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2024 China Automation Congress, CAC 2024

作者： Chen, Tingtian Wang, Qi Min, Huasong Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9798350368604

Achieving precise localization and high-quality static map construction in unfamiliar environments is fundamental for mobile robots. However, in environments like urban roads, dynamic objects such as pedestrians and vehicles can cause LiDAR SLAM odometry drift, leaving ghostings in the constructed map, which can lead to the failure of subsequent tasks such as path planning based on prior maps. Therefore, performing dynamic points filtering on point clouds is crucial for mobile robots, this paper introduces a novel Dynamic Non-Ground points Filtering LiDAR SLAM(DNGF-LS). Firstly, employing a Coarse-to-Fine Concentric Zone Model(C-F-CZM) to represent point clouds and perform ground segmentation addresses the sparsity issue caused by distant points in ground plane fitting, yielding accurate non-ground point clouds. Subsequently, converting the current frame’s previous ten frames of point clouds to 4D point clouds with the same viewpoint, utilizing sparse voxel grids to represent 4D point clouds, and inputting them into a sparse 4D Convolutional Neural Network(CNN) for dynamic points filtering. Finally, retaining static points for pose estimation and mapping using LiDAR Odometry And Mapping(LOAM). Experimental results on highly dynamic datasets demonstrate that DNGF-LS can construct high-quality static maps and achieve superior localization accuracy compared to LIO-SEGMOT and SUMA++. © 2024 IEEE.

关键词： Mapping

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YOLOv5-LF: An optimized YOLOv5 algorithm for real-time automatic tracking

YOLOv5-LF: An optimized YOLOv5 algorithm for real-time autom...

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2024 China Automation Congress, CAC 2024

作者： Luo, Jicheng Cheng, Lei Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9798350368604

Recent research has concentrated on enhancing robot visual tracking in dynamic settings. To boost real-time tracking capabilities, we introduce an optimized robot visual tracking control system that employs an advanced lightweight YOLOv5 algorithm. This system integrates the LCNet-a lightweight CPU-based Convolutional Neural Network-and a PConv-driven FasterNet module, both of which significantly enhance the detection of object features. Our experimental setup, which uses an industrial camera to acquire target pose data, demonstrates that after implementing the optimized model, the system efficiently detects and tracks moving objects in real-time from video streams. The gimbal’s positioning is dynamically adjusted in response to the moving target’s image displacement. The YOLOv5-LF network model, quantified with OpenVINO, has demonstrated a detection accuracy of 95.23% and a recognition rate of 90 frames per second (FPS) on Intel’s NUC. This not only ensures seamless real-time system management but also enhances the precision of object detection. The refined detection algorithm notably outperforms the original network in terms of visual tracking efficacy on our experimental platform. © 2024 IEEE.

关键词： Convolutional neural networks

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A Fruit Detection Model in Complex Environments Based on Attention Mechanism and Feature Fusion

A Fruit Detection Model in Complex Environments Based on Att...

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2024 China Automation Congress, CAC 2024

作者： Nan, Qian Huang, Wenhui Min, Huasong Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9798350368604

In complex orchard environments, harvesting robots face significant challenges in identifying fruits accurately due to fruits of various sizes, varying degrees of sunlight intensity, and occlusion. To resolve this issue, we propose a fruit detection model in this paper, which is called AMMFF-YOLO and based on the YOLOv8s architecture. Firstly, a small target detection layer is introduced to enhance the model’s ability to identify small-sized fruits. Secondly, a light-adaptive module is proposed, which increases spatial dimension information without reducing the channel dimension, enriching the model’s perception ability under different sunlight intensities. This module is named C2fCS, which stands for C2f module based on channel and spatial (CS) attention mechanisms. Furthermore, a context information enhancement module (CIEM) is proposed, which employs dilated convolutions and skip connections to fuse feature information from multiple scales, enlarges the receptive field, and improves the model’s ability to recognize target fruits and backgrounds under occlusion. Lastly, the Inner-MPDIoU Loss is used as a bounding box regression loss function to improve the model’s localization ability and detection accuracy. In the detection experiments of strawberries, apples, and citrus in complex orchard environments, the detection accuracy of the proposed model is higher than the detection accuracy of other state-of-art models 0.4% ∼ 8.8%. The experimental results validate the effectiveness of AMMFF-YOLO in fruit detection under complex orchard environments. © 2024 IEEE.

关键词： Orchards

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SESGMS: A Welding Seam Extraction Method Based on Spherical Gaussian Mixture Sampling

SESGMS: A Welding Seam Extraction Method Based on Spherical ...

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2024 China Automation Congress, CAC 2024

作者： Wu, Chuxiong Zhou, Haotian Min, Huasong Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9798350368604

Accurate seam extraction is crucial for optimizing welding robot performance. Current 3D weld detectors often struggle with geometric approximation challenges and fail to adapt effectively across different weld types, leading to missed or redundant detections that require meticulous threshold adjustments. This paper proposes a weld seam detection method for accurate and robust extraction of weld seam points. Firstly, a neighborhood-weighted spherical projection algorithm is proposed to enhance local feature distinctions within point clouds, improving recognition accuracy of weld seam points. Secondly, a novel Local Reference Frame (LRF) algorithm based on spherical projection is designed to ensure rotational isotropy and facilitate feature comparability across diverse point cloud orientations. In LFR, rotational Spherical Gaussian (SG) closed-form is utilized for hybrid sampling, aiming to achieve efficient, stable, and isotropic 2D transformations of point cloud features. Finally, angle-independent feature vectors are generated by kernel density estimation with a nearest neighbor algorithm to assess symmetry, improving the extraction accuracy of weld. Experimental results on three types of workpieces and seven angular weld models demonstrate that our method excels in adaptive weld seam extraction, particularly in accurately identifying weld seam points. © 2024 IEEE.

关键词： Spheres

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Deep Reinforcement Learning-based Multi-Objective Scheduling for Distributed Heterogeneous Hybrid Flow Shops with Blocking Constraints

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Engineering 2025年第3期46卷 278-291页

作者： Xueyan Sun Weiming Shen Jiaxin Fan Birgit Vogel-Heuser Fandi Bi Chunjiang Zhang State Key Laboratory of Intelligent Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China Institute of Automation and Information Systems Technical University of MunichGarching 85748Germany

This paper investigates a distributed heterogeneous hybrid blocking flow-shop scheduling problem(DHHBFSP)designed to minimize the total tardiness and total energy consumption simultaneously,and proposes an improved proximal policy optimization(IPPO)method to make real-time decisions for the DHHBFSP.A multi-objective Markov decision process is modeled for the DHHBFSP,where the reward function is represented by a vector with dynamic weights instead of the common objectiverelated scalar value.A factory agent(FA)is formulated for each factory to select unscheduled jobs and is trained by the proposed IPPO to improve the decision *** FAs work asynchronously to allocate jobs that arrive randomly at the shop.A two-stage training strategy is introduced in the IPPO,which learns from both single-and dual-policy data for better data *** proposed IPPO is tested on randomly generated instances and compared with variants of the basic proximal policy optimization(PPO),dispatch rules,multi-objective metaheuristics,and multi-agent reinforcement learning *** experimental results suggest that the proposed strategies offer significant improvements to the basic PPO,and the proposed IPPO outperforms the state-of-the-art scheduling methods in both convergence and solution quality.

关键词： Multi-objective Markov decision process Multi-agent deep reinforcement learning Proximal policy optimization Distributed hybrid flow-shop scheduling Blocking constraints

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Attitude Tracking Control for Rigid Spacecraft with Arbitrary Convergence Time

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Journal of systems Science & Complexity 2024年第6期37卷 2579-2594页

作者： XU Yu-Tian WU Ai-Guo Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Harbin Institute of Technology(Shenzhen)Shenzhen 518055China Key University Laboratory of Mechanism&Machine Theory and Intelligent Unmanned Systems of Guangdong Harbin Institute of Technology(Shenzhen)Shenzhen 518055China Department of Automation Harbin Institute of Technology(Shenzhen)Shenzhen 518055China HIT Wuhu Robot Technology Research Institute Wuhu 241060China

In this paper,attitude tracking control with arbitrary convergence time for rigid spacecraft is ***,a novel time-varying sliding function is proposed to achieve free-will arbitrary time convergence when the system states reside on the sliding *** such a sliding function,an attitude tracking controller is designed to guarantee that the states of the closed-loop system converge to the sliding surface within a predetermined time in the presence of external *** free-will arbitrary time convergences of the closed-loop system and sliding function are illustrated by numerical simulations.

关键词： Attitude tracking free-will arbitrary time stability rigid spacecraft sliding mode control

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Modeling and precise tracking control of spatial bending pneumatic softactuators

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Biomimetic Intelligence & robotics 2024年第4期4卷 92-98页

作者： Yize Ma Qingxiang Wu Zehao Qiu Yongchun Fang Ning Sun Institute of Robotics and Automatic Information Systems(IRAIS) College of Artificial IntelligenceNankai UniversityTianjin 300350China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai UniversityShenzhen 518083China

In recent years,a variety of pneumatic soft actuators(PSAs)have been proposed due to the develop-ment of soft robots in biomimetic robots,medical devices,*** the same time,the modeling and control of PSAs remains an open *** this paper,a spatial bending pneumatic soft actuator(SBPSA)modeling method based on the Prandtl-Ishlinskii(PI)model is proposed,and the inverse model is designed to compensate for hysteresis ***,an adaptive feedback controller combined with a hysteresis compensator is proposed for the precise control and tracking of ***,an experimental platform is built,and experimental results demonstrate the effectiveness of the proposed method for precise tracking.

关键词： Spatial bending Hysteresis compensation Prandtl-Ishlinskii modeling Adaptive feedback control

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Recent Developments in Metallic Degradable Micromotors for Biomedical and Environmental Remediation Applications

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Nano-Micro Letters 2024年第3期16卷 1-35页

作者： Sourav Dutta Seungmin Noh Roger Sanchis Gual Xiangzhong Chen Salvador Pané Bradley J.Nelson Hongsoo Choi Department of Robotics and Mechatronics Engineering Daegu Gyeongbuk Institute of Science and Technology(DGIST)Daegu 42988Republic of Korea DGIST‑ETH Microrobotics Research Center DGISTDaegu 42988Republic of Korea Multi‑Scale Robotics Lab Institute of Robotics and Intelligent SystemsETH Zurich8092 ZurichSwitzerland Institute of Optoelectronics State Key Laboratory of Photovoltaic Science and TechnologyShanghai Frontiers Science Research Base of Intelligent Optoelectronics and PerceptionFudan UniversityShanghai 200433People’s Republic of China

Synthetic micromotor has gained substantial attention in biomedicine and environmental ***-based degradable micromotor composed of magnesium(Mg),zinc(Zn),and iron(Fe)have promise due to their nontoxic fuel-free propulsion,favorable biocompatibility,and safe excretion of degradation products Recent advances in degradable metallic micromotor have shown their fast movement in complex biological media,efficient cargo delivery and favorable biocompatibility.A noteworthy number of degradable metal-based micromotors employ bubble propulsion,utilizing water as fuel to generate hydrogen *** novel feature has projected degradable metallic micromotors for active in vivo drug delivery *** addition,understanding the degradation mechanism of these micromotors is also a key parameter for their design and *** propulsion efficiency and life span govern the overall performance of a degradable metallic *** we review the design and recent advancements of metallic degradable ***,we describe the controlled degradation,efficient in vivo drug delivery,and built-in acid neutralization capabilities of degradable micromotors with versatile biomedical ***,we discuss micromotors’efficacy in detecting and destroying environmental ***,we address the limitations and future research directions of degradable metallic micromotors.

关键词： Magnesium Zinc Iron Biodegradable microrobot Biomedical Environmental

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CCA-MTFCN: A Robotic Pushing-Grasping Collaborative Method Based on Deep Reinforcement Learning 8th

CCA-MTFCN: A Robotic Pushing-Grasping Collaborative Method B...

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8th International Conference on Cognitive systems and information Processing, ICCSIP 2023

作者： Xu, Haiyuan Wang, Qi Min, Huasong Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan430081 China

ISBN: (纸本)9789819980178

Robotic grasping in dense clutter is often infeasible because of the occlusion and stacking of objects. Directly grasping the stacked objects may cause collisions and result in low efficiency and high failure rates. In practice, the lateral robotic push can separate stacked objects to create collision-free grasp affordances. Inspired by this, we devise a method called CCA-MTFCN based on deep reinforcement learning, which can learn the synergies between pushing and grasping policies to complete the task of removing all objects in a heavily cluttered environment. Specifically, a hard parameter-sharing Multi-Task Fully Convolutional Network (MTFCN) is proposed to model the action-value function, then multi-scale feature fusion mechanism is implemented in it which can enhance visual perception capability in cluttered environments. Moreover, a new reward function based on connected component analysis (CCA) is designed to effectively evaluate the quality of push actions in pushing-and-grasping collaboration. This enables us to explicitly encourage pushing actions that aid grasping thus improving the efficiency of sequential decision-making. Our approach was trained in simulation through trial-and-error, and evaluation experiments for object removal tasks in dense clutter demonstrate that our proposed method outperforms several baseline approaches in terms of task completion rate, grasp success rate, and action efficiency, which also has the capability to generalize to new scenarios. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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Learning Scalable Task Assignment with Imperative-Priori Conflict Resolution in Multi-UAV Adversarial Swarm Defense Problem

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Journal of systems Science & Complexity 2024年第1期37卷 369-388页

作者： ZHAO Zhixin CHEN Jie XIN Bin LI Li JIAO Keming ZHENG Yifan School of Electronics and Information Engineering Tongji UniversityShanghai 201804China Shanghai Institute of Intelligent Science and Technology Tongji UniversityShanghai 201804China School of Automation Beijing Institute of TechnologyBeijing 100081China National Key Laboratory of Autonomous Intelligent Unmanned Systems Beijing 100081China Shanghai Institute of Intelligent Science and Technology Tongji UniversityShanghai 201804China.

The multi-UAV adversary swarm defense(MUASD)problem is to defend a static base against an adversary UAV swarm by a defensive UAV *** the problem into task assignment and low-level interception strategies is a widely used ***-based approaches for task assignment are a promising *** studies on learning-based methods generally assume decentralized decision-making architecture,which is not beneficial for conflict *** contrast,centralized decision-making architecture is beneficial for conflict resolution while it is often detrimental to *** achieve scalability and conflict resolution simultaneously,inspired by a self-attention-based task assignment method for sensor target coverage problem,a scalable centralized assignment method based on self-attention mechanism together with a defender-attacker pairwise observation preprocessing(DAP-SelfAtt)is ***,an imperative-priori conflict resolution(IPCR)mechanism is proposed to achieve conflict-free ***,the IPCR mechanism is parallelized to enable efficient *** validate the algorithm,a variant of proximal policy optimization algorithm(PPO)is employed for training in scenarios of various *** experimental results show that the proposed algorithm not only achieves conflict-free task assignment but also maintains scalability,and significantly improve the success rate of defense.

关键词： Conflict resolution reinforcement learning scalability task assignment.

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