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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2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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APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semantic Segmentation

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APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semanti...

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Weizhao He Yang Zhang Wei Zhuo Linlin Shen Jiaqi Yang Songhe Deng Liang Sun Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen Institute of Artificial Intelligence and Robotics for Society School of Computer Science University of Nottingham China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Few-shot semantic segmentation (FSS) endeavors to segment unseen classes with only a few labeled samples. Current FSS methods are commonly built on the assumption that their training and application scenarios share similar domains, and their performances degrade significantly while applied to a distinct domain. To this end, we propose to leverage the cutting-edge foundation model, the segment Anything Model (SAM), for generalization enhancement. The SAM however performs unsatisfactorily on domains that are distinct from its training data, which primarily comprise natural scene images, and it does not support automatic segmentation of specific semantics due to its interactive prompting mechanism. In our work, we introduce APSeg, a novel auto-prompt network for cross-domain few-shot semantic segmentation (CD-FSS), which is designed to be auto-prompted for guiding cross-domain segmentation. Specifically, we propose a Dual Prototype Anchor Transformation (DPAT) module that fuses pseudo query prototypes extracted based on cycle-consistency with support prototypes, allowing features to be transformed into a more stable domain-agnostic space. Additionally, a Meta Prompt (MPG) module is introduced to automatically generate prompt embeddings, eliminating the need for manual visual prompts. We build an efficient model which can be applied directly to target domains without fine-tuning. Extensive experiments on four cross-domain datasets show that our model outperforms the state-of-the-art CD-FSS method by 5.24% and 3.10% in average accuracy on 1-shot and 5-shot settings, respectively.

关键词： Training Metalearning Visualization Semantic segmentation Semantics Prototypes Training data

NeSLAM: Neural Implicit Mapping and Self-Supervised Feature Tracking With Depth Completion and Denoising

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arXiv 2024年

作者： Deng, Tianchen Wang, Yanbo Xie, Hongle Wang, Hesheng Wang, Jingchuan Wang, Danwei Chen, Weidong Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China School of Electrical and Electronic Engineering Nanyang Technological University Singapore

In recent years, there have been significant advancements in 3D reconstruction and dense RGB-D SLAM systems. One notable development is the application of Neural Radiance Fields (NeRF) in these systems, which utilizes implicit neural representation to encode 3D scenes. This extension of NeRF to SLAM has shown promising results. However, the depth images obtained from consumer-grade RGB-D sensors are often sparse and noisy, which poses significant challenges for 3D reconstruction and affects the accuracy of the representation of the scene geometry. Moreover, the original hierarchical feature grid with occupancy value is inaccurate for scene geometry representation. Furthermore, the existing methods select random pixels for camera tracking, which leads to inaccurate localization and is not robust in real-world indoor environments. To this end, we present NeSLAM, an advanced framework that achieves accurate and dense depth estimation, robust camera tracking, and realistic synthesis of novel views. First, a depth completion and denoising network is designed to provide dense geometry prior and guide the neural implicit representation optimization. Second, the occupancy scene representation is replaced with Signed Distance Field (SDF) hierarchical scene representation for high-quality reconstruction and view synthesis. Furthermore, we also propose a NeRF-based self-supervised feature tracking algorithm for robust real-time tracking. Experiments on various indoor datasets demonstrate the effectiveness and accuracy of the system in reconstruction, tracking quality, and novel view synthesis. © 2024, CC BY-NC-SA.

关键词： Geometry

Saliency-SLAM: saliency prediction for visual SLAM

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Saliency-SLAM: saliency prediction for visual SLAM

Tianjin University-IET Electrical & Information engineering Doctoral Forum (TJU IET 2023)

作者： S. Jin Q. Meng Institute of Robotics and Autonomous Systems Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information Engineering Tianjin University Tianjin People's Republic of China

This paper proposes a novel approach for visual simultaneous localization and mapping (vSLAM), called Saliency-SLAM. This method effectively emulates the visual attention mechanism by integrating a saliency prediction model. In contrast to conventional vSLAM methods, which treat all feature points as equally significant, salient feature points are given greater importance in Saliency-SLAM. Therefore, we construct a novel saliency dataset specifically for vSLAM task to train a saliency prediction model by integrating multi-level visual information. Then, we propose to use the salient value of predicted result as the weight of significance for the feature points during the optimization process. Exhaustive experiments have been conducted on the KITTI dataset, demonstrating that our proposed method outperforms existing approaches in terms of localization accuracy.

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Particle-based Instance-aware Semantic Occupancy Mapping in Dynamic Environments

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arXiv 2024年

作者： Chen, Gang Wang, Zhaoying Dong, Wei Alonso-Mora, Javier Autonomous Multi-Robots Lab Department of Cognitive Robotics School of Mechanical Engineering Delft University of Technology Delft2628 CD Netherlands State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiaotong University Shanghai200240 China

Representing the 3D environment with instance-aware semantic and geometric information is crucial for interaction-aware robots in dynamic environments. Nevertheless, creating such a representation poses challenges due to sensor noise, instance segmentation and tracking errors, and the objects’ dynamic motion. This paper introduces a novel particle-based instance-aware semantic occupancy map to tackle these challenges. Particles with an augmented instance state are used to estimate the Probability Hypothesis Density (PHD) of the objects and implicitly model the environment. Utilizing a State-augmented Sequential Monte Carlo PHD (S2MC-PHD) filter, these particles are updated to jointly estimate occupancy status, semantic, and instance IDs, mitigating noise. Additionally, a memory module is adopted to enhance the map’s responsiveness to previously observed objects. Experimental results on the Virtual KITTI 2 dataset demonstrate that the proposed approach surpasses state-of-the-art methods across multiple metrics under different noise conditions. Subsequent tests using real-world data further validate the effectiveness of the proposed approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Mapping

Cable Length Estimation for a Hyper-Redundant Robot Based on Cable Hole Clearance Calculation

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Cable Length Estimation for a Hyper-Redundant Robot Based on...

IEEE International Conference on Automation Science and engineering (CASE)

作者： Ziqing Li Jiangqin Deng Yang Zheng Chao Liu Guoying Gu Robotics Institute School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong University Shanghai China

For the cable-driven hyper-redundant robot, most of the current drive cable length estimation methods consider that the cable always passes through the center of the hole, and do not consider the effect of cable-hole clearance, resulting in a large motion error. In this paper, we propose a cable length estimating method by considering cable-hole clearance. Based on the simplified space model of the cable hole, it uses the recursive algorithm to obtain the approximate solution of the actual drive cable length. The algorithm can meet the requirements of real-time control while ensuring calculation accuracy. The correctness of the approximate solution is verified by using the interior point penalty function method. Taking a 24DoF hyper-redundant manipulator with joint angle closed-loop control as an example, the effectiveness of the proposed method is proved by comparing the theoretical calculation results with the actual drive cable length.

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Collaborative Fall Detection and Response using Wi-Fi Sensing and Mobile Companion Robot

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arXiv 2024年

作者： Chen, Yunwang Kang, Yaozhong Zhao, Ziqi Hong, Yue Meng, Lingxiao Meng, Max Q.-H. The Shenzhen Key Laboratory of Robotics Perception and Intelligence Southern University of Science and Technology Shenzhen518055 China The Department of Electrical and Electronic Engineering SUSTech Shenzhen China The School of System Design and Intelligent Manufacturing SUSTech Shenzhen China

This paper presents a collaborative fall detection and response system integrating Wi-Fi sensing with robotic assistance. The proposed system leverages channel state information (CSI) disruptions caused by movements to detect falls in non-line-of-sight (NLOS) scenarios, offering non-intrusive monitoring. Besides, a companion robot is utilized to provide assistance capabilities to navigate and respond to incidents autonomously, improving efficiency in providing assistance in various environments. The experimental results demonstrate the effectiveness of the proposed system in detecting falls and responding effectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Fall detection

An Energetic Approach to Task-Invariant Ankle Exoskeleton Control

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An Energetic Approach to Task-Invariant Ankle Exoskeleton Co...

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

作者： Katharine Walters Gray C. Thomas Jianping Lin Robert D. Gregg The Department of Robotics University of Michigan Ann Arbor MI USA The State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China

Robotic ankle exoskeletons have been shown to reduce human effort during walking. However, existing ankle exoskeleton control approaches are limited in their ability to apply biomimetic torque across diverse tasks outside of the controlled lab environment. Energy shaping control can provide task-invariant assistance without estimating the user's state, classifying task, or reproducing pre-defined torque trajectories. In previous work, we showed that an optimally task-invariant energy shaping controller implemented on a knee-ankle ex-oskeleton reduced the effort of certain muscles for a range of tasks. In this paper, we extend this approach to the sensor suite available at the ankle and present its implementation on a commercially-available, bilateral ankle exoskeleton. An experiment with three healthy subjects walking on a circuit and on a treadmill showed that the controller can approximate biomimetic profiles for varying terrains and task transitions without classifying tasks or switching control modes.

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GM-DF: Generalized Multi-Scenario Deepfake Detection

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arXiv 2024年

作者： Lai, Yingxin Yu, Zitong Yang, Jing Li, Bin Kang, Xiangui Shen, Linlin The School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen University Shenzhen518060 China The Guangdong Key Laboratory of Information Security The School of Computer Science and Engineering Sun Yat-sen University Guangzhou510080 China Computer Vision Institute School of Computer Science & Software Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China

Existing face forgery detection usually follows the paradigm of training models in a single domain, which leads to limited generalization capacity when unseen scenarios and unknown attacks occur. In this paper, we elaborately investigate the generalization capacity of deepfake detection models when jointly trained on multiple face forgery detection datasets. We first find a rapid degradation of detection accuracy when models are directly trained on combined datasets due to the discrepancy across collection scenarios and generation methods. To address the above issue, a Generalized Multi-Scenario Deepfake Detection framework (GM-DF) is proposed to serve multiple real-world scenarios by a unified model. First, we propose a hybrid expert modeling approach for domain-specific real/forgery feature extraction. Besides, as for the commonality representation, we use CLIP to extract the common features for better aligning visual and textual features across domains. Meanwhile, we introduce a masked image reconstruction mechanism to force models to capture rich forged details. Finally, we supervise the models via a domain-aware meta-learning strategy to further enhance their generalization capacities. Specifically, we design a novel domain alignment loss to strongly align the distributions of the meta-test domains and meta-train domains. Thus, the updated models are able to represent both specific and common real/forgery features across multiple datasets. In consideration of the lack of study of multi-dataset training, we establish a new benchmark leveraging multi-source data to fairly evaluate the models' generalization capacity on unseen scenarios. Both qualitative and quantitative experiments on five datasets conducted on traditional protocols as well as the proposed benchmark demonstrate the effectiveness of our approach. The codes will be available on https://***/laiyingxin2/GM-DF. Copyright © 2024, The Authors. All rights reserved.

关键词： Image reconstruction