poor heat dissipation conditions can lead to thermal limits. However, the conventional coupled analysis is extremely time-consuming, especially in 3D analysis. To this end, a fast semi-analytical electromagnetic-thermal coupling calculation framework is proposed that couples a harmonic modeling method (HMM) with a lumped parameter thermal network (LPTN), incorporating heat transfer mechanisms including conduction, convection, and radiation. In the electromagnetic field analysis part, an order-reduced electromagnetic model is constructed using the HMM considering the nonlinearity of the iron core. Based on that, a bi-directional electromagnetic-thermal coupling analysis is realized, the variation of electromagnetic properties with temperature, including copper conductivity, permanent magnet (PM) remanence, and the permeability of the iron core, are incorporated into the coupling analysis. Notably, the stator teeth are finely segmented in the electromagnetic model to accurately capture magnetic saturation effects under varying temperature conditions. Ultimately, the reliability and accuracy of the proposed method are verified through the finite element method (FEM) and prototype experiments. It is demonstrated that compared with FEM, the proposed framework has a satisfactory calculation efficiency and saves 98.4% of the computation time than FEM, making it suitable for preliminary motor design and optimization considering multiphysics influence. © 2015 IEEE.

关键词： Permanent magnets

Bilevel Multi-Armed Bandit-Based Hierarchical Reinforcement Learning for Interaction-Aware Self-Driving At Unsignalized Intersections

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IEEE Transactions on Vehicular Technology 2025年

作者： Peng, Zengqi Wang, Yubin Zheng, Lei Ma, Jun Robotics and Autonomous Systems Thrust Guangzhou511453 China The Hong Kong University of Science and Technology Division of Emerging Interdisciplinary Areas Hong Kong Hong Kong

In this work, we present BiM-ACPPO, a bilevel multi-armed bandit-based hierarchical reinforcement learning framework for interaction-aware decision-making and planning at unsignalized intersections. Essentially, it proactively takes the uncertainties associated with surrounding vehicles (SVs) into consideration, which encompass those stemming from the driver's intention, interactive behaviors, and the varying number of SVs. Intermediate decision variables are introduced to enable the high-level RL policy to provide an interaction-aware reference, for guiding low-level model predictive control (MPC) and further enhancing the generalization ability of the proposed framework. By leveraging the structured nature of self-driving at unsignalized intersections, the training problem of the RL policy is modeled as a bilevel curriculum learning task, which is addressed by the proposed Exp3.S-based BiMAB algorithm. It is noteworthy that the training curricula are dynamically adjusted, thereby facilitating the sample efficiency of the RL training process. Comparative experiments are conducted in the high-fidelity CARLA simulator, and the results indicate that our approach achieves superior performance compared to all baseline methods. Furthermore, experimental results in two new urban driving scenarios clearly demonstrate the commendable generalization performance of the proposed method. © 1967-2012 IEEE.

关键词： Reinforcement learning

MF-BERT: A Siamese Pre-training Framework for Motion Forecasting

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MF-BERT: A Siamese Pre-training Framework for Motion Forecas...

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jianxin Shi Jinhao Chen Xiaolong Chen Jun Ma Tianyu Wo School of Computer Science and Engineering Beihang University Beijing China Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology Guangzhou

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

关键词： Measurement Supervised learning Self-supervised learning Predictive models Signal processing Feature extraction Trajectory Forecasting Speech processing Standards

Motion-Coupled Mapping Algorithm for Hybrid Rice Canopy

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arXiv 2025年

作者： Feng, Huaiqu Zhao, Guoyang Liu, Cheng Wang, Yongwei Wang, Jun College of Biosystems Engineering and Food Science Zhejiang University China Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology Guangzhou China

This paper presents a motion-coupled mapping algorithm for contour mapping of hybrid rice canopies, specifically designed for Agricultural Unmanned Ground Vehicles (Agri-UGV) navigating complex and unknown rice fields. Precise canopy mapping is essential for Agri-UGVs to plan efficient routes and avoid protected zones. The motion control of Agri-UGVs, tasked with impurity removal and other operations, depends heavily on accurate estimation of rice canopy height and structure. To achieve this, the proposed algorithm integrates real-time RGB-D sensor data with kinematic and inertial measurements, enabling efficient mapping and proprioceptive localization. The algorithm produces grid-based elevation maps that reflect the probabilistic distribution of canopy contours, accounting for motion-induced uncertainties. It is implemented on a high-clearance Agri-UGV platform and tested in various environments, including both controlled and dynamic rice field settings. This approach significantly enhances the mapping accuracy and operational reliability of Agri-UGVs, contributing to more efficient autonomous agricultural operations. Copyright © 2025, The Authors. All rights reserved.

关键词： Mapping

SCORE: Saturated Consensus Relocalization in Semantic Line Maps

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arXiv 2025年

作者： Jiang, Haodong Zheng, Xiang Zhang, Yanglin Zeng, Qingcheng Li, Yiqian Hong, Ziyang Wu, Junfeng School of Data Science The Chinese University of Hong Kong Shenzhen China Robotics and Autonomous Systems Thrust System Hub The Hong Kong University of Science and Technology Guangzhou China

This is the arxiv version for our paper submitted to IEEE/RSJ IROS 2025. We propose a scene-agnostic and light-weight visual relocalization framework that leverages semantically labeled 3D lines as a compact map representation. In our framework, the robot localizes itself by capturing a single image, extracting 2D lines, associating them with semantically similar 3D lines in the map, and solving a robust perspective-n-line problem. To address the extremely high outlier ratios (exceeding 99.5%) caused by one-to-many ambiguities in semantic matching, we introduce the Saturated Consensus Maximization (Sat-CM) formulation, which enables accurate pose estimation when the classic Consensus Maximization framework fails. We further propose a fast global solver to the formulated Sat-CM problems, leveraging rigorous interval analysis results to ensure both accuracy and computational efficiency. Additionally, we develop a pipeline for constructing semantic 3D line maps using posed depth images. To validate the effectiveness of our framework, which integrates our innovations in robust estimation and practical engineering insights, we conduct extensive experiments on the ScanNet++ dataset. Copyright © 2025, The Authors. All rights reserved.

关键词：

CoDriveVLM: VLM-Enhanced Urban Cooperative Dispatching and Motion Planning for Future autonomous Mobility on Demand systems

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arXiv 2025年

作者： Liu, Haichao Yao, Ruoyu Liu, Wenru Huang, Zhenmin Shen, Shaojie Ma, Jun The Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology Guangzhou China The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

The increasing demand for flexible and efficient urban transportation solutions has spotlighted the limitations of traditional Demand Responsive Transport (DRT) systems, particularly in accommodating diverse passenger needs and dynamic urban environments. autonomous Mobility-on-Demand (AMoD) systems have emerged as a promising alternative, leveraging connected and autonomous vehicles (CAVs) to provide responsive and adaptable services. However, existing methods primarily focus on either vehicle scheduling or path planning, which often simplify complex urban layouts and neglect the necessity for simultaneous coordination and mutual avoidance among CAVs. This oversimplification poses significant challenges to the deployment of AMoD systems in real-world scenarios. To address these gaps, we propose CoDriveVLM, a novel framework that integrates high-fidelity simultaneous dispatching and cooperative motion planning for future AMoD systems. Our method harnesses Vision-Language Models (VLMs) to enhance multi-modality information processing, and this enables comprehensive dispatching and collision risk evaluation. The VLM-enhanced CAV dispatching coordinator is introduced to effectively manage complex and unforeseen AMoD conditions, thus supporting efficient scheduling decision-making. Furthermore, we propose a scalable decentralized cooperative motion planning method via consensus alternating direction method of multipliers (ADMM) focusing on collision risk evaluation and decentralized trajectory optimization. Simulation results demonstrate the feasibility and robustness of CoDriveVLM in various traffic conditions, showcasing its potential to significantly improve the fidelity and effectiveness of AMoD systems in future urban transportation networks. The code is available at https://***/henryhcliu/***. Copyright © 2025, The Authors. All rights reserved.

关键词： Risk assessment

LearningFlow: Automated Policy Learning Workflow for Urban Driving with Large Language Models

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arXiv 2025年

作者： Peng, Zengqi Wang, Yubin Han, Xu Zheng, Lei Ma, Jun Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology Guangzhou China Data Science and Analytics Thrust The Hong Kong University of Science and Technology Guangzhou China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology China

Recent advancements in reinforcement learning (RL) demonstrate the significant potential in autonomous driving. Despite this promise, challenges such as the manual design of reward functions and low sample efficiency in complex environments continue to impede the development of safe and effective driving policies. To tackle these issues, we introduce LearningFlow, an innovative automated policy learning workflow tailored to urban driving. This framework leverages the collaboration of multiple large language model (LLM) agents throughout the RL training process. LearningFlow includes a curriculum sequence generation process and a reward generation process, which work in tandem to guide the RL policy by generating tailored training curricula and reward functions. Particularly, each process is supported by an analysis agent that evaluates training progress and provides critical insights to the generation agent. Through the collaborative efforts of these LLM agents, LearningFlow automates policy learning across a series of complex driving tasks, and it significantly reduces the reliance on manual reward function design while enhancing sample efficiency. Comprehensive experiments are conducted in the high-fidelity CARLA simulator, along with comparisons with other existing methods, to demonstrate the efficacy of our proposed approach. The results demonstrate that LearningFlow excels in generating rewards and curricula. It also achieves superior performance and robust generalization across various driving tasks, as well as commendable adaptation to different RL algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning