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IEEE-RAS International Conference on Humanoid Robots

作者： Petr Svarny Mazin Hamad Alexander Kurdas Matej Hoffmann Saeed Abdolshah Sami Haddadin Department of Cybernetics Faculty of Electrical Engineering Czech Technical University in Prague Department of Computer Engineering Chair of Robotics and Systems Intelligence Munich Institute of Robotics and Machine Intelligence School of Computation Information and Technology Technische Universität München (TUM)

ISBN: (纸本)9798350309805

Various approaches can ascertain the safety and efficiency of physical human-robot collaboration. This paper presents the concept of robot functional mode switching to efficiently ensure human safety during collaborative tasks based on biomechanical pain and injury data and task information. Besides the robot's reflected inertial properties summarizing its impact dynamics, our concept also integrates safe and smooth velocity shaping that respects human partner motion, interaction type, and task knowledge. We further discuss different approaches to safely shape the robot velocity without sacrificing the overall task execution time and motion smoothness. The experimental results showed that our proposed approaches could decrease jerk level during functional mode switching and limit the impact of safety measures on productivity, especially when guided with additional task knowledge.

关键词： Robot motion Service robots Human-robot interaction Humanoid robots Switches Interference Safety

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Biomechanical models in the lower-limb exoskeletons development: A review

TechRxiv

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TechRxiv 2023年

作者： Firouzi, Vahid Seyfarth, Andre Song, Seungmoon Von Stryk, Oskar Sharbafi, Maziar Ahmad Lauflabor Locomotion Laboratory Institute of Sport Science TU Darmstadt Germany Simulation Systems Optimization and Robotics Group Department of Computer Science TU Darmstadt Germany Mechanical and Industrial Engineering Northeastern University United States

Lower limb exoskeletons serve multiple purposes, like supporting and augmenting movement. Biomechanical models are practical tools to understand human movement, and motor control. This review explores the applications of biomechanical gait models in the development of lower-limb exoskeletons. Biomechanical models can be broadly categorized as conceptual and detailed models. Biomechanical models can be used for the design, control, and assessment of exoskeletons. Also, these models can be used to estimate unmeasurable or hard-to-measure variables, which is also useful within the aforementioned applications. The analysis of applications and research areas aims to inspire further advancements in this field and contribute to the development of more effective exoskeletons. We highlight validating simulation studies, improving the accuracy and fidelity of biomechanical models, and benefiting from exoskeletons to enhance or validate biomechanical models as future research areas related to biomechanical models. We also emphasize the exploration of model-based design and control approaches for exoskeletons targeting pathological gait, and utilizing biomechanical models for diverse design objectives of exoskeletons. Also, increasing the availability of open-source resources accelerates exoskeleton and biomechanical model advancement. Our review reveals existing needs and cracks new perspectives for the development of more usable and human-compatible exoskeletons based on biomechanical models. © 2023, CC BY.

关键词： Exoskeleton (robotics)

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Robotic Single-Micropipette Enucleation Method for Zona-free Oocytes

Robotic Single-Micropipette Enucleation Method for Zona-free...

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Chinese Control Conference (CCC)

作者： Yidi Zhang Yaowei Liu Minghui Li Dong Sun Xin Zhao Institute of Robotics and Automatic Information System (IRAIS) and the Tianjin Key Laboratory of Intelligent Robotic (tjKLIR) Nankai University Tianjin China Department of Biomedical Engineering City University of Hong Kong Hong Kong SAR China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen China Centre for Robotics and Automation Shenzhen Research Institute City University of Hong Kong Shenzhen China

Somatic cell nuclear transfer is a mammalian cloning method that involves replacing the oocyte nucleus with a somatic cell nucleus. One of the most critical steps in this process is the efficient production of large numbers of enucleated oocytes. While a single-micropipette-based manual enucleation method has been widely used in manual cloning, it is still a manual operation that requires exceptional technical skills and experience. Additionally, it is difficult to accurately control the amount of enucleation and to meet the demand for high-throughput enucleation. Therefore, we propose a robotic enucleation method using a single micropipette for zona-free oocytes, which allows for the detection and control of the amount of enucleation, as well as continuous enucleation on a large scale. A single oocyte enucleation can be completed in less than 13 seconds. This robotic enucleation method provides a solution for enhancing the operational efficiency of robotic somatic cell nuclear transfer.

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IoT Cameras Network Based Motion Tracking for an Unmanned Aerial Vehicle Control Interface

IoT Cameras Network Based Motion Tracking for an Unmanned Ae...

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Information, Control, and Communication Technologies (ICCT), International Conference on

作者： Alexander Dubelschikov Tatyana Tsoy Hongbing Li Evgeni Magid Tikhonov Moscow Institute of Electronics and Mathematics HSE University Moscow Russia Laboratory of Intelligent Robotics Systems (LIRS) Institute of Information Technologies and Intelligent Systems Kazan Federal University Kazan Russian Federation Department of Instrument Science and Engineering Shanghai Jiao Tong University Shanghai China HSE University Moscow Russia

This work develops a concept of using stationary IoT cameras with motion tracking and notification functions employed for a teleoperated unmanned aerial vehicles (UAV) control. The notification function uses a popular messenger to improve an operator convenience. This approach allows the operator using a mobile phone as a UAV control panel by notifying on a presence of motion within a currently invisible area. This work extends our previous research on using stationary IoT cameras for a UAV teleoperation

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An efficient Deep Spatio-Temporal Context Aware decision Network (DST-CAN) for Predictive Manoeuvre Planning

arXiv

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arXiv 2022年

作者： Chowdhury, Jayabrata Sundaram, Suresh Rao, Nishanth Sundararajan, Narasimhan Robert Bosch Centre for Cyber-Physical Systems Artificial Intelligence and Robotics Lab Dept. of Aerospace Engineering Indian Institute of Science Bangalore India The Artificial Intelligence and Robotics Lab Dept. of Aerospace Engineering Indian Institute of Science Bangalore India WIRIN Project Dept. of Aerospace Engineering Indian Institute of Science Bangalore India

The safety and efficiency of an Autonomous Vehicle (AV) manoeuvre planning heavily depend on the future trajectories of surrounding vehicles. If an AV can predict its surrounding vehicles’ future trajectories, it can make safe and efficient manoeuvre decisions. In this paper, we present a Deep Spatio-Temporal Context-Aware decision Network (DST-CAN) for predictive manoeuvre decisions for AVs on highways. DST-CAN has two main components, namely spatio-temporal context-aware map generator and predictive manoeuvre decisions engine. DST-CAN employ a memory neuron network to predict the future trajectories of its surrounding vehicles. Using look-ahead prediction and past actual trajectories, a spatio-temporal context-aware probability occupancy map is generated. These context-aware maps as input to a decision engine generate a safe and efficient manoeuvre decision. Here, CNN helps extract feature space, and two fully connected network generates longitudinal and lateral manoeuvre decisions. Performance evaluation of DST-CAN has been carried out using two publicly available NGSIM US-101 and I-80 highway datasets. A traffic rule is defined to generate ground truths for these datasets in addition to human decisions. Two DST-CAN models are trained using imitation learning with human driving decisions from actual traffic data and rule-based ground truth decisions. The performances of the DST-CAN models are compared with the state-of-the-art Convolutional Social-LSTM (CS-LSTM) models for manoeuvre prediction. The results clearly indicate that the context-aware maps help DST-CAN to predict the decision accurately over CS-LSTM. Further, an ablation study has been carried out to understand the effect of prediction horizons of performance and a robustness study to understand the near collision scenarios over actual traffic observations. The context-aware map with a 3 second prediction horizon is most suitable and robust against near collision. © 2022, CC BY.

关键词： Autonomous vehicles

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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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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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CPL-CLIP: Compound Prompt Learning for Flexible-Modal Face Anti-Spoofing

CPL-CLIP: Compound Prompt Learning for Flexible-Modal Face A...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Ying Zhang Xiangyu Zhu Ajian Liu Xun Lin Jun Wan Jing Zhang Zhen Lei State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences School of Computer Science and Engineering Beihang University School of Computer Science and Engineering Macau University of Science and Technology Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences

ISBN: (数字)9798350364132

ISBN: (纸本)9798350364149

Face anti-spoofing (FAS) is pivotal in safeguarding the integrity of face recognition systems. Flexible-modal FAS utilizes multi-modal data and trains a unified model adaptable to any single-modal testing scenario. This innovation addresses the shortcomings of conventional multi-modal FAS approaches, which typically demand separate model training and deployment for each modality. However, existing flexible-modal FAS approaches activate specific network branches based on the modality of the tested sample. This not only increases the model’s parameters but also necessitates the provision of the image’s modality for testing, thereby constraining deployment flexibility. To address the issue, we present Compound Prompt Learning CLIP (CPL-CLIP), a novel method for flexible-modal FAS. This approach capitalizes on a learned textual prompt that is nearly independent of modality, thus bolstering class-based classification across arbitrary modalities. Specifically, our CPL-CLIP introduces a Dual-Branch Prompt (DBP), consisting of class and modal prompts that describe and guide classification, where each prompt is composed of learnable vectors and fixed templates. To further render the class prompt as modality-agnostic as possible, a Cosine Similarity Loss (CSL) is proposed to facilitate the maximal separation of the class prompt from the modality prompt. With only the class prompt utilized during testing, CPL-CLIP enables deployment in diverse modal testing scenarios without the necessity of the test image’s modality to be known. Extensive experiments demonstrate CPL-CLIP’s superiority over existing methods on several flexible-modal FAS benchmarks.

关键词： Training Adaptation models Technological innovation Accuracy Face recognition Benchmark testing Vectors Robustness Data models Compounds

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AIDE: A Vision-Driven Multi-View, Multi-Modal, Multi-Tasking Dataset for Assistive Driving Perception

AIDE: A Vision-Driven Multi-View, Multi-Modal, Multi-Tasking...

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International Conference on Computer Vision (ICCV)

作者： Dingkang Yang Shuai Huang Zhi Xu Zhenpeng Li Shunli Wang Mingcheng Li Yuzheng Wang Yang Liu Kun Yang Zhaoyu Chen Yan Wang Jing Liu Peixuan Zhang Peng Zhai Lihua Zhang Academy for Engineering and Technology Fudan University Institute of Meta-Medical Boli Technology Co. Ltd. Changchun China Engineering Research Center of AI and Robotics Ministry of Education Shanghai China AI and Unmanned Systems Engineering Research Center of Jilin Province Changchun China

Driver distraction has become a significant cause of severe traffic accidents over the past decade. Despite the growing development of vision-driven driver monitoring systems, the lack of comprehensive perception datasets restricts road safety and traffic security. In this paper, we present an AssIstive Driving pErception dataset (AIDE) that considers context information both inside and outside the vehicle in naturalistic scenarios. AIDE facilitates holistic driver monitoring through three distinctive characteristics, including multi-view settings of driver and scene, multi-modal annotations of face, body, posture, and gesture, and four pragmatic task designs for driving understanding. To thoroughly explore AIDE, we provide experimental benchmarks on three kinds of baseline frameworks via extensive methods. Moreover, two fusion strategies are introduced to give new insights into learning effective multi-stream/modal representations. We also systematically investigate the importance and rationality of the key components in AIDE and benchmarks. The project link is https://***/ydk122024/AIDE.

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An active pursuit strategy for autonomous mobile robots based on deep reinforcement learning

An active pursuit strategy for autonomous mobile robots base...

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IEEE International Conference on Cyborg and Bionic systems (CBS)

作者： Yingnan Gao Lei Cheng Yu He Duanchu Wang Control Science and Engineering Wuhan University of Science and Technology Wuhan China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan China School of Information Science and Engineering/School of Artificial Intelligence Wuhan University of Science and Technology Wuhan China

It is a great challenging but solvable task in an actual robot confrontation: how to effectively pursue the target robot. In this paper, a maximized entropy-based deep reinforcement learning mobile robot active pursuit strategy (APS) approach is proposed for target tracking challenges, then our strategy approach will be implemented on a physical robot. The use of maximizing entropy reinforcement learning allows the agent to have diverse strategies to adapt to dynamic environment changes. We combine learning-based algorithms with rule-based algorithm that allow agent to avoid being targeted by human players to the greatest extent possible in confrontations. We conducted experiments by transferring the learned pursuit strategies to a physical world mobile robot and analyzed the feasibility of application in ICRA RoboMaster AI Challenge (RMUA) environment. The experimental results demonstrate the feasibility and potential of the algorithm in learning multiple tracking strategies.

关键词： Training Deep learning Target tracking Heuristic algorithms Virtual environments Stochastic processes Reinforcement learning

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