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14th ieee International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Liao, Junting Zhao, Chunjian Xie, Hui School of Computer and Artificial Intelligence Xiangnan University Hunan Chenzhou423000 China China Telecom Hunan Chenzhou Branch Hunan Chenzhou423000 China

ISBN: (纸本)9798331531935

With the rapid development of robotics and automation, online trajectory planning faces problems such as poor adaptability to dynamic environments and low computational efficiency. This study aims to propose a collaborative optimization algorithm that combines deep learning and compliant control to improve the efficiency and accuracy of online trajectory planning. First, CNN is used for feature extraction and a prediction model is established. Then a compliant control strategy is designed to deal with dynamic obstacles to ensure the flexibility and stability of the robot in trajectory planning. The trained CNN model is then used to generate real-time trajectory predictions. The robot motion is adjusted through a fuzzy logic controller to optimize the relative position of the trajectory and obstacles. The average obstacle avoidance success rate of the algorithm in dynamic environments reaches 97%. The collaborative optimization algorithm of deep learning and compliant control effectively improves the real-time and accuracy of online trajectory planning, providing a new solution for the application of intelligent robots in complex environments. © 2025 ieee.

关键词： Intelligent robots

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Dynamic bimanual human-to-robot object handovers using motion prediction deep neural networks

Dynamic bimanual human-to-robot object handovers using motio...

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2025 ieee/SICE International symposium on System Integration, SII 2025

作者： Mavsar, Matija Uchibe, Eiji Morimoto, Jun Ude, Ales Jožef Stefan Institute Dept. of Automatics Biocybernetics and Robotics Ljubljana Slovenia ATR Computational Neuroscience Laboratories Dept. of Brain-Robot Interface Kyoto Japan Kyoto University Graduate School of Informatics Kyoto Japan

ISBN: (纸本)9798331531614

Facilitating dynamic bimanual handovers between humans and robots is a complex endeavor that requires integrating human pose estimation, motion prediction, and generating appropriate trajectories for receiving robots. This study introduces a method designed to predict required bimanual receiver robot motion during handover tasks, leveraging pose estimation and motion prediction networks. Additionally, we propose a real-time control approach for a dual-arm humanoid robot to dynamically adjust its receiving trajectories. We evaluate the ability of neural networks to accurately predict receiver trajectories and thus improve the handover process. We compare long short-term memory (LSTM) and transformer architectures for motion prediction and also assess prediction accuracy of both absolute and relative receiving trajectories as well trajectories for each separate robot arm, and show that the use of absolute and relative coordinates is beneficial for generating more accurate receiver motions. © 2025 ieee.

关键词： Trajectories

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Offline and Online Energy Simulation using Virtual Commissioning Models with Extended Dynamical Behavior

Offline and Online Energy Simulation using Virtual Commissio...

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2025 ieee/SICE International symposium on System Integration, SII 2025

作者： Deubert, Darius Pfeifer, Denis Scheifele, Christian Fehr, Jorg Verl, Alexander Bosch Rexroth AG Lohr am Main97816 Germany Institute for Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Stuttgart70174 Germany ISG Industrielle Steuerungstechnik GmbH Stuttgart70563 Germany Institute of Engineering and Computational Mechanics University of Stuttgart Stuttgart70174 Germany

ISBN: (纸本)9798331531614

Considering energy consumption and dynamical behavior within the simulation of manufacturing systems emerges as an important objective for their development, commissioning, and operation. Currently, these aspects are often neglected in many fields including virtual commissioning. This work presents an approach to extend highly detailed virtual commissioning models of components from industrial automation by dynamics models for the calculation of energy consumption in an offline simulation and for reuse in an online simulation. To validate the presented approach, an exemplary offline virtual commissioning simulation of a pick-and-place robot application is implemented and subsequently transferred to an online simulation. The validation shows a good accuracy of the simulated energy consumption, enabling virtual commissioning engineers to analyze and optimize energy consumption already in the design phase of manufacturing systems. Additionally, the dynamical behavior was well reflected in the online simulation, contributing to the capability of simulation in the operation phase. © 2025 ieee.

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Mobile Robot Navigation Using ORB-SLAM3: A Multi-Modal Autonomous System

Mobile Robot Navigation Using ORB-SLAM3: A Multi-Modal Auton...

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2025 International Conference on Electrical automation and Artificial intelligence, ICEAAI 2025

作者： Da Conceicao, Ariano Tian, Lianfang Du, Qiliang Yuan, Ling School of Automation Science and Engineering South China University of Technology Guangzhou510641 China Research Institute of Modern Industrial Innovation South China University of Technology Guangzhou China Guangdong Engineering Research Center of Cloud-Edge-End Collaboration Technology for Smart City Guangzhou China Key Laboratory of Autonomous Systems and Network Control of Ministry of Education Guangzhou China Guangzhou China Sino-Singapore International Joint Research Institute Guangzhou511363 China

ISBN: (纸本)9798331506797

Indoor mobile robots require reliable solutions for mapping, localization, and navigation tasks. This paper presents a mobile robot system that implements the ability to realize mapping using visual SLAM with ORB-SLAM3 and LiDAR-based mapping with Gmapping for indoor perception and navigation through a modular ROS architecture. The framework also implements autonomous navigation and an autonomous Rapidly-exploring Random Trees (RRT) exploration module with adaptive reset mechanisms. The system operates on a ROS-based platform equipped with an Intel RealSense D435i RGB-D camera and LDS-02 LiDAR scanner. Experimental validation in simulated indoor environments demonstrates the system's capabilities and the presented architecture achieves computational efficiency through selective utilization of mapping modules while maintaining mapping accuracy through complementary sensor modalities. © 2025 ieee.

关键词： LiDAR SLAM Mapping Navigation ORBSLAM 3 ROS RRT

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MINER-RRT*: A Hierarchical and Fast Trajectory Planning Framework in 3D Cluttered Environments

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ieee TRANSACTIONS ON automation SCIENCE AND ENGINEERING 2025年 22卷 10973-10985页

作者： Wang, Pengyu Tang, Jiawei Lin, Hin Wang Zhang, Fan Wang, Chaoqun Wang, Jiankun Shi, Ling Meng, Max Q. -H. Southern Univ Sci & Technol Shenzhen Key Lab Robot Percept & Intelligence Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen 518055 Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Shandong Univ Sch Control Sci & Engn Jinan 250100 Shandong Peoples R China Southern Univ Sci & Technol Jiaxing Res Inst Jiaxing 314001 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2R3 Canada Chinese Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

Trajectory planning for quadrotors in cluttered environments has been challenging in recent years. While many trajectory planning frameworks have been successful, there still exists potential for improvements, particularly in enhancing the speed of generating efficient trajectories. In this paper, we present a novel hierarchical trajectory planning framework to reduce computational time and memory usage called MINER-RRT*, which consists of two main components. First, we propose a sampling-based path planning method boosted by neural networks, where the predicted heuristic region accelerates the convergence of rapidly-exploring random trees. Second, we utilize the optimal conditions derived from the quadrotor's differential flatness properties to construct polynomial trajectories that minimize control effort in multiple stages. Extensive simulation and real-world experimental results demonstrate that, compared to several state-of-the-art (SOTA) approaches, our method can generate high-quality trajectories with better performance in 3D cluttered environments (https://***/fXuuMRX19q0). Note to Practitioners-The motivation is the problem of planning trajectories for quadrotor autonomous flight in 3D cluttered and complex scenarios such as wild forest exploration and subterranean environment search-and-rescue. Sampling-based path planning methods are suitable for dealing with the complexity of the physical environment but are not convenient for computing dynamics and their differentials. Optimization-based trajectory generation methods are appropriate for handling various high-order constraints but rely on high-quality initial path solutions. Therefore, this paper combines the advantages of the two methods to propose a novel trajectory planning framework that can generate high-quality trajectories for quadrotors faster than many previous algorithms. We conduct numerous simulations and real-world experiments to verify that our method can be effectively deployed in re

关键词： Trajectory Trajectory planning Planning Quadrotors Three-dimensional displays Polynomials Trajectory optimization Splines (mathematics) Resource management Heuristic algorithms Robot trajectory planning sampling-based algorithm deep neural network

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Algebraic Reproduction of Triage Decision Making on Simple Heuristics of Boolean Multivalued Logic

Algebraic Reproduction of Triage Decision Making on Simple H...

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computational intelligence in Health and Medicine (CIHM), ieee symposium on

作者： Taira Watanabe Taishi Nishizaka Kujira Suzuki Atsushi Nishikawa Hitoshi Katai Hisashi Suzuki Graduate School of Engineering Science Osaka University Osaka Japan Faculty of Science and Engineering Chuo University Tokyo Japan Cancer Center Tachikawa Hospital Tokyo Japan

ISBN: (数字)9798331508333

ISBN: (纸本)9798331508340

In this research, we consider a simplified disaster situation for a thought experiment where priorities must be assigned when treating victims. We model egalitarian decision making, utilitarian decision making, and triage decision making (which combines both egalitarian and utilitarian decision makings) using a quantum computing-like multivalued logic that forms a Boolean algebra in contrast to popular multivalued logics that lack algebraic consistency. As a result, this article reports that some simple heuristic rules can quantitatively and algebraically reproduce and control human decisions regarding the prioritization of treatment for victims that reflects bioethical considerations.

关键词： Ethics Quantum computing Disasters computational modeling Decision making Multivalued logic Boolean algebra computational intelligence Dementia

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A Fully Sparse Detector with Cascade Heads for LiDAR-based 3D Object Detection 13

A Fully Sparse Detector with Cascade Heads for LiDAR-based 3...

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13th International Conference on Intelligent control and Information Processing, ICICIP 2025

作者： Chen, Junnan Zhang, Gang Hu, Xiaolin Zeng, Zhigang School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Department of Computer Science and Technology Institute for AI BNRist THU-Bosch JCML Center Tsinghua Universigy Beijing China

ISBN: (纸本)9798331516147

LiDAR-based 3D object detection is a critical component of perception systems in autonomous driving. Existing detectors typically rely on dense feature maps for 3D object prediction. However, as the perception range expands, the computational overhead associated with dense feature maps increases quadratically, creating significant challenges for scaling to long-range detection scenarios. To address this issue, fully sparse detectors have recently been proposed, but they often suffer from suboptimal performance or excessively complex architectures. In this paper, we present CHNet, a fully sparse detector designed to overcome these limitations by introducing a cascade head module, which consists of two sequential heads. By generating coarse object predictions, creating new feature points based on centers of the predicted objects, and aggregating features of the entire object to these new center points, CHNet enhances the qualities of object feature representations, achieving higher detection performance. Notably, CHNet effectively addresses the center feature missing problem in LiDAR-based 3D object detection by adding at most a hundred new feature points, offering a potential avenue for future research. On the widely used Waymo Open and Argoverse2 datasets, CHNet achieves impressive results without any additional embellishments. © 2025 ieee.

关键词： Object recognition

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Evolutionary-Based Quantum Classification for Surface Electromyography Signals

Evolutionary-Based Quantum Classification for Surface Electr...

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Multidisciplinary computational intelligence Incubators (MCII Companion), ieee symposium for

作者： Giovanni Acampora Angela Chiatto Autilia Vitiello Department of Physics “Ettore Pancini” University of Naples Federico II Naples Italy

ISBN: (数字)9798331519667

ISBN: (纸本)9798331519674

The classification of surface electromyography (sEMG) signals represents a key objective in the domain of rehabilitation and prosthetic control. This task is particularly challenging due to several factors, including the high dimensionality of the data and the high computational cost of the currently used deep learning models. In this context, quantum computing methods may prove advantageous due to their capacity to process a substantial amount of data facilitated by quantum phenomena such as superposition. In light of these considerations, the objective of this study is to demonstrate the feasibility of quantum computing by performing a comparison between evolutionarybased quantum classifiers for sEMG signals. In particular, the considered quantum classifiers are based on variational quantum circuits trained using evolutionary algorithms such as genetic algorithms, differential evolution and particle swarm optimization. As demonstrated in the experimental session utilizing a wellknown hand gesture dataset, quantum classifiers, and especially those based on genetic algorithms, are effective for sEMG signal classification.

关键词： Hands Quantum computing Quantum algorithm computational modeling Pattern classification Electromyography Quantum circuit Particle swarm optimization Genetic algorithms Prosthetics

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Proceedings of the 1997 ieee International symposium on computational intelligence in Robotics and automation, CIRA

Proceedings of the 1997 IEEE International Symposium on Comp...

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Proceedings of the 1997 ieee International symposium on computational intelligence in Robotics and automation, CIRA

The proceedings contains 59 papers from the 1997 ieee International symposium on computational intelligence in Robotics and automation. Topics discussed include: robot learning;motion control;navigational map construction and localization;sensor-based manipulation;intelligent control;path planning;learning and control for locomotion;sonar;Q-learning;neural networks;sequential decision-making;sensor data fusion;behavioral synthesis;evolutionary computation;robot dynamics;robot vision;robot skills;and vision-based tracking.

关键词： Robotics

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MamUNet: Mamba Enhances Vessel Segmentation in NIR-II Fluorescence Imaging

MamUNet: Mamba Enhances Vessel Segmentation in NIR-II Fluore...

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ieee International symposium on Biomedical Imaging

作者： Keyi Han Anqi Xiao Jie Tian Zhenhua Hu CAS Key Laboratory of Molecular Imaging Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

The Transformer architecture achieves excellent global context understanding of sequential data through the self-attention mechanism, but it faces computational challenges when modeling long-range dependencies. Mamba, based on the state space model (SSM), has emerged as a notable approach for effectively addressing these long-range dependencies, significantly improving computational and storage efficiency. Therefore, we propose MamUNet, a new method that combines the powerful modeling capabilities of the Mamba model with the strengths of the UNet architecture for complex vessel segmentation tasks in NIR-II fluorescence images. The encoder employs the classic network ResNet to learn local low-level features, followed by Mamba for global feature learning to capture more contextual information. We conducted experiments on a dataset of human brain NIR-II fluorescence vessel images and three publicly available retinal vessel datasets. The results demonstrate that our method achieves more accurate vessel segmentation with lower computational cost, outperforming other comparison methods.

关键词： Representation learning Image segmentation Accuracy computational modeling Computer architecture Fluorescence Transformers Retinal vessels computational efficiency Faces

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