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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Ming Yang Dingkui Tian Minjia Xu Feng Li Jianquan Sun Weiwei Shang Xinyu Wu University of Science and Technology of China Hefei P. R. China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Daqing Vocational College Daqing China SIAT-CUHK Joint Laboratory of Robotics and Intelligent Systems Shenzhen China

Climbing stairs has been an indispensable ability for humanoids. This paper presents a novel and practical problem: climbing stairs of varying heights. In this study, the humanoid is modeled as an inverted pendulum and set the pelvis trajectory on the vertical axis, representing the center of mass (CoM), as a quadratic function. We also use a polynomial to design the ZMP and CoM trajectory on the horizontal plane. To generate the CoM trajectory, the sliding mode controller is utilized to track the reference trajectory. To eliminate chattering of the sliding mode controller, the universal approximation property of the radial basis function (RBF) network is leveraged to approximate the switching law. Finally, simulation experiments are performed in CoppeliaSim, and the biped robot is capable of walking stably and quickly.

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Design of a Force-Controlled End-Effector with Slender Flexible Beams 16th

Design of a Force-Controlled End-Effector with Slender Fl...

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16th International Conference on intelligent robotics and Applications, ICIRA 2023

作者： Chen, Yin Chen, Genliang Chai, Yuchen Wang, Hao Kong, Lingyu State Key Laboratory of Mechanical Systems and Vibration Shanghai Jiao Tong University Shanghai200240 China Meta Robotics Institute Shanghai Jiao Tong University Shanghai200240 China Shanghai Key Laboratory of Digital Manufacturing for Thin-Walled Structures Shanghai Jiao Tong University Shanghai200240 China Intelligent Robot Research Center Zhejiang Lab Hangzhou China

ISBN: (纸本)9789819965007

This paper proposes a force-controlled end-effector with slender flexible beams. On the one hand, it can actively control the deformation of slender flexible beams to output a constant force at the end-effector without using a force sensor. On the other hand, it serves as a passive compliant mechanism utilizing the large deformation characteristic of slender flexible beams to prevent structural damage. The kinetostatic modeling of the force-controlled end-effector has been derived to obtain the relationship between the output force and displacement. To conduct experimental tests, a prototype of the force-controlled end-effector has been fabricated. From the experimental results, it can be seen that the error of the force-controlled end-effector is within 0.5N. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Compliant mechanisms

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Two ways to make your robot proactive: reasoning about human intentions, or reasoning about possible futures

arXiv

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arXiv 2022年

作者： Buyukgoz, Sera Grosinger, Jasmin Chetouani, Mohamed Saffiotti, Alessandro SoftBank Robotics Europe Paris France Sorbonne University Institute for Intelligent Systems and Robotics CNRS UMR 7222 Paris France AASS Cognitive Robotic Systems Lab School of Science and Technology Orebro University Orebro Sweden

Robots sharing their space with humans need to be proactive in order to be helpful. Proactive robots are able to act on their own initiative in an anticipatory way to benefit humans. In this work, we investigate two ways to make robots proactive. One way is to recognize human's intentions and to act to fulfill them, like opening the door that you are about to cross. The other way is to reason about possible future threats or opportunities and to act to prevent or to foster them, like recommending you to take an umbrella since rain has been forcasted. In this paper, we present approaches to realize these two types of proactive behavior. We then present an integrated system that can generate proactive robot behavior by reasoning on both factors: intentions and predictions. We illustrate our system on a sample use case including a domestic robot and a human. We first run this use case with the two separate proactive systems, intention-based and prediction-based, and then run it with our integrated system. The results show that the integrated system is able to take into account a broader variety of aspects that are needed for proactivity. © 2022, CC BY.

关键词： Human robot interaction

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GO-VMP: Global Optimization for View Motion Planning in Fruit Mapping

arXiv

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arXiv 2025年

作者： Jose, Allen Isaac Pan, Sicong Zaenker, Tobias Menon, Rohit Houben, Sebastian Bennewitz, Maren Bonn-Rhein-Sieg University of Applied Sciences Germany Humanoid Robots Lab University of Bonn Germany Fraunhofer Institute for Intelligent Analysis and Information Systems Germany Humanoid Robots Lab University of Bonn Lamarr Institute for Machine Learning and Artificial Intelligence Center for Robotics University of Bonn Germany

Automating labor-intensive tasks such as crop monitoring with robots is essential for enhancing production and conserving resources. However, autonomously monitoring horticulture crops remains challenging due to their complex structures, which often result in fruit occlusions. Existing view planning methods attempt to reduce occlusions but either struggle to achieve adequate coverage or incur high robot motion costs. We introduce a global optimization approach for view motion planning that aims to minimize robot motion costs while maximizing fruit coverage. To this end, we leverage coverage constraints derived from the set covering problem (SCP) within a shortest Hamiltonian path problem (SHPP) formulation. While both SCP and SHPP are well-established, their tailored integration enables a unified framework that computes a global view path with minimized motion while ensuring full coverage of selected targets. Given the NP-hard nature of the problem, we employ a region-prior-based selection of coverage targets and a sparse graph structure to achieve effective optimization outcomes within a limited time. Experiments in simulation demonstrate that our method detects more fruits, enhances surface coverage, and achieves higher volume accuracy than the motion-efficient baseline with a moderate increase in motion cost, while significantly reducing motion costs compared to the coverage-focused baseline. Real-world experiments further confirm the practical applicability of our approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Fruits

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Lateral Walking Gait Phase Detection with Data Optimization for a Hip Exoskeleton in Resistance Exercise

Lateral Walking Gait Phase Detection with Data Optimization ...

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IEEE Workshop on Advanced robotics and its Social Impacts, ARSO

作者： Wujing Cao Xinbo Zhu Rui Xu Changyu Li Hongliu Yu Mingming Zhang Haoyong Yu Worawarit Kobsiriphat Xinyu Wu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society and SIAT-CUHK Joint Laboratory of Robotics and Intelligent Systems. College of Automation Engineering Nanjing University of Aeronautics and Astronautics Jining Zhongke Intelligent Technology Co. Ltd. University of Shanghai for Science and Technology Southern University of Science and Technology Department of Biomedical Engineering National University of Singapore. National Metal and Materials Technology Center Thailand

ISBN: (数字)9798350344639

ISBN: (纸本)9798350344646

The accurate detection of lateral walking gait phases is essential for the effective implementation of hip exoskeleton systems in lateral resistance walking exercises. However, limitations in hardware, such as memory and computing power, in the microcontrollers of wearable devices, significantly impact the size and training speed of the lateral walking gait phase detection model, thus affecting the exoskeleton system. This study proposes a data optimization algorithm that utilizes K-means clustering combined with commonly used machine learning algorithms, including Random Forests (RF), Support Vector Machines (SVM), and k-Nearest Neighbors (KNN), to reduce both the training time and size of the model. With the implementation of this algorithm, the training time and model size of RF, SVM, and KNN-based models are reduced by 89.6%, 99.8%, and 97.9%, and 89.6%, 92.7%, and 95.2% respectively. The corresponding gait phase prediction accuracy experiences only a slight decrease of 1.6%, 1.7%, and 2.8% respectively. This method ensures a sufficiently high accuracy in detecting lateral walking gait phases while simultaneously achieving higher efficiency and a smaller model size.

关键词： Legged locomotion Training Support vector machines Solid modeling Machine learning algorithms Accuracy Exoskeletons

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Airborne Sense and Detect of Drones using Deep Learning and LiDAR Point Clouds

arXiv

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arXiv 2023年

作者： Manduhu, Manduhu Dow, Alexander Trslic, Petar Dooly, Gerard Blanck, Benjamin Riordan, James Drone Systems Lab School of Computing Engineering and Physical Sciences University of the West of Scotland Glasgow United Kingdom Centre for Robotics & Intelligent Systems Department of Electronic & Computer Engineering University of Limerick Limerick Ireland Hamburg Port Authority Hamburg Germany

The safe operation of drone swarms beyond visual line of sight requires multiple safeguards to mitigate the risk of collision between drones flying in close-proximity scenarios. Cooperative navigation and flight coordination strategies that rely on pre-planned trajectories, constant network connectivity and reliable Global Navigation Satellite System (GNSS) positioning are brittle to failure. Drone embedded sense and detect offers a comprehensive mode of separation between drones for deconfliction and collision avoidance. This paper presents the first airborne LiDAR based solution for drone-swarm detection and localization using 3D deep learning model. It adapts an existing deep learning neural network to the air-to-air drone scenario by expanding the scan space vertically. A new sparse convolution is proposed and applied to accelerate the backbone layer, which is the most time-consuming part of the neural network. To collect training data of safety critical, close-proximity multi-drone operations, a scenario Digital Twin is used to augment real datasets with high fidelity synthetic data. The trained model achieves over 80% recall and 96% precision when tested on real-world datasets. By incorporating a tracking-by-detection algorithm the system can reliably monitor the separation distance of multiple drones in challenging environments. Copyright © 2023, The Authors. All rights reserved.

关键词： Drones

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Real-time Terrain Recognition based on Transformer and Gait Prediction based on CNN-LSTM-Attention for Exosuit

Real-time Terrain Recognition based on Transformer and Gait ...

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2023 IEEE International Conference on Real-Time Computing and robotics, RCAR 2023

作者： Yang, Fangliang Chen, Chunjie Wang, Zhuo Xu, Yinsheng Liu, Yao Wu, Xinyu University of Chinese Academy of Sciences Shenzhen College of Advanced Technology Shenzhen518055 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China Guilin University of Technology Guilin541004 China

ISBN: (纸本)9798350327182

For wearable soft exosuits, an imprecise control strategy can easily injure the wearer, while real-time terrain recognition and accurate gait phase estimation can effectively improve the control strategy of the exosuit and make the wearer comfortable and safe. In this paper, a real-time terrain recognition algorithm based on Transformer and a gait estimation algorithm based on CNN-LSTM-Attention are implemented. The Transformer-based recognition of different terrains improves the recognition accuracy to some extent, and the CNN-LSTM-Attention based feature extraction for temporal signals such as gait phase is also extremely noticeable. Experiments show that the Transformer-based algorithm achieves 99.64 % recognition accuracy in six different terrain environments. In the gait phase estimation experiment, CNN-LSTM-CBAM achieved the best performance with an evaluation index r2 of 0.9221. The aforementioned terrain recognition algorithms and gait phase estimation algorithms may have a positive impact on soft exosuit and dynamic prosthetics research. © 2023 IEEE.

关键词： Landforms

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Preliminary study of online real-time control system for lower extremity exoskeletons based on EEG and sEMG fusion

Preliminary study of online real-time control system for low...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Li, Wenju Shao, Keyong Zhu, Chi Ma, Yue Cao, Wujing Yin, Meng Yang, Lijun Luo, Mingxiang Wu, Xinyu Northeast Petroleum University School of Electrical Engineering Information Daqing China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Guangdong Shenzhen518055 China Maebashi Institute of Technology Department of Systems Life Engineering Maebashi3710132 Japan

ISBN: (纸本)9781665481090

Motion imaging (MI) is widely used in exoskele-ton robot control direction. However, online real-time con-trol of exoskeletons based on biological signals is difficult to achieve. Because the anti-interference of biological signals in real life is poor and affected by individual differences, the control of auxiliary equipment by the realization of human-machine interface is still facing great challenges. In this paper, a human-machine interface (HMI) based on hybrid biological signals is designed and online real-time control of lower extremity exoskeletons is realized. Based on a comprehensive analysis of human walking, the HMI uses the electromyography signals (upper extremities) and EEG signals generated during real exercise as input signals. Different analysis methods are designed for different modal signals. We extracted four features of sEMG signals to distinguish between different movement processes in the human body. For EEG signals, the use of Independent Component Analysis (ICA) increases the robustness of the system. Event-related desynchronization/synchronization (ERS/ERD) and power spectral densities are reused for analysis from the perspective of the interaction of multiple information in the channel, time and frequency domains. The human-machine interface integrates EEG and EMG multimodal biological signals, and the average recognition accuracy of different subjects has reached 93.7%. In order to increase the recognition accuracy of practical applications and reduce the differences between different individuals, a window voting mechanism is designed. Healthy subjects were tested, and the recognition accuracy of the lower limb exoskeleton online real-time assistance system reached 99%, with high accuracy and reliability. © 2022 IEEE.

关键词： Real time control

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Robust Motion Mapping Between Human and Humanoids Using CycleAutoencoder

Robust Motion Mapping Between Human and Humanoids Using Cycl...

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IEEE International Conference on robotics and Biomimetics

作者： Matthew Stanley Lingfeng Tao Xiaoli Zhang Intelligent Robotics and Systems Lab Colorado School of Mines Golden CO USA

ISBN: (纸本)9781665405362

Teleoperation needs accurate and robust motion mapping between human and humanoid motion to generate intuitive robot control with human-like motion. Data-driven methods are often deployed as it can result in intuitive, real time motion mapping. When using these methods, the common focus is on the accuracy of the motion mapping model. However, effort needs to be put into making the mapping model robust in face of noisy or incomplete dataset. In other words, the model needs to learn the generalizable mapping rules, not just be accurate in predicting the training data. To create a robust and accurate model for motion mapping, we developed the novel CycleAutoencoder method. This method simultaneously trains two autoencoders using traditional losses, mixed losses, and cycle losses. These losses allow the autoencoders to reconstruct the motion mutually between humans and humanoids. This allows the method to learn the mapping with improved accuracy and robustness compared to training a traditional autoencoder. The results of human subject involved experiments demonstrated that the CycleAutoencoder method can achieve both accuracy and robustness for the mapping compared with other autoencoder-based mapping methods.

关键词： Training Robot control Humanoid robots Training data Predictive models Robustness Real-time systems

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Trend of intelligent Robot Application Based on intelligent Agriculture System

Trend of Intelligent Robot Application Based on Intelligent ...

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International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM)

作者： Xia Han Lu Xu Yifei Peng Zefeng Wang Special Robotics Lab Huzhou University Huzhou China Agricultural Robotics Lab Nanjing Tuobu Intelligent Technology Co. LTD Nanjing China Robotics Materials Lab Association of intelligent systems and robotics (ASIR) Paris France

ISBN: (纸本)9781665417334

At present, China's artificial intelligence technology, Internet technology development increasingly mature, gradually applied in the field of agricultural robots. The use of artificial intelligence technology to assist agricultural production can significantly improve the quality and efficiency of agricultural production, effectively promote farmers' increased production and income, and increase their economic benefits. This paper analyzes the necessity of agricultural mechanization from the current situation of agricultural development in China, and analyzes the wide application of agricultural robots from the current situation of agricultural mechanization. Six key technologies of artificial intelligence used in modern intelligent agricultural robots are listed, and some application examples are provided corresponding to these technologies. This article explores the development trend of artificial intelligence in intelligent agriculture, and proposes the concept of “four-dimensional agriculture” with decentralized integration of supply, transportation and marketing of agricultural products, as well as the concept of an overall solution of “intelligent agriculture” based on intelligent robotics. The content includes: 1. remote intelligent agricultural monitoring; 2. standardized production of agricultural products; 3. safety traceability and anti-counterfeit authentication of agricultural products.

关键词： Agricultural robots Roads Transportation Production Market research Safety Reliability

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