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Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE International

作者： Wenyuan Mi Jincheng Yu Zaixin Song Kuang Yang Zheng Cai Hang Zhao School of Mechanical Engineering and Automation Harbin Institute of Technology (Shenzhen) Shenzhen China Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hong Kong SAR China Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China

Permanent magnet (PM) resolvers are highly employed with the ascent of the electric machine speed. In this digest, a novel modular flux switching PM (FSPM) resolver is designed based on the FSPM machine. By removing the stator yoke, the flux path in each tooth coil is fixed and the total harmonic distortion (THD) of the output voltage is reduced to only 0.37%. Moreover, the proposed 12/16 FSPM resolver can be adapted to fit different rotor poles under the same mechanical support with different modular units (e.g., 6/8 and 10/8 FSPM resolver). Therefore, it can be applied in electric machines with variable rotor pole pairs.

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Preference Aligned Diffusion Planner for Quadrupedal Locomotion Control

arXiv

引用

arXiv 2024年

作者： Yuan, Xinyi Shang, Zhiwei Wang, Zifan Wang, Chenkai Shan, Zhao Zhu, Meixin Bai, Chenjia Li, Xuelong Wan, Weiwei Harada, Kensuke Graduate School of Engineering Science Osaka University Japan Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology Guangzhou China China Telecom China Department of Statistics and Data Science Southern University of Science and Technology China School of Transportation Southeast University China

Diffusion models demonstrate superior performance in capturing complex distributions from large-scale datasets, providing a promising solution for quadrupedal locomotion control. However, the robustness of the diffusion planner is inherently dependent on the diversity of the pre-collected datasets. To mitigate this issue, we propose a two-stage learning framework to enhance the capability of the diffusion planner under limited dataset (reward-agnostic). Through the offline stage, the diffusion planner learns the joint distribution of state-action sequences from expert datasets without using reward labels. Subsequently, we perform the online interaction in the simulation environment based on the trained offline planner, which significantly diversified the original behavior and thus improves the robustness. Specifically, we propose a novel weak preference labeling method without the ground-truth reward or human preferences. The proposed method exhibits superior stability and velocity tracking accuracy in pacing, trotting, and bounding gait under different speeds and can perform a zero-shot transfer to the real Unitree Go1 robots. The project website for this paper is at https://***. io/preference-aligned-diffusion-legged/. Copyright © 2024, The Authors. All rights reserved.

关键词： Robustness (control systems)

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Reward-Driven Automated Curriculum Learning for Interaction-Aware Self-Driving at Unsignalized Intersections

Reward-Driven Automated Curriculum Learning for Interaction-...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Zengqi Peng Xiao Zhou Lei Zheng Yubin Wang Jun Ma Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Division of Emerging Interdisciplinary Areas The Hong Kong University of Science and Technology Hong Kong SAR China HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Futian China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In this work, we present a reward-driven automated curriculum reinforcement learning approach for interaction-aware self-driving at unsignalized intersections, taking into account the uncertainties associated with surrounding vehicles (SVs). These uncertainties encompass the uncertainty of SVs’ driving intention and also the quantity of SVs. To deal with this problem, the curriculum set is specifically designed to accommodate a progressively increasing number of SVs. By implementing an automated curriculum selection mechanism, the importance weights are rationally allocated across various curricula, thereby facilitating improved sample efficiency and training outcomes. Furthermore, the reward function is meticulously designed to guide the agent towards effective policy exploration. Thus the proposed framework could proactively address the above uncertainties at unsignalized intersections by employing the automated curriculum learning technique that progressively increases task difficulty, and this ensures safe self-driving through effective interaction with SVs. Comparative experiments are conducted in Highway_Env, and the results indicate that our approach achieves the highest task success rate, attains strong robustness to initialization parameters of the curriculum selection module, and exhibits superior adaptability to diverse situational configurations at unsignalized intersections. Furthermore, the effectiveness of the proposed method is validated using the high-fidelity CARLA simulator.

关键词： Training Uncertainty Reinforcement learning Robustness Timing Resource management Intelligent robots autonomous vehicles

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An Image Acquisition Scheme for Visual Odometry based on Image Bracketing and Online Attribute Control

An Image Acquisition Scheme for Visual Odometry based on Ima...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Shuyang Zhang Jinhao He Bohuan Xue Jin Wu Pengyu Yin Jianhao Jiao Ming Liu Department of Electronic and Computer Engineering the Hong Kong University of Science and Technology Hong Kong SAR China Thrust of Robotics and Autonomous Systems the Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Center for Advanced Robotics Technology Innovation (CARTIN) School of Electrical and Electronic Engineering Nanyang Technological University Singapore Department of Computer Science University College London London UK

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Visual odometry (VO) system is challenged by complex illumination environments. Image quality and its consistency in the time domain directly determine feature detection and tracking performance, which further affect the robustness and accuracy of the entire system. In this paper, an image acquisition scheme with image bracketing patterns is proposed. Images with different exposure levels are continuously captured to sufficiently explore the scene under varying illumination. An attribute control method is designed to adjust image exposures within the brackets online. Gaussian process regression fits the relationship between image quality metric and exposure via image synthesis technique. The optimal exposures for the next bracket are obtained directly without attempts to ensure a quick response. Experiments show our acquisition system’s effectiveness and performance improvement for VO tasks in complex illumination scenes.

关键词： Image quality Measurement Image synthesis Lighting Gaussian processes Robustness Time-domain analysis

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Design Considerations of Outer-Rotor Flux-Modulated Permanent-Magnet In-Wheel Motors

Design Considerations of Outer-Rotor Flux-Modulated Permanen...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Zekang Huang Yixiao Luo Wenyuan Mi Zheng Cai Fei Zhao Hang Zhao Jincheng Yu School of Mechanical Engineering and Automation Harbin Institute of Technology (Shenzhen) Shenzhen China School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China Robotics and Autonomous Systems Thrust Hong Kong University of Science and Technology (Guangzhou) Guangzhou China

This paper presents the design considerations of outer-rotor flux-modulated permanent-magnet (OR-FMPM) in-wheel motors applied to light electric vehicles (EVs). The basic wheel model and drive system specifications are considered in the motor design, with utility of the flat-wire armature windings. The multi-step motor optimization focusing on the outer-rotor PM machines is carried out to reach the design objectives of the drive system, with auxiliary of the sensitivity analysis to lower the computing burden. Finally, the motor electromagnetic performances are specifically analyzed, verifying the feasibility of the motor design in the application of in-wheel motors.

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RiskMap: A Unified Driving Context Representation for autonomous Motion Planning in Urban Driving Environment

RiskMap: A Unified Driving Context Representation for Autono...

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IEEE International Conference on robotics and Biomimetics

作者： Ren Xin Sheng Wang Yingbing Chen Jie Cheng Ming Liu Jun Ma Division of Emerging Interdisciplinary Areas The Hong Kong University of Science and Technology Hong Kong SAR China Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong SAR China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Motion planning is a complicated task that requires the combination of perception, map information integration and prediction, particularly when driving in heavy traffic. Developing an extensible and efficient representation that visualizes sensor noise and provides basis to real-time planning tasks is desirable. We aim to develop an interpretable map representation, which offers prior of driving cost in planning tasks. In this way, we can simplify the planning process for dealing with complex driving scenarios and visualize sensor noise. Specifically, we propose a unified context representation empowered by deep neural networks. The unified representation is a differentiable risk field, which is an analytical representation of statistical cognition regarding traffic participants for downstream planning tasks. This representation method is nominated as RiskMap. A sampling-based planner is adopted to train and compare RiskMap generation methods. In this paper, the RiskMap generation tools and model structures are explored, the results illustrate that our method can improve driving safety and smoothness, and the limitation of our method is also discussed.

关键词： Training Visualization Analytical models Noise Robot sensing systems Real-time systems Planning Safety Driver behavior Tuning

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Feasibility Investigation on Sinusoidal-Reluctance Rotor Contour in Variable Reluctance Resolver

Feasibility Investigation on Sinusoidal-Reluctance Rotor Con...

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Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE International

作者： Wenyuan Mi Jincheng Yu Zheng Cai Hang Zhao Fei Zhao Yixiao Luo School of Mechanical Engineering and Automation Harbin Institute of Technology (Shenzhen) Shenzhen China Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China

In this digest, a novel variable reluctance (VR) resolver with sinusoidal-reluctance rotor contour is proposed and its envelope feasibility is evaluated via the analytical and finite element (FE) analysis. Moreover, the third order harmonic suppression via flux leakage compensation is addressed to reveal the operation principle of the proposed VR resolver. Under 4 mm tooth width, the total harmonic distortion of the cosine envelope with the proposed rotor contour can be limited to only 0.56%.

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A Translation-Tolerant Place Recognition Method by Viewpoint Unification

A Translation-Tolerant Place Recognition Method by Viewpoint...

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International Conference on Intelligent Transportation

作者： Linwei Zheng Xiangcheng Hu Fulong Ma Guoyang Zhao Weiqing Qi Jun Ma Ming Liu Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong SAR China Division of Emerging Interdisciplinary Areas The Hong Kong University of Science and Technology Hong Kong SAR China

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Place recognition serves as a fundamental component in tasks like loop closure detection and relocalization for mobile robots. Polar coordinate representations, such as Scan Context, which align with the data structure of range sensors, have become the most common data structure for point cloud descriptors in place recognition. While polar representations demonstrate the rotation invariance, they remain susceptible to translation variations. In this study, we introduce a novel approach: shifting the viewpoint of the original point cloud to construct the unified Scan Context, thereby mitigating translation variance. Our key concept focuses on identifying a stable, unified viewpoint for a given place and then pre-translating the point cloud accordingly. This naturally results in a descriptor devoid of translation variance. Importantly, within a given place, the viewpoint unification process tends to relocate the viewpoint to a similar position, irrespective of the original sensor perspective. In other words, the unified Scan Context becomes more closely associated with the place's structural characteristics than the physical location of the sensor. We validate our method through a series of comprehensive experiments encompassing synthetic scenarios and real-world datasets, showcasing its robustness in effectively handling translation variations.

关键词： Point cloud compression Translation Sensor phenomena and characterization Data structures Robustness Mobile robots Intelligent transportation systems

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Vivim: a Video Vision Mamba for Ultrasound Video Segmentation

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Yang, Yijun Xing, Zhaohu Yu, Lequan Fu, Huazhu Huang, Chunwang Zhu, Lei Nansha Guangdong Guangzhou511400 China School of Computing and Data Science University of Hong Kong Hong Kong 138632 Singapore Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Southern Medical University Department of Ultrasound China Robotics and Autonomous Systems Thrust China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong

Ultrasound video segmentation gains increasing attention in clinical practice due to the redundant dynamic references in video frames. However, traditional convolutional neural networks have a limited receptive field and transformer-based networks are unsatisfactory in constructing long-term dependency from the perspective of computational complexity. This bottleneck poses a significant challenge when processing longer sequences in medical video analysis tasks using available devices with limited memory. Recently, state space models (SSMs), famous by Mamba, have exhibited linear complexity and impressive achievements in efficient long sequence modeling, which have developed deep neural networks by expanding the receptive field on many vision tasks significantly. Unfortunately, vanilla SSMs failed to simultaneously capture causal temporal cues and preserve non-casual spatial information. To this end, this paper presents a Video Vision Mamba-based framework, dubbed as Vivim, for ultrasound video segmentation tasks. Our Vivim can effectively compress the long-term spatiotemporal representation into sequences at varying scales with our designed Temporal Mamba Block. We also introduce an improved boundary-aware affine constraint across frames to enhance the discriminative ability of Vivim on ambiguous lesions. Extensive experiments on thyroid segmentation in ultrasound videos, breast lesion segmentation in ultrasound videos, and polyp segmentation in colonoscopy videos demonstrate the effectiveness and efficiency of our Vivim, superior to existing methods. © 1991-2012 IEEE.

关键词： Image segmentation

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Viko 2.0: A Hierarchical Gecko-inspired Adhesive Gripper with Visuotactile Sensor

arXiv

引用

arXiv 2022年

作者： Pang, Chohei Wang, Qicheng Mak, Kinwing Yu, Hongyu Wang, Michael Yu The Department of Mechanical and Aerospace Engineering Hong Kong University of Science and Technology Hong Kong Q. Wang is with Robotics and Autonomous Systems Thrust Systems Hub Hong Kong University of Science and Technology Hong Kong

Robotic grippers with visuotactile sensors have access to rich tactile information for grasping tasks but encounter difficulty in partially encompassing large objects with sufficient grip force. While hierarchical gecko-inspired adhesives are a potential technique for bridging performance gaps, they require a large contact area for efficient usage. In this work, we present a new version of an adaptive gecko gripper called Viko 2.0 that effectively combines the advantage of adhesives and visuotactile sensors. Compared with a non-hierarchical structure, a hierarchical structure with a multi-material design achieves approximately a 1.5 times increase in normal adhesion and double in contact area. The integrated visuotactile sensor captures a deformation image of the hierarchical structure and provides a real-time measurement of contact area, shear force, and incipient slip detection at 24 Hz. The gripper is implemented on a robotic arm to demonstrate an adaptive grasping pose based on contact area, and grasps objects with a wide range of geometries and textures. Copyright © 2022, The Authors. All rights reserved.

关键词： Grippers

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