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

作者： Zhi Zheng Jin Wang Yuze Wu Qifeng Cai Huan Yu Ruibin Zhang Jie Tu Jun Meng Guodong Lu Fei Gao The State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou China Robotics Institute of Zhejiang University Hangzhou China Robotics Research Center of Yuyao City Ningbo China The State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China Huzhou Institute of Zhejiang University Huzhou China Center for Data Mining and Systems Biology College of Electrical Engineering Zhejiang University Hangzhou China

The Roller-Quadrotor is a novel quadrotor that combines the maneuverability of aerial drones with the endurance of ground vehicles. This work focuses on the design, modeling, and experimental validation of the Roller-Quadrotor. Flight capabilities are achieved through a quadrotor config-uration, with four thrust-providing actuators. Additionally, rolling motion is facilitated by a unicycle-driven and rotor-assisted turning structure. By utilizing terrestrial locomotion, the vehicle can overcome rolling and turning resistance, thereby conserving energy compared to its flight mode. This innovative approach not only tackles the inherent challenges of traditional rotorcraft but also enables the vehicle to roll through narrow gaps and overcome obstacles by taking advantage of its aerial mobility. We develop comprehensive models and controllers for the Roller-Quadrotor and validate their performance through experiments. The results demonstrate its seamless transition between aerial and terrestrial locomotion, as well as its ability to safely roll through gaps half the size of its diameter. Moreover, the terrestrial range of the vehicle is approximately 2.8 times greater, while the operating time is about 41.2 times longer compared to its aerial capabilities. These findings underscore the feasibility and effectiveness of the proposed structure and control mechanisms for efficient rolling through challenging terrains while conserving energy.

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A High-Precision Edge Detection Algorithm Based on Improved Sobel Operator-Assisted HED

A High-Precision Edge Detection Algorithm Based on Improved ...

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IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent systems

作者： Yeborui Yuan Wenyuan Chen Jie Tang Jia Yang School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China State Key Laboratory of Robotics Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

In response to the problem of poor detection performance of the traditional Sobel operator in edge detection, a high-precision edge detection algorithm based on Sobel operator-assisted Holistically-nested Edge Detection (HED) network was proposed, which is named ISAHED here. The algorithm is designed as follows. Firstly, an improved Sobel operator based on the traditional Sobel operator was proposed through adding gradient direction. Then, the image output by the improved Sobel operator is taken as the ground-truth image and as the end-to-end comparison object in a HED-like network. Finally, the ISAHED algorithm outputs edge images with better quality through iterative learning of the HED-like neural network. The experimental results prove that the proposed ISAHED algorithm has better edge detection performance by visually observing the final edge images and further evaluating the values of FOM. This work provides an important reference for the further development of the edge detection technology.

关键词： Automation Image edge detection Image processing Neural networks Control systems Iterative algorithms Intelligent systems

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Magnetically Driven Capsule Robot for Multi-Targeted Biopsy and Drug Delivery in Stomach

Magnetically Driven Capsule Robot for Multi-Targeted Biopsy ...

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IEEE International Conference on robotics and Biomimetics

作者： Zhihui Xu Tanyong Wei Zongze Li Ding Huang 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 Engineering and Automation Harbin Institute of Technology Shenzhen China

Capsule robots have emerged as a promising solution for non-invasive and painless screening of gastrointestinal diseases, offering significant advantages in patient comfort and reduced risk of cross-infection. Despite these advantages, existing capsule robots fall short in their ability to conduct targeted biopsies and deliver drugs to specific gastric lesions. To address this limitation, this paper presents the design and development of a magnetically driven capsule robot that leverages visual feedback to achieve accurate targeting for biopsy and drug administration in gastric lesions. Internal magnets, responsive to an external gradient magnetic field, enable the controlled propulsion of the capsule robot. A kinetic model of the capsule robot is analyzed, allowing it to recognize lesion contours through the endoscopic camera. This recognition determines the rotation angle or small adjustments required for the capsule robot's attitude and positioning. Experimental results in a simplified in vitro stomach model showcase the capability of the capsule robot to achieve targeted biopsy and drug delivery. These results highlight the potential feasibility of the proposed capsule robot in enhancing diagnostic accuracy and treatment efficacy with broader applications within the realm of gastric healthcare.

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Parameter Identification of Chaotic systems Based on Least Squares Method and Pauta Criterion

Parameter Identification of Chaotic Systems Based on Least S...

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Chinese Automation Congress (CAC)

作者： Yu Liu Zhihuan Chen Yaru Qiao Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan University of Science and Technology Wuhan China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan China

In this study, an offline parameter identification approach based on the measured time series of chaotic system is proposed. In this approach, least squares method is firstly introduced to minimize the time series error and Pauta criterion is then employed to pick out the wrong extreme value point. Two representative cases are conducted to verify the effectiveness of our proposed approach. Numerical results are in agreement with theoretical analysis. Comparing to other existing methods, our proposed approach just needs to obtain the measured time series to estimate the parameters, which is simple in theorem and easy to be implemented.

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Creating a Dynamic Online Learning Environment with AI Integration

Creating a Dynamic Online Learning Environment with AI Integ...

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International Computer Sciences and Information Technologies (CSIT)

作者： Oleksandra Bulgakova Zarina Viniarska Viacheslav Zosimov P.M Platonov Educational and Scientific Institute of Computer Engineering Automation Robotics and Computer Programming Odesa National University of Technology Odesa Ukraine Department of Applied information systems Taras Shevchenko National University of Kyiv Kyiv Ukraine

ISBN: (数字)9798331542634

ISBN: (纸本)9798331542641

Educational, online learning has become a common practice due to rapid digitalization and recent global events. This study focuses on developing a comprehensive online learning platform that addresses users' needs by integrating advanced AI capabilities for personalized support and adaptive learning. The platform supports user registration and authentication, course and lesson management, and offers interactive features like AI-driven chat support. In this study, we show the importance of creating user-friendly online learning platforms with the implementation of modern AI trends. The development and testing of this platform is ongoing to determine the adaptation of the system to different learning styles.

关键词： Adaptive learning Electronic learning Authentication Passwords Market research Real-time systems Artificial intelligence Information technology Standards Testing

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Vector Field Path Following of Static Sinusoidal Paths for Underwater Snake Robots

Vector Field Path Following of Static Sinusoidal Paths for U...

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Control Technology and Applications (CCTA),

作者： Ivan Gushkov Amer Orucevic Kristin Y. Pettersen Weijia Yao Jan Tommy Gravdahl Department of Engineering Cybernetics Centre for Autonomous Marine Operations and Systems (NTNU AMOS) Norwegian University of Science and Technology (NTNU) Trondheim Norway School of Robotics Hunan University China Engineering and Technology Institute Groningen University of Groningen The Netherlands

In this paper, a guiding vector field (GVF) approach is applied to solve the path-following problem of a static unbounded sinusoidal path for an underwater snake robot (USR) in the presence of constant irrotational currents. A sliding mode velocity controller for the USR is designed, and the path-following error dynamics for the normalized GVF are analyzed through cascaded systems theory. The cascade is shown to be globally uniformly asymptotically stable (GUAS) when perturbed by a uniformly bounded interconnection term which vanishes with the heading error. Results from a simulation study with a complex USR model are presented, validating the design approach.

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Design of a Mesh Crawling Robot with Beetle-Inspired Variable Stiffness Tarsus

Design of a Mesh Crawling Robot with Beetle-Inspired Variabl...

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IEEE International Conference on robotics and Biomimetics

作者： Hao Chen Kai Hu Longlong Hu Ziqi Zhou Yao Li Bing Li Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Key University Laboratory of Mechanism & Machine Theory and Intelligent Unmanned Systems of Guangdong Shenzhen China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Compared with large robots, micro-robots have the advantages of small size, low power consumption, and high concealment. For complex terrain, the crawling performance of micro-robots will be significantly reduced. In this study, the mesh surface was used as the crawling base of the robot, and a variable stiffness tarsus was designed with the beetle as a reference. This tarsus composed of multiple bending parts achieves a large range of stiffness changes, 0.07~0.495 N/mm. We have achieved stiffness control of the tarsus in a single actuation by trajectory planning of the claw ends. The proposed robot can achieve a big stiffness of the tarsus to resist the deformation of the tarsus due to external forces, which means that the robot can increase the adhesion ability when crawling at the stance phase. Therefore, even when the load was increased from 10g to 40g, the average crawling speed of the robot was not significantly reduced. This new tarsus can improve the crawling performance of the micro-robot on the mesh.

关键词： Legged locomotion Power demand Trajectory planning Force Kinematics Resists Bending Robots Substrates Trajectory optimization

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Simulation of Dynamic Environments for SLAM

arXiv

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arXiv 2023年

作者： Bonetto, Elia Xu, Chenghao Ahmad, Aamir Max Planck Institute for Intelligent Systems Tübingen Germany Institute of Flight Mechanics and Controls University of Stuttgart Stuttgart Germany Faculty of Mechanical Maritime and Materials Engineering Department of Cognitive Robotics Delft University of Technology Delft Netherlands

Simulation engines are widely adopted in robotics. However, they lack either full simulation control, ROS integration, realistic physics, or photorealism. Recently, synthetic data generation and realistic rendering has advanced tasks like target tracking and human pose estimation. However, when focusing on vision applications, there is usually a lack of information like sensor measurements or time continuity. On the other hand, simulations for most robotics tasks are performed in (semi)static environments, with specific sensors and low visual fidelity. To solve this, we introduced in our previous work a fully customizable framework for generating realistic animated dynamic environments (GRADE) [1]. We use GRADE to generate an indoor dynamic environment dataset and then compare multiple SLAM algorithms on different sequences. By doing that, we show how current research over-relies on known benchmarks, failing to generalize. Our tests with refined YOLO and Mask R-CNN models provide further evidence that additional research in dynamic SLAM is necessary. The code, results, and generated data are provided as open-source at https://***/grade-rr. Copyright © 2023, The Authors. All rights reserved.

关键词： Target tracking

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How to Price Fresh Data with Strategic Users

How to Price Fresh Data with Strategic Users

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International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)

作者： Junyi He Meng Zhang Qian Ma Jianwei Huang Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS) School of Science and Engineering The Chinese University of Hong Kong Shenzhen Guangdong china Zhejiang University/University of Illinois at UrbanaChampaign Institute Zhejiang University Haining China School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen Guangdong China School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society The Chinese University of Hong Kong Shenzhen Shenzhen China

The interests in obtaining fresh data in real-time applications have facilitated fresh data markets. However, existing works on designing fresh data markets have ignored strategic users. Being strategic means that users can optimally time their data purchases, which affects markets' profit. In this paper, we study a fresh data market, where strategic users with heterogeneous data valuations stochastically arrive over time. The strategic users decide the time of data purchase, considering the evolution of data freshness and prices, while the platform decides the data pricing policy over time to maximize its profit. We first consider a dynamic pricing policy, where the platform offers a price menu to each arrival user. The analysis is technically challenging, as heterogeneous users face different integer programming problems in optimizing their data purchase time, making direct optimization of data prices infeasible. To tackle the challenge, we adopt a mechanism design approach. We show that the direct mechanism design problem relax the original problem and obtain the optimal pricing policy analytically. Next, to reduce the implementation complexity, we study a single pricing policy, where the price is fixed over time. We derive the optimal single price analytically in a two-period refreshing model. Perhaps surprisingly, although strategic users have more purchasing options than non-strategic users, users who behave strategically may be worse off. Simulation results show that, although a platform refreshes the data less frequently in the presence of strategic users than facing myopic users, it can earn up to 5 times higher profit.

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Recognition of Dynamic Grasping Motion through Feature Extraction for Teleoperation

Recognition of Dynamic Grasping Motion through Feature Extra...

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WRC Symposium on Advanced robotics and Automation (WRC SARA)

作者： Yanping Dai Wenyuan Chen Ning Xi Wenxue Wang State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Department of Industrial and Manufacturing Systems Engineering Emerging Technologies Institute University of Hong Kong Hong Kong China

Detecting human movement intentions and transmitting human operational capabilities to robots is of great significance for teleoperations. However, the difference in kinematic structure between human hands and robotic manipulators brings difficulties to teleoperation mapping. In this paper, we present an approach to hand grasp recognition and mapping based on feature extraction to drive heterogeneous remote manipulator. The hand grasp data set HUST recorded with a data glove was used for this study. The data were preprocessed and their features were extracted by PCA. A neural network-based classifier was obtained through offline training of human grasping activities. We compared different network models and selected the network with the best performance based on the training time and the task requirements of teleoperation. The experimental results showed that the recognition accuracy of dynamic grasping motion with feature extraction is higher than that without feature extraction and can be up to 97.47%. Finally, we validated the feasibility of this method to tele-operate an Barrett hand through online experiments. Our proposed method provides an effective approach of mapping natural human grasping operations to heterogeneous robotic teleoperation systems.

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