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arXiv 2024年

作者： Huang, Tianyu Li, Haoang Peng, Liangzu Liu, Yinlong Liu, Yun-Hui The Department of Mechanical and Automation Engineering T Stone Robotics Institute The Chinese University of Hong Kong Hong Kong The Thrust of Robotics and Autonomous Systems and the Thrust of Intelligent Transportation The Hong Kong University of Science and Technology Guangzhou China University of Macau 999078 China

Estimating the rigid transformation with 6 degrees of freedom based on a putative 3D correspondence set is a crucial procedure in point cloud registration. Existing correspondence identification methods usually lead to large outlier ratios (> 95 % is common), underscoring the significance of robust registration methods. Many researchers turn to parameter search-based strategies (e.g., Branch-and-Bround) for robust registration. Although related methods show high robustness, their efficiency is limited to the high-dimensional search space. This paper proposes a heuristics-guided parameter search strategy to accelerate the search while maintaining high robustness. We first sample some correspondences (i.e., heuristics) and then just need to sequentially search the feasible regions that make each sample an inlier. Our strategy largely reduces the search space and can guarantee accuracy with only a few inlier samples, therefore enjoying an excellent trade-off between efficiency and robustness. Since directly parameterizing the 6-dimensional nonlinear feasible region for efficient search is intractable, we construct a three-stage decomposition pipeline to reparameterize the feasible region, resulting in three lower-dimensional sub-problems that are easily solvable via our strategy. Besides reducing the searching dimension, our decomposition enables the leverage of 1-dimensional interval stabbing at all three stages for searching acceleration. Moreover, we propose a valid sampling strategy to guarantee our sampling effectiveness, and a compatibility verification setup to further accelerate our search. Extensive experiments on both simulated and real-world datasets demonstrate that our approach exhibits comparable robustness with state-of-the-art methods while achieving a significant efficiency boost. © 2024, CC BY.

关键词： Efficiency

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Design of Lock/Unlock Controllable Joint for Wire-driven Robotic Arms

Design of Lock/Unlock Controllable Joint for Wire-driven Rob...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Yang Zhang Zaiyang Liu Atsushi Kakogawa Yang Tian Shugen Ma Zhongkui Wang Graduate School of Science and Engineering Ritsumeikan University Shiga Japan Department of Robotics Ritsumeikan University Shiga Japan Robotics and Autonomous Systems Thrust Systems Hub The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China

ISBN: (数字)9798350372601

ISBN: (纸本)9798350372618

Wire-driven robotic arms are characterized by high redundancy and a high length-to-diameter ratio, enabling them to navigate and explore effectively in confined environments by controlling and changing their configurations timely, such as during internal reactor inspections following accidents at nuclear facilities. Their flexibility is achieved by changing the configurations of the robotic arms, which can be controlled by using multiple wires in each segment for control, or by using combinations with fewer wires and lock/unlock joint mechanisms. Such mechanisms (e.g., latch) have some limitations in terms of control, energy, response, and long-term reliability. To address, we designed a lock/unlock controllable joint for wire-driven robotic arms using the electromagnetic brake, which can improve the joint capability. This preliminary study progressed through prototype design, control system design, and preliminary testing. We analyzed the challenges associated with existing wire-driven robotic arms with lock/unlock joints. A prototype and control system were then designed and preliminarily tested. Based on the results, the usability of this joint was discussed. This preliminary study laid the groundwork for the subsequent optimization of wire-driven robotic arms.

关键词： Program processors Wires Redundancy Prototypes Arms Manipulators Real-time systems

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Design and Analysis of Fault-Tolerant IPM Considering Bidirectional Electromagnetic-Thermal Coupling Constraints

Design and Analysis of Fault-Tolerant IPM Considering Bidire...

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International Conference on Electrical Machines and systems (ICEMS)

作者： Yiming Ma Xiangdong Su Zhenxiao Yin Yujia Zhang Hang Zhao CSG PGC Energy Storage Research Institute Guangzhou China Thrust of Robotics and Autonomous Systems The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China

ISBN: (数字)9784886864406

ISBN: (纸本)9798350379105

In this article, a five-phase interior permanent magnet motor (IPM) is designed and fabricated, featuring higher torque density and reduced torque ripple, making it suitable for demanding applications such as aviation electrification. The motor's performance is further evaluated through a bidirectional electromagnetic-thermal coupling analysis. Compared to separated electromagnetic and thermal analyses, the bidirectional approach provides more accurate assessments of the motor's electromagnetic behavior under temperature rise conditions. Additionally, the torque characteristics of the fault-tolerant IPM in its fault operation mode are analyzed, offering insights into the reliability of multiphase motors for aviation electrification. The research also examines the variations and disparity (19.98 % to 40 % electromagnetic torque deviation) of the electromagnetic characteristics under fault conditions. This analysis helps to depict the effects of motor faults on other subsystems in complicated aviation systems, offering further and a more comprehensive understanding of the design and operation challenges and impacts associated with multiphase motor faults in aviation electric propulsion systems (EPS).

关键词： Couplings Fault tolerance Torque Fault tolerant systems Electrification Reliability theory Propulsion Permanent magnet motors Torque measurement Electromagnetics

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SCORE: Saturated Consensus Relocalization in Semantic Line Maps

arXiv

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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.

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

arXiv

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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

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A Centimeter-Scale Inspection Robot for Locomotion in Viscous Fluids

A Centimeter-Scale Inspection Robot for Locomotion in Viscou...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Jun Zhang Zaiyang Liu Zhenxuan Ma Yang Tian Shugen Ma Zhongkui Wang Graduate School of Science and Engineering Ritsumeikan University Kusatsu Shiga Japan Department of Robotics Ritsumeikan University Kusatsu Shiga Japan Robotics & Autonomous Systems Thrust Systems Hub The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China

ISBN: (数字)9798350372601

ISBN: (纸本)9798350372618

Nature has inspired many designs for micro-robot locomotion, particularly helical propulsion inspired by Escherichia coli and spermatozoa, which has found widespread applications in medical micro-robots for generating thrust at low Reynolds numbers. However, the design and fabrication of small robots for inspection tasks, especially in industrial settings filled with highly viscous liquids, remain challenging. In this paper, we propose a robot capable of locomotion in constrained environments filled with high-viscosity fluids. The robot is propelled by two helical propellers, enabling it to move effectively at low Reynolds numbers. The optimal parameters for the helical propellers are analyzed using Resistive Force Theory (RFT), and structures are designed and experimentally validated accordingly. Finite element analysis is employed to determine the suitable body for the robot. The robot prototype is fabricated using 3D printing. Finally, locomotion experiment was conducted to validate the feasibility of the robot moving in viscous liquid.

关键词： Fabrication Liquids Service robots Propellers Shape Computational modeling Prototypes

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A Universal Multi-Vehicle Cooperative Decision-Making Approach in Structured Roads by Mixed-Integer Potential Game

arXiv

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arXiv 2024年

作者： Meng, Chengzhen Huang, Zhenmin 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

Due to the intricate of real-world road topologies and the inherent complexity of autonomous vehicles, cooperative decision-making for multiple connected autonomous vehicles (CAVs) remains a significant challenge. Currently, most methods are tailored to specific scenarios, and the efficiency of existing optimization and learning methods applicable to diverse scenarios is hindered by the complexity of modeling and data dependency, which limit their real-world applicability. To address these issues, this paper proposes a universal multi-vehicle cooperative decision-making method in structured roads with game theory. We transform the decision-making problem into a graph path searching problem within a way-point graph framework. The problem is formulated as a mixed-integer linear programming problem (MILP) first and transformed into a mixed-integer potential game (MIPG), which reduces the scope of problem and ensures that no player needs to sacrifice for the overall cost. Two Gauss-Seidel algorithms for cooperative decision-making are presented to solve the MIPG problem and obtain the Nash equilibrium solutions. Specifically, the sequential Gauss-Seidel algorithm for cooperative decision-making considers the varying degrees of CAV interactions and flexibility in adjustment strategies to determine optimization priorities, which reduces the frequency of ineffective optimizations. Experimental evaluations across various urban traffic scenarios with different topological structures demonstrate the effectiveness and efficiency of the proposed method compared with MILP and comparisons of different optimization sequences validate the efficiency of the sequential Gauss-Seidel algorithm for cooperative decision-making. Copyright © 2024, The Authors. All rights reserved.

关键词： Mixed-integer linear programming

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Robust Optimization of a Stator Tooth Chamfering IPMSM for Electric Power Steering Applications

Robust Optimization of a Stator Tooth Chamfering IPMSM for E...

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2024 International Conference on Electrical Machines, ICEM 2024

作者： Liu, Liyang Ma, Yiming Wang, Ruichi Li, Zequan Sun, Lu Zhao, Hang Robotics and Autonomous Systems Thrust Guangzhou China CSG PGC Energy Storage Research Institute China Southern Power Grid Energy Storage Co. Ltd Guangzhou China School of Mechanical Electrical and Manufacturing Loughborough University Loughborough United Kingdom School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350370607

The impact of manufacturing errors on motor performance is a crucial issue in the mass production of electric motor products. However, robust indexes are complicated to consider in the design process due to the enormous computational complexity. This paper presents a surrogate- assisted robust optimization method that integrates sensitivity analysis with Pareto front infill criteria. The approach enables efficient sampling in the regions where the optimal solution is most probable, thereby ensuring the local generalization capability of the surrogate model. As a result, robust optimization of probabilistic manufacturing error variables is effectively accomplished. An interior permanent-magnet synchronous machine (IPMSM) employing the stator tooth chamfering technique to mitigate cogging torque is examined for electric power steering applications. To verify the effectiveness of this approach, a limited series of prototypes were fabricated and rigorously tested, confirming the practicality of the proposed method. © 2024 IEEE.

关键词： Stators

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GS-LIVM: Real-Time Photo-Realistic LiDAR-Inertial-Visual Mapping with Gaussian Splatting

arXiv

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arXiv 2024年

作者： Xie, Yusen Huang, Zhenmin Wu, Jin Ma, Jun Robotics and Autonomous Systems 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 Hong Kong

In this paper, we introduce GS-LIVM, a real-time photorealistic LiDAR-Inertial-Visual mapping framework with Gaussian Splatting tailored for outdoor scenes. Compared to existing methods based on Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), our approach enables real-time photo-realistic mapping while ensuring high-quality image rendering in large-scale unbounded outdoor environments. In this work, Gaussian Process Regression (GPR) is employed to mitigate the issues resulting from sparse and unevenly distributed LiDAR observations. The voxel-based 3D Gaussians map representation facilitates real-time dense mapping in large outdoor environments with acceleration governed by custom CUDA kernels. Moreover, the overall framework is designed in a covariance-centered manner, where the estimated covariance is used to initialize the scale and rotation of 3D Gaussians, as well as update the parameters of the GPR. We evaluate our algorithm on several outdoor datasets, and the results demonstrate that our method achieves state-of-the-art performance in terms of mapping efficiency and rendering quality. The source code is available on GitHub . Copyright © 2024, The Authors. All rights reserved.

关键词： Mapping

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

arXiv

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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

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