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IEEE International Conference on robotics and Biomimetics

作者： Zheyuan Lin Min Huang Wen Wang Mengjie Qin Te Li Minhong Wan Jason Gu Shiqiang Zhu Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

ISBN: (纸本)9781665481106

For service robots, person re-identification (ReID) and multi-pedestrian tracking (MPT) are vital to get a person's location and link identities across frames. Though their accuracy keeps improving, most work lacks consideration of the application scenario, haunted by limited space, constrained power supply, and demands of the real-time response during human-robot interaction. Some ReID models learn trivial or unrelated features, inhibiting the downstream tasks. To solve these issues, the efficient and light-weighted Head-Shoulder Mask aided ResNet (HSMR) is proposed. This model applies multi-task learning to enhance the feature extraction performance in the training stage without extra computational load during inference. The auxiliary task fully uses head-shoulder information to guide the network and focuses on the head region, which contains the identity information. In experiments on the Tour-Guide Robot Data Base (TGRDB), HSMR earned results better than ResNet-18 on the ReID task and was superior to the recent two-stream method on the MPT task. On the mobile hardware, inference reaches an average of 15.2 FPS, three times faster than the two-stream method. The code is released at https://***/ZhYLin99/HSMR.

关键词： Training Service robots Computational modeling Robot vision systems Human-robot interaction Multitasking Real-time systems

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A Multi-Layer Software Architecture for Aerial Cognitive Multi-Robot systems in Power Line Inspection Tasks

A Multi-Layer Software Architecture for Aerial Cognitive Mul...

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2021 International Conference on Unmanned Aircraft systems, ICUAS 2021

作者： Silano, Giuseppe Bednar, Jan Nascimento, Tiago Capitan, Jesus Saska, Martin Ollero, Anibal Czech Technical University Faculty of Electrical Engineering Prague Czech Republic Universidade Federal da Paraiba Department of Computer Systems Brazil Grvc Robotics Laboratory University of Seville Spain

ISBN: (纸本)9780738131153

This paper presents a multi-layer software architecture to perform cooperative missions with a fleet of quad-rotors providing support in electrical power line inspection operations. The proposed software framework guarantees the compliance with safety requirements between drones and human workers while ensuring that the mission is carried out successfully. Besides, cognitive capabilities are integrated in the multi-vehicle system in order to reply to unforeseen events and external disturbances. The feasibility and effectiveness of the proposed architecture are demonstrated by means of realistic simulations. © 2021 IEEE.

关键词： Inspection

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Directly wireless communication of human minds via non-invasive brain-computer-metasurface platform

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eLight 2022年第1期2卷 132-142页

作者： Qian Ma Wei Gao Qiang Xiao Lingsong Ding Tianyi Gao Yajun Zhou Xinxin Gao Tao Yan Che Liu Ze Gu Xianghong Kong Qammer HAbbasi Lianlin Li Cheng‑Wei Qiu Yuanqing Li Tie Jun Cui Institute of Electromagnetic Space Southeast UniversityNanjing 210096China School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China State Key Laboratory of Millimeter Wave Southeast UniversityNanjing 210096China Center of Intelligent Metamaterials Pazhou Laboratory Guangzhou 510330China Research Center for Brain-Computer Interface Pazhou LabGuangzhou 510330China Department of Electrical and Computer Engineering National University of SingaporeSingaporeSingapore University of Glasgow James Watt School of EngineeringGlasgow G128QQUK State Key Laboratory of Advanced Optical Communication Systems and Networks Department of ElectronicsPeking UniversityBeijing 100871China

Brain-computer interfaces(BCIs),invasive or non-invasive,have projected unparalleled vision and promise for assisting patients in need to better their interaction with the *** by the BCI-based rehabilitation technologies for nerve-system impairments and amputation,we propose an electromagnetic brain-computer-metasurface(EBCM)paradigm,regulated by human’s cognition by brain signals directly and *** experimentally show that our EBCM platform can translate human’s mind from evoked potentials of P300-based electroencephalography to digital coding information in the electromagnetic domain non-invasively,which can be further processed and transported by an information metasurface in automated and wireless *** wireless communications of the human minds are performed between two EBCM operators with accurate text ***,several other proof-of-concept mind-control schemes are presented using the same EBCM platform,exhibiting flexibly-customized capabilities of information processing and synthesis like visual-beam scanning,wave modulations,and pattern encoding.

关键词： communication computer surface

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IoT for Sustainable Hospitality: A Systematic Review of Opportunities and Challenges for the Hospitality Industry Revolution

IoT for Sustainable Hospitality: A Systematic Review of Oppo...

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International Conference on Distributed Computing in Sensor systems (DCOSS)

作者： Marios S. Poullas Elena Kakoulli Department of Computer Science Intelligent Systems Laboratory Neapolis University Pafos Pafos Cyprus Cell & Developmental Biology University College London London UK Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

Technology has influenced the hospitality industry, and the Internet of Things (IoT) has become a focus for sustainability, revenue growth, and problem-solving. This systematic review examined the opportunities and challenges of implementing IoT technology for a sustainable hospitality. Through a thorough analysis of 28 studies, we found that IoT technology has great potential to address sustainability issues in the hospitality industry. However, there are still significant challenges that need to be addressed for the successful implementation of IoT technology in the hospitality industry. Overall, this study highlights the potential of IoT technology to revolutionize the hospitality industry and make it more sustainable. Further research is needed to address the challenges of IoT implementation in the hospitality industry and to evaluate the effectiveness of IoT technology in achieving sustainability goals.

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SubPipe: A Submarine Pipeline Inspection Dataset for Segmentation and Visual-inertial Localization

SubPipe: A Submarine Pipeline Inspection Dataset for Segment...

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OCEANS

作者： Olaya Álvarez-Tuñón Luiza Ribeiro Marnet Martin Aubard László Antal Maria Costa Yury Brodskiy Department of Electrical and Computer Engineering Artificial Intelligence in Robotics Laboratory (AiR-Lab) Aarhus University Aarhus C Denmark EIVA a/s Skanderborg Denmark OceanScan Marine Systems & Technology Matosinhos Portugal RWTH Aachen University Aachen Germany

ISBN: (数字)9798350362077

ISBN: (纸本)9798350362084

This paper presents SubPipe, an underwater dataset for SLAM, object detection, and image segmentation. SubPipe has been recorded using a lightweight autonomous underwater vehicle (LAUV), operated by OceanScan MST, and carrying a sensor suite including two cameras, a side-scan sonar, and an inertial navigation system, among other sensors. The AUV has been deployed in a pipeline inspection environment with a submarine pipe partially covered by sand. The AUV's pose ground truth is estimated from the navigation sensors. The side-scan sonar and RGB images include object detection and segmentation annotations, respectively. State-of-the-art segmentation, object detection, and SLAM methods are benchmarked on SubPipe to demonstrate the dataset's challenges and opportunities for leveraging computer vision algorithms. To the authors' knowledge, this is the first anno-tated underwater dataset providing a real pipeline inspection scenario. The dataset and experiments are publicly avail-able online at https://***/remaro-network/SubPipe-dataset.

关键词： Location awareness Image segmentation Simultaneous localization and mapping Oceans Pipelines Object detection Inertial navigation

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Design of Capsule Robot for Magnetic Gastric Biopsy and Drug Administration

Design of Capsule Robot for Magnetic Gastric Biopsy and Drug...

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IEEE International Conference on Mechatronics and Automation

作者： Zhihui Xu Zongze Li Shoubin Liu Chengzhi Hu Department of Mechanical and Energy Engineering Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Southern University of Science and Technology Shenzhen China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China School of Mechanical and Electrical Engineering Harbin Institute of Technology Shenzhen China

Capsule robots have emerged as a promising tool for the early diagnosis of gastrointestinal diseases. Unlike conventional gastroscopy, they offer the potential for minimally invasive or even non-invasive treatment, and can alleviate discomfort and pain in patients. However, current capsule robots have some limitations. For instance, they lack stomach tissue biopsy and drug administration functions, and the capsule robot used for biopsy does not consider the tolerance of gastrointestinal tissue. Similarly, the capsule robot used for drug administration does not account for the outlet speed of drug administration. This paper proposes a new biopsy method using a magnetically manipulated capsule robot and analyzes the principles governing the robot's manipulation during the biopsy and drug administration processes. Furthermore, an ANSYS gastric model was established and used to simulate and experimentally analyze damage to the gastric tissue model from different types of biopsy needles and their moving speeds. The appropriate type of biopsy needle and moving speed were determined to prevent damage to the gastric tissue. The study also used FLUENT to determine the appropriate thrust at the entrance of the drug cavity and the impact of the drug density on the drug outlet velocity to ensure complete adherence of the drug to the lesion.

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An effective Reinforcement Learning method for preventing the overfitting of Convolutional Neural Networks

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Advances in Computational Intelligence 2022年第5期2卷 1-15页

作者： Mahdavi-Hormat, Ali Menhaj, Mohammad Bagher Shakarami, Ashkan Department of Electrical Computer and Biomedical Engineering Islamic Azad University Qazvin Iran Department of Electrical Engineering Amirkabir University of Technology (Tehran Polytechnic) Tehran Iran Intelligent Autonomous Systems Laboratory (IAS-Lab) Department of Information Engineering University of Padova Padua Italy

Convolutional Neural Networks are machine learning models that have proven abilities in many variants of tasks. This powerful machine learning model sometimes suffers from overfitting. This paper proposes a method based on Reinforcement Learning for addressing this problem. In this research, the parameters of a target layer in the Convolutional Neural Network take as a state for the Agent of the Reinforcement Learning section. Then the Agent gives some actions as forming parameters of a hyperbolic secant function. This function’s form is changed gradually and implicitly by the proposed method. The inputs of the function are the weights of the layer, and its outputs multiply by the same weights to updating them. In this study, the proposed method is inspired by the Deep Deterministic Policy Gradient model because the actions of the Agent are into a continuous domain. To show the proposed method’s effectiveness, the classification task is considered using Convolutional Neural Networks. In this study, 7 datasets have been used for evaluating the model; MNIST, Extended MNIST, small-notMNIST, Fashion-MNIST, sign language MNIST, CIFAR-10, and CIFAR-100.

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Motion Planning Integrated with Vehicle-Terrain Interactions for Off-Road Autonomous Ground Vehicles 27

Motion Planning Integrated with Vehicle-Terrain Interactions...

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27th IEEE International Conference on intelligent Transportation systems, ITSC 2024

作者： Yin, Jianhua Zhan, Xianyuan Ji, Tianying Xu, Bingrong He, Yi Zhang, Daqing Li, Lingxi Wuhan University of Technology Intelligent Transportation Systems Research Center Wuhan430063 China Engineering Research Center of Transportation Information and Safety Ministry of Education Wuhan430063 China Chongqing Research Institute Wuhan University of Technology Chongqing China Institute for Ai Industry Research Tsinghua University Beijing100084 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China Tsinghua University Department of Computer Science and Technology Beijing100084 China School of Automation Wuhan University of Technology Wuhan430070 China College of Mechanical and Electrical Engineering Hunan Agricultural University Changsha410128 China Sunward Intelligent Equipment Co. Ltd. Changsha410100 China Elmore Family School of Electrical and Computer Engineering College of Engineering Purdue University IN46202 United States

ISBN: (纸本)9798331505929

Off-road autonomous ground vehicles (AGVs) work in unstructured environments with varied elevation and deformable ground, where vehicle-terrain interactions are more complex resulting in unpredictable dynamics behavior. Current sensor-based hazards/obstacle identification methods mainly focus on geometric hazards/obstacles with explicit geometry, which might be adequate for vehicles in on-road conditions. However, in the off-road environment, vehicle-terrain interactions could introduce non-geometric hazards that cannot be detected by current geometric hazards-oriented sensing techniques, e.g. vehicle stuck in mud. To enable AGVs to traverse the target terrain and meet the goal while avoiding non-geometric hazards, this work proposes a motion planning method integrated with coupled vehicle-terrain dynamics response. Specifically, terramechanics-based modeling and simulation (M&S) is utilized to characterize vehicle-terrain interactions. Based on the dynamics response, a reinforcement learning (RL) algorithm with a specialized reward function is used to navigate the AGV to find the goal with high mobility and safety. We demonstrate the proposed method with two case studies, 1) high mobility is the foremost concern with less safety requirement, and 2) both high mobility and safety are required. The results show that the proposed motion planning method is effective for navigating the AGV in off-road terrains. © 2024 IEEE.

关键词： Off road vehicles

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Multi-Vehicle Cooperative Persistent Coverage for Random Target Search

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IEEE robotics and Automation Letters 2025年第7期10卷 6680-6687页

作者： Li, Zhuo Li, Guangzheng Sadeghi, Alireza Sun, Jian Wang, Gang Wang, Jialin Beijing Institute of Technology National Key Lab of Autonomous Intelligent Unmanned Systems School of Automation Beijing100084 China Chongqing Innovation Center Beijing Institute of Technology Chongqing401120 China University of Minnesota Department of Electrical and Computer Engineering MinneapolisMN55455 United States Intelligent Game and Decision Laboratory China Academy of Launch Vehicle Technology Beijing100071 China

This letter investigates the target search problem for a network of autonomous vehicles, aiming to maximize the detection of randomly appearing targets within a given area. Considering no prior knowledge of the targets is available, we propose a multi-vehicle cooperative persistent coverage scheme under the framework of multi-agent reinforcement learning, in contrast to heuristic and model-based optimization methods in existing works. We model the persistent coverage problem as a partially observable Markov decision process (POMDP) due to the vehicles' limited observation ranges, and introduce a knowability map to characterize their knowledge of the target area. Each vehicle employs a distributed estimator, leveraging its own observations and shared information from neighboring vehicles, to construct a globally estimated knowability map - thereby mitigating partial observability. The persistent coverage policies are learned with the architecture of centralized training and distributed execution, enabling cooperative and efficient target search by fully exploiting shared information. Moreover, we propose an adaptive partition method for the target area to ensure a fixed dimension of the state space in the POMDP, which can improve scalability of the learned policy to target areas with various sizes. Simulations validate effectiveness and scalability of the proposed cooperative scheme. © 2025 IEEE.

关键词： Reinforcement learning

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QuerySOD: A Small Object Detection Algorithm Based on Sparse Convolutional Network and Query Mechanism

QuerySOD: A Small Object Detection Algorithm Based on Sparse...

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

作者： Zhengcai Cao Junnian Li Jie Niu MengChu Zhou State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China College of Information Science and Technology Beijing University of Chemical Technology Beijing China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Although remarkable advances have been achieved in generic object detection, small object detection (SOD) remains challenging owing to small objects’ information loss and noisy representation caused by their non-uniform distribution. Their limited width and height, scale variations, and redundant computation make SOD hard. To overcome them, this work proposes a new SOD method based on sparse convolutional network (SCNet) and Query Mechanism called QuerySOD. First, an extended feature pyramid network is constructed for extracting feature maps of small objects with more regional details. Then, a Sparse Head is neatly designed by using SCNet for accelerating the interfering speed and obtaining weights of each layer. After that, a Query Mechanism is innovatively introduced for harvesting the benefit of sparse value feature maps from the Sparse Head. QuerySOD is evaluated on public benchmarks including COCO and VisDrone. Finally, we apply it on ‘Jinghai’ unmanned survey vehicles and receive excellent SOD performance from this real-world application.

关键词： Surveys Accuracy Object detection Seaports Lakes Feature extraction Prediction algorithms Convolutional neural networks Security Noise measurement

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