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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Liu, Weilong Wang, Jin Zhang, Kewen Yu, Huan Zheng, Zhi Lu, Guodong State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China Robotics Research Center of Yuyao City Ningbo315400 China College of Mechanical Engineering Zhejiang University of Technology Hangzhou310023 China Robotics Institute of Zhejiang University Hangzhou310027 China

ISBN: (纸本)9798350312201

Multi-Agent Path Finding (MAPF) has been widely studied in the field of artificial intelligence and multi-agent path search, and researchers have made improvements to make such algorithms applicable to actual nonholonomic constrained robots. In this paper, the collision detection is improved by body-occupied collision detection, which improves the efficiency of collision detection. In addition, to solve the problem of low efficiency in centralized search for multi-robot solutions, reasonable grouping is performed to reduce the number of collision nodes expanded in the centralized Conflict-based search(CBS), and feasible results are obtained faster. At the same time, the heuristic multi-robot search based on collision frequency is improved to reduce the search time. Finally, we constructed the trajectory smoothing problem based on the multi-robot path search results and applied the solution to real robot trajectory tracking. Our research provides valuable insights into solving trajectory planning problems for nonholonomic constrained mobile robots in practical scenarios. © 2023 IEEE.

关键词： Motion planning

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Predict Hip Joint Moment Using CNN for Hip Exoskeleton Control 1

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

作者： Zhang, Yuanwen Xiong, Jingfeng Qian, Yuepeng Chen, Xinxing Guo, Yixuan Fu, Chenglong Leng, Yuquan Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen518055 China National University of Singapore Singapore117583 Singapore

ISBN: (数字)9789819964987

ISBN: (纸本)9789819964970

The assistance of the hip exoskeleton is closely related to hip joint moment, and the corresponding assistance can be generated by hip joint moment directly. However, it is difficult to obtain the hip joint moment directly from the sensor during walking. An effective method is to use deep learning models to predict hip joint moment using hip joint angle information. But there are still many challenges in applying this method to practical control systems. This study designs specific parameters to train a deep neural network for predicting hip joint moments and discusses the effects of the number of convolutional layers (N) and the length of sampling time (SL) on the prediction accuracy and the computation time in a real control system. Experiments show that for N ≤ 5, using angle information with SL ≤ 1.0 s achieves the highest prediction accuracy. However, for larger values of N (N > 5), the optimal SL becomes to be longer, where it increases to 2.0 s and the corresponding RMSE is 0.06260 Nm/kg and 0.05977 Nm/kg, respectively. These results indicate the importance of SL for prediction accuracy. Furthermore, a comparative analysis of the computation time with models using different N and SL is performed, and the relationship between computation time and prediction accuracy is discussed. This study provides specific model training parameters and detailed comparative analysis, which facilitates further application of deep learning models on hip exoskeletons. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Forecasting

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Design of a Small-Scale Locust-Inspired Robot Capable of Jump-gliding Locomotion

Design of a Small-Scale Locust-Inspired Robot Capable of Jum...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Zhang, Weitao Wei, Zisen Xu, Yi Peng, Liang Dong, Changwen Jin, Yanzhou Shi, Qing Ministry of Education Key Laboratory of Biomimetic Robots and Systems Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Intelligent Robotics Institute School of Mechatronical Engineering Beijing100081 China

ISBN: (纸本)9798350325706

Small-scale robots are widely used in real-world rescue missions, but their mobility and movement range are still limited. One solution to improve their adaptability to complex environments is to introduce jumping and gliding strategies into the robot design. In this paper, we developed a small-scale locust-inspired robot capable of active (propeller-driven) gliding after launching from the ground, which had a body length of 19.1 cm and a weight of 97 g. On the basis of the locust's musculoskeletal model, the jumping system was designed by a six-bar mechanism which can amplify the power. To improve the lift in gliding phase while reducing the draft in jumping phase, the gliding system with a folding wing and a front propeller was proposed corresponding to the fixed-wing principle. The results obtained through a series of experimental tests reveal that the robot achieves a jumping height of 0.15 m and a passive gliding distance of 1.5 m, which has a glide ratio of 1.13. Remarkably, actuating by a propeller, the robot can jump up to a height of 0.20 m, covering a gliding distance of 2.9 m. It is worth noting that the glide ratio of the robot improves by 91.2% in the propeller-driven jump-gliding mode. © 2023 IEEE.

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Combine Neural Radiance Fields in Different Reference Coordinates

Combine Neural Radiance Fields in Different Reference Coordi...

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2023 China Automation Congress, CAC 2023

作者： Zhang, Weili Li, Zhongheng Guo, Yu Wang, Fei Institute of Artificial Intelligence and Robotics Xi'An Jiaotong University Xi'an China Beijing Institute of Spacecraft System Engineering Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications Beijing China

ISBN: (纸本)9798350303759

Neural Radiance Fields (NeRF) leverage deep learning networks to restore high-quality implicit representations of 3D scenes from 2D images, surpassing traditional texture mapping in rendering quality. Typically, reconstructed images by NeRF exist in a reference coordinate system, necessitating consideration of each surface's information in a single shot. Overlapping parts in adjacent images are addressed by obtaining the camera pose through sparse COLMAP reconstruction. However, occluded regions, caused by contact with support surfaces, pose challenges for complete reconstruction. To address this, we propose a scene fusion algorithm based on NeRF, facilitating the amalgamation of neural radiance fields under different reference coordinate systems. This reduces data requirements and shooting conditions, enhancing the implicit 3D reconstruction of real objects. By estimating the translation and rotation relationship between image capture angles and the NeRF model, we fuse neural radiance fields in distinct reference coordinate systems, completing object surface information. Evaluation is conducted on both NeRF synthetic dataset and real objects. © 2023 IEEE.

关键词： Cameras

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Power Line Following Based on Measurements of the Magnetic Field

Power Line Following Based on Measurements of the Magnetic F...

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International Conference on Unmanned Aircraft systems (ICUAS)

作者： Goran Vasiljević Dean Martinović Stjepan Bogdan Faculty of Electrical Engineering and Computing Laboratory for Robotics and Intelligent Control Systems Unska 3 University of Zagreb Zagreb Croatia

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

In this paper, we present a method to control the UAV to follow the power line with a specific position and orientation based only on the measurement of the magnetic field generated by the current flow in the power line. In this way, it is possible to localize the UAV with respect to the power line without the need for additional sensors, even in poor visibility conditions. The measurements from four magnetometers attached to the UAV are used to solve an optimization problem that involves determining the relative pose of the UAV with respect to the power line. Based on the relative pose, the UAV is controlled to follow the power line in a predefined position and orientation. Experiments in a test setup have confirmed that the method is applicable in a realistic environment.

关键词： Location awareness Visualization Power measurement Magnetic field measurement Magnetometers Current measurement Position measurement Autonomous aerial vehicles Magnetic fields Optimization

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Enhancing Impulse Response-Based SSVEP Decoding Method by Incorporating Temporal Local Correlation 7

Enhancing Impulse Response-Based SSVEP Decoding Method by In...

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7th International Conference on Biological Information and Biomedical engineering, BIBE 2024

作者： Huang, Jiayang Wang, Jiaxi Yang, Pengfei Xiong, Bang Zhang, Zhi-Qiang School of Computer Science and Technology The Key Laboratory of Smart Human-Computer Interaction Wearable Technology of Shaanxi Province Xidian University China School of Electronic and Electrical Engineering Institute of Robotics Autonomous Systems and Sensing University of Leeds United Kingdom

ISBN: (纸本)9783800762910

Various training-based spatial filtering methods have been proposed to classify steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). However, many overlook the temporal instability of SSVEPs, leading to decreased classification performance. Therefore, we propose an SSVEP decoding method that calculates template signals and spatial filters using the subject's impulse responses and periodic impulses, incorporating the temporally local correlation coefficient in the SSVEP decoding process. Then, we employ a combined correlation coefficient for SSVEP classification. The proposed method addresses the temporal instability of SSVEPs, with template signals that exhibit strong individual characteristics. Utilizing a public benchmark dataset, we achieved a classification accuracy of 87.43% and an ITR of 256.1 bits/min at 1s time window, demonstrating the performance of this method. Compared to other classical spatial filtering methods, the proposed method demonstrates superior classification performance, which can enhance the practical usability of SSVEP-BCI systems. © VDE VERLAG GMBH.

关键词： Wiener filtering

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Control Aspects in Programming by Demonstation for Robotic Deep- Micro- Hole Drilling

Control Aspects in Programming by Demonstation for Robotic D...

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Mediterranean Conference on Control and Automation (MED)

作者： Bruno Maric Frano Petric Matko Orsag LARICS Laboratory for Robotics and Intelligent Control Systems Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

This paper explores control aspects in a collab-orative robotic system designed to replicate human operators' drilling skills in deep- micro- hole tasks, focusing on the glass-container mould industry. To replicate human performance, we develop a single-DOF Impedance-based drill controller for a standard industrial position-controlled robot, enhancing safe and smooth interactions between the drill bit and the mould. To address high feed rates observed in worker demonstrations and prevent bit breaks, we focus on minimizing non-axial motion during drilling on an off-the-shelf available industrial manipulator KUKA KR 10. This involves analyzing the impact of joint dynamics on drill motion and designing a control system for the robot's low-level control to achieve axial drill motion. We show that the proposed joint control system that takes into account relationship between joint and tool dynamics outperforms available robot low-level control in minimizing non-axial motion, leading to the robot being able to outperform the human in deep- micro- hole drilling.

关键词： Drilling Service robots Dynamics Robot control Time factors Synchronization Task analysis

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Learning Rat-Like Behavior for a Small-Scale Biomimetic Robot

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engineering 2022年第10期17卷 232-243页

作者： Zihang Gao Guanglu Jia Hongzhao Xie Qiang Huang Toshio Fukuda Qing Shi Key Laboratory of Biomimetic Robots and Systems(Beijing Institute of Technology) Ministry of EducationBeijing 100081China Intelligent Robotics Institute School of Mechatronical EngineeringBeijing Institute of TechnologyBeijing 100081China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of TechnologyBeijing 100081China

Existing biomimetic robots can perform some basic rat-like movement primitives(MPs)and simple behavior with stiff combinations of these *** mimic typical rat behavior with high similarity,we propose parameterizing the behavior using a probabilistic model and movement ***,an analysis of fifteen 10 min video sequences revealed that an actual rat has six typical behaviors in the open field,and each kind of behavior contains different bio-inspired combinations of eight *** used the softmax classifier to obtain the behavior-movement hierarchical probability ***,we specified the MPs using movement parameters that are static and *** obtained the predominant values of the static and dynamic movement parameters using hierarchical clustering and fuzzy C-means clustering,*** predominant parameters were used for fitting the rat spinal joint trajectory using a second-order Fourier series,and the joint trajectory was generalized using a back propagation neural network with two hidden ***,the hierarchical probability model and the generalized joint trajectory were mapped to the robot as control policy and commands,*** implemented the six typical behaviors on the robot,and the results show high similarity when compared with the behaviors of actual rats.

关键词： Biomimetic Bio-inspired robot Neural network learning system Behavior generation

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AI-Enhanced Structural Health Monitoring with a Multi-Rotor Aerial Vehicle

AI-Enhanced Structural Health Monitoring with a Multi-Rotor ...

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International Conference on Unmanned Aircraft systems (ICUAS)

作者： Filip Zorić Ana Milas Tamara Petrović Zdenko Kovačić Matko Orsag Faculty of Electrical Engineering and Computing LARICS Laboratory for Robotics and Intelligent Control Systems University of Zagreb Zagreb Croatia

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

Roads, bridges, tunnels, railways and canals are crucial for the transportation of goods and people in modern society. Modern infrastructure is subject to damage and requires therefore regular inspection and maintenance to remain safe and functional. While this can be done manually, large parts of the mentioned infrastructure are hardly accessible, which is a main motivational factor for the use of multi-rotor aerial vehicles (MAV s) for inspection and maintenance tasks. MAV s, paired with novel deep learning computer vision techniques, such as instance segmentation, are emerging as an obvious choice for automating certain parts of structural health monitoring (SHM). In this manuscript, we provide an overview of available SHM datasets and how they can be utilized for the task of autonomous crack detection. We use available datasets to train and compare two neural network architectures for instance segmentation. The segmented crack instances are then localized in a global coordinate frame to perform autonomous mapping of the potentially dangerous infrastructure defects. Experimental studies demonstrate the effectiveness of the proposed SHM approach, which results in precisely localized infrastructure defects marked on the global map.

关键词： Instance segmentation Irrigation Structural panels Neural networks Transportation Inspection Rail transportation

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Active Scout: Multi-Target Tracking Using Neural Radiance Fields in Dense Urban Environments

Active Scout: Multi-Target Tracking Using Neural Radiance Fi...

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

作者： Christopher D. Hsu Pratik Chaudhari DEVCOM Army Research Laboratory Department of Electrical & Systems Engineering and General Robotics Automation Sensing and Perception (GRASP) Laboratory University of Pennsylvania

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

We study pursuit-evasion games in highly occluded urban environments, e.g. tall buildings in a city, where a scout (quadrotor) tracks multiple dynamic targets on the ground. We show that we can build a neural radiance field (NeRF) representation of the city—online—using RGB and depth images from different vantage points. This representation is used to calculate the information gain to both explore unknown parts of the city and track the targets—thereby giving a completely first-principles approach to actively tracking dynamic targets. We demonstrate, using a custom-built simulator using Open Street Maps data of Philadelphia and New York City, that we can explore and locate 20 stationary targets within 300 steps. This is slower than a greedy baseline, which does not use active perception. But for dynamic targets that actively hide behind occlusions, we show that our approach maintains, at worst, a tracking error of 200m; the greedy baseline can have a tracking error as large as 600m. We observe a number of interesting properties in the scout’s policies, e.g., it switches its attention to track a different target periodically, as the quality of the NeRF representation improves over time, the scout also becomes better in terms of target tracking.

关键词： Target tracking Urban areas Buildings Games Neural radiance field Information filters Probabilistic logic Mutual information Intelligent robots Quadrotors

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