检索结果-内蒙古大学图书馆

Autonomous Liver Ultrasound Examination Based on Imitation Learning and Stiffness Estimation

学校读者我要写书评

暂无评论

Autonomous Liver Ultrasound Examination Based on Imitation L...

IEEE International Conference on Industrial Technology (ICIT)

作者： Zihao Wang Donghao Shi Chenguang Yang Weiyong Si Qinchuan Li School of Mechanical Engineering Zhejiang Sci-Tech University Hangzhou China Bristol Robotics Laboratory University of the West of England Bristol The United Kingdom School of Computer Science and Electronic Engineering University of Essex London The United Kingdom

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

In the past few decades, the demand for assistive robots in the field of healthcare has steadily increased. We conducted research on an autonomous robot system based on imitation learning to assist in liver scans guided by ultrasound. Traditional procedures for liver examination using ultrasound imaging typically require a physician to slide the ultrasound probe along the patient's lower ribs for multi-angle observation. To automate this process, we employed imitation learning to capture the physician's manipulation techniques. However, the imitation generalization system often struggles to accurately extract the starting and ending points due to the obstruction of fat and skin, and the individual variations in patient anatomy lead to inconsistencies in the positions of lower ribcage. To improve the scanning process, we introduced stiffness estimation to differentiate between the abdomen and the rib, allowing real-time adjustments for optimal alignment of lower ribcage. Furthermore, due to the need for occasional physician intervention during the detection process, we implemented mechanisms for pausing and resuming the trajectory learning system to ensure continued operation. Ultimately, the proposed approach was evaluated using the Franka Panda robot system and demonstrated autonomous liver ultrasound examination in scenarios involving multiple human subjects.

关键词： Visualization Ultrasonic imaging Education Liver Estimation Medical services Skin

Efficient Arm Motion Planning with Reduced Viapoints for Mars Rover Operations

学校读者我要写书评

暂无评论

Efficient Arm Motion Planning with Reduced Viapoints for Mar...

Space robotics (iSpaRo), International Conference on

作者： Justin Huang Valen Yamamoto Ana Warner Jet Propulsion Laboratory California Institute of Technology Pasadena CA Department of Electrical Engineering and Computer Science UC Irvine Irvine CA Department of Robotics University of Michigan Ann Arbor MI

ISBN: (数字)9798350367232

ISBN: (纸本)9798350367249

This paper describes additions and enhancements we made to the standard RRT-Connect motion planning algorithm to suit the unique needs of operating the robotic arm on the Perseverance Mars Rover. One constraint we have is to maintain a minimum distance between the terrain and any part of the arm hardware. Another goal specific to rover planning is to minimize the number of viapoints in our motion plans. These algorithms should also run efficiently so that rover planners can generate arm motion sequences in time for uplink windows. We applied the RRT-Connect algorithm to the rover’s robotic arm and tested its performance, and we found that it could be used successfully for rover planning with a few changes. First, we implemented the terrain clearance requirement using a software tool called Proximeter. Second, we simplify the paths computed by RRT-Connect to minimize the number of viapoints in a motion plan. Third, we leverage Proximeter to reduce the number of expensive terrain collision checks done by the algorithm, speeding up motion planning.

关键词： Space vehicles Mars Costs Software algorithms Manipulators Hardware Planning

Shift from Texture-bias to Shape-bias: Edge Deformation-based Augmentation for Robust Object Recognition

学校读者我要写书评

暂无评论

Shift from Texture-bias to Shape-bias: Edge Deformation-base...

International Conference on computer Vision (ICCV)

作者： Xilin He Qinliang Lin Cheng Luo Weicheng Xie Siyang Song Feng Liu Linlin Shen Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Shenzhen Institue of Artificial Intelligence & Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing University of Leicester

Recent studies have shown the vulnerability of CNNs under perturbation noises, which is partially caused by the reason that the well-trained CNNs are too biased toward the object texture, i.e., they make predictions mainly based on texture cues. To reduce this texture-bias, current studies resort to learning augmented samples with heavily perturbed texture to make networks be more biased toward relatively stable shape cues. However, such methods usually fail to achieve real shape-biased networks due to the insufficient diversity of the shape cues. In this paper, we propose to augment the training dataset by generating semantically meaningful shapes and samples, via a shape deformation-based online augmentation, namely as SDbOA. The samples generated by our SDbOA have two main merits. First, the augmented samples with more diverse shape variations enable networks to learn the shape cues more elaborately, which encourages the network to be shape-biased. Second, semantic-meaningful shape-augmentation samples could be produced by jointly regularizing the generator with object texture and edge-guidance soft constraint, where the edges are represented more robustly with a self information guided map to better against the noises on them. Extensive experiments under various perturbation noises demonstrate the obvious superiority of our shape-bias-motivated model over the state of the arts in terms of robustness performance. Code is available at https://***/C0notSilly/-ICCV-23-Edge-Deformation-based-Online-Augmentation.

关键词：

Graph Neural Networks for Human-Aware Social Navigation 21st

学校读者我要写书评

暂无评论

Graph Neural Networks for Human-Aware Social Navigation

21st International Workshop of Physical Agents, WAF 2020

作者： Manso, Luis J. Jorvekar, Ronit R. Faria, Diego R. Bustos, Pablo Bachiller, Pilar Department of Computer Science College of Engineering and Physical Sciences Aston University Birmingham United Kingdom Department of Computer Engineering Pune Institute of Computer Technology Pune India Robotics and Artificial Vision Laboratory University of Extremadura Badajoz Spain

ISBN: (纸本)9783030625788

Autonomous navigation is a key skill for assistive and service robots. To be successful, robots have to comply with social rules, such as avoiding the personal spaces of the people surrounding them, or not getting in the way of human-to-human and human-to-object interactions. This paper suggests using Graph Neural Networks to model how inconvenient the presence of a robot would be in a particular scenario according to learned human conventions so that it can be used by path planning algorithms. To do so, we propose two automated scenario-to-graph transformations and benchmark them with different Graph Neural Networks using the SocNav1 dataset [1]. We achieve close-to-human performance in the dataset and argue that, in addition to its promising results, the main advantage of the approach is its scalability in terms of the number of social factors that can be considered and easily embedded in code in comparison with model-based approaches. The code used to train and test the resulting graph neural network is available in a public repository. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Human robot interaction

Design, Dynamic Modeling and Control of a 2-DOF Robotic Wrist Actuated by Twisted and Coiled Actuators

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Zhang, Yunsong Zhou, Xinyu Zhang, Feitian Department of Advanced Manufacturing and Robotics College of Engineering State Key Laboratory of Turbulence and Complex Systems Peking University Beijing100871 China Department of Electrical and Computer Engineering Michigan State University East LansingMI48824 United States

Robotic wrists play a pivotal role in the functionality of industrial manipulators and humanoid robots, facilitating manipulation and grasping tasks. In recent years, there has been a growing interest in integrating artificial muscle-driven actuators for robotic wrists, driven by advancements in technology offering high energy density, lightweight construction, and compact designs. However, in the study of robotic wrists driven by artificial muscles, dynamic model-based controllers are often overlooked, despite their critical importance for motion analysis and dynamic control of robots. This paper presents a novel design of a two-degree-of-freedom (2-DOF) robotic wrist driven by twisted and coiled actuators (TCA) utilizing a parallel mechanism with a 3RRRR configuration. The proposed robotic wrist is expected to feature lightweight structures and superior motion performance while mitigating friction issues. The Lagrangian dynamic model of the wrist is established, along with a nonlinear model predictive controller (NMPC) designed for trajectory tracking tasks. A prototype of the robotic wrist is developed, and extensive experiments are conducted to validate its superior motion performance and the proposed dynamic model. Subsequently, extensive comparative experiments between NMPC and PID controller were conducted under various operating conditions. The experimental results demonstrate the effectiveness and robustness of the dynamic model-based controller in the motion control of TCA-driven robotic wrists. Copyright © 2025, The Authors. All rights reserved.

关键词： Industrial manipulators

CLUE-AI: A Convolutional Three-stream Anomaly Identification Framework for Robot Manipulation

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Altan, Dogan Sariel, Sanem Artificial Intelligence and Robotics Laboratory Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey

Robot safety has been a prominent research topic in recent years since robots are more involved in daily tasks. It is crucial to devise the required safety mechanisms to enable service robots to be aware of and react to anomalies (i.e., unexpected deviations from intended outcomes) that arise during the execution of these tasks. Detection and identification of these anomalies is an essential step towards fulfilling these requirements. Although several architectures are proposed for anomaly detection, identification is not yet thoroughly investigated. This task is challenging since indicators may appear long before anomalies are detected. In this paper, we propose a ConvoLUtional threE-stream Anomaly Identification (CLUE-AI) framework to address this problem. The framework fuses visual, auditory and proprioceptive data streams to identify everyday object manipulation anomalies. A stream of 2D images gathered through an RGB-D camera placed on the head of the robot is processed within a self-attention enabled visual stage to capture visual anomaly indicators. The auditory modality provided by the microphone placed on the robot's lower torso is processed within a designed convolutional neural network (CNN) in the auditory stage. Last, the force applied by the gripper and the gripper state are processed within a CNN to obtain proprioceptive features. These outputs are then combined with a late fusion scheme. Our novel three-stream framework design is analyzed on everyday object manipulation tasks with a Baxter humanoid robot in a semi-structured setting. The results indicate that the framework achieves an f-score of 94% outperforming the other baselines in classifying anomalies that arise during runtime. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

Distributed Inverse Dynamics Control for Quadruped Robots using Geometric Optimization

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Khandelwal, Nimesh Manu, Amritanshu Gupta, Shakti S. Kothari, Mangal Krishnamurthy, Prashanth Khorrami, Farshad Deptartment of Mechanical Engineering IIT Kanpur UP Kanpur India Department of Electrical and Computer Engineering New York University BrooklynNY United States Mobile Robotics Laboratory Department of Mechanical Engineering IIT Kanpur UP Kanpur208016 India

This paper presents a distributed inverse dynamics controller (DIDC) for quadruped robots that addresses the limitations of existing reactive controllers: simplified dynamical models, the inability to handle exact friction cone constraints, and the high computational requirements of whole-body controllers. Current methods either ignore friction constraints entirely or use linear approximations, leading to potential slip and instability, while comprehensive whole-body controllers demand significant computational resources. Our approach uses full rigid-body dynamics and enforces exact friction cone constraints through a novel geometric optimization-based solver. DIDC combines the required generalized forces corresponding to the actuated and unactuated spaces by projecting them onto the actuated space while satisfying the physical constraints and maintaining orthogonality between the base and joint tracking objectives. Experimental validation shows that our approach reduces foot slippage, improves orientation tracking, and converges at least two times faster than existing reactive controllers with generic QP-based implementations. The controller enables stable omnidirectional trotting at various speeds and consumes less power than comparable methods while running efficiently on embedded processors. © 2024, CC BY-SA.

关键词： Multipurpose robots

An Efficient Model-Based Approach on Learning Agile Motor Skills without Reinforcement

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Shi, Haojie Li, Tingguang Zhu, Qingxu Sheng, Jiapeng Han, Lei Meng, Max Q.-H. Tencent Robotics X China The Chinese University of Hong Kong Hong Kong Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China The Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong The Department of Electrical and Computer Engineering The University of Alberta Canada

Learning-based methods have improved locomotion skills of quadruped robots through deep reinforcement learning. However, the sim-to-real gap and low sample efficiency still limit the skill transfer. To address this issue, we propose an efficient model-based learning framework that combines a world model with a policy network. We train a differentiable world model to predict future states and use it to directly supervise a Variational Autoencoder (VAE)-based policy network to imitate real animal behaviors. This significantly reduces the need for real interaction data and allows for rapid policy updates. We also develop a high-level network to track diverse commands and trajectories. Our simulated results show a tenfold sample efficiency increase compared to reinforcement learning methods such as PPO. In real-world testing, our policy achieves proficient command-following performance with only a two-minute data collection period and generalizes well to new speeds and paths. Copyright © 2024, The Authors. All rights reserved.

关键词： Efficiency

Benchmarking Graph Representations and Graph Neural Networks for Multivariate Time Series Classification

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Yang, Wennuo Wu, Shiling Zhou, Yuzhi Luo, Cheng He, Xilin Xie, Weicheng Shen, Linlin Song, Siyang Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China Guangdong Provincial Key Laboratory of Intelligent Information Processing China HBUG Lab University of Exeter United Kingdom

Multivariate Time Series Classification (MTSC) enables the analysis if complex temporal data, and thus serves as a cornerstone in various real-world applications, ranging from healthcare to finance. Since the relationship among variables in MTS usually contain crucial cues, a large number of graph-based MTSC approaches have been proposed, as the graph topology and edges can explicitly represent relationships among variables (channels), where not only various MTS graph representation learning strategies but also different Graph Neural Networks (GNNs) have been explored. Despite such progresses, there is no comprehensive study that fairly benchmarks and investigates the performances of existing widely-used graph representation learning strategies/GNN classifiers in the application of different MTSC tasks. In this paper, we present the first benchmark which systematically investigates the effectiveness of the widely-used three node feature definition strategies, four edge feature learning strategies and five GNN architecture, resulting in 60 different variants for graph-based MTSC. These variants are developed and evaluated with a standardized data pipeline and training/validation/testing strategy on 26 widely-used suspensor MTSC datasets. Our experiments highlight that node features significantly influence MTSC performance, while the visualization of edge features illustrates why adaptive edge learning outperforms other edge feature learning methods. The code of the proposed benchmark is publicly available at https://***/CVI-yangwn/*** Codes 68T10 © 2025, CC BY.

关键词： Graph neural networks