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Soft Robot Kinematic Control Via Manipulability-Aware Redundancy Resolution

Journal of Mechanisms and robotics 2025年第7期17卷 071013页

作者： Azizkhani, Milad Ha, Junhyoung Gunderman, Anthony L. Chen, Yue Department of Mechanical Engineering Georgia Institute of Technology Atlanta GA 30332 Center for Healthcare Robotics Artificial Intelligence and Robotics Institute Korea Institute of Science and Technology Seoul 02792 South Korea Department of Mechanical Engineering University of Arkansas Fayetteville AR 72701 Department of Biomedical Engineering Georgia Institute of Technology Atlanta GA 30332

This article addresses the kinematic control of a redundant soft robotic arm. Full pose kinematic control of soft robots is challenging because direct application of the classical controllers developed based on rigid robots to soft robots could lead to unreliable or infeasible motions. In this study, we explore the manipulability property of a soft robotic arm and develop an advanced resolved-rate controller that prioritizes position over orientation control and switches its modes and gains based on position and orientation manipulabilities, enabling stable motion even when the robot is close to the singular configurations. The simulation and experimental results indicate that our proposed method outperforms previous methods in terms of both accuracy and smoothness during operation.

关键词： Resolution (Optics) Kinematics bio-inspired Simulation Errors Redundancy (Engineering) continuum robotics Robots soft robotics Soft robots robotics kinematics Algorithms

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An Anti-Swing Controller for Aerial Transportation System with Flexible Suspending Rope 5

An Anti-Swing Controller for Aerial Transportation System wi...

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5th Asian Conference on artificial intelligence Technology, ACAIT 2021

作者： Zhang, Zhaopeng Liang, Xiao Zhang, Zhuang Han, Jianda College of Artificial Intelligence Nankai University Institute of Robotics and Automatic Information System Tianjin China

ISBN: (纸本)9781665426305

Numerous researches on aerial transportation uses the assumption that the payload is connected via a rigid rope. Although this assumption makes sense in most situations, it will still lose effectiveness occasionally. Therefore, this paper considers the dynamics and control of a system with flexible rope. Based on assumed mode method(AMM) and the assumption that the length of the rope remains unchanged, the dynamic model of this system is established by Euler-Lagrange techniques. Subsequently, through the energy-based analysis method, we design a nonlinear anti-swing controller. Asymptotic results are obtained with rigorous theoretical derivations provided by the Lyapunov-based stability analysis and LaSalle's invariance theorem. Two groups of simulation results are provided to demonstrate the superior performance of the proposed controller. © 2021 IEEE.

关键词： Controllers

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CoreNet: Conflict Resolution Network for Point-Pixel Misalignment and Sub-Task Suppression of 3D LiDAR-Camera Object Detection

arXiv

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arXiv 2025年

作者： Li, Yiheng Yang, Yang Lei, Zhen Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences 999077 Hong Kong

Fusing multi-modality inputs from different sensors is an effective way to improve the performance of 3D object detection. However, current methods overlook two important conflicts: point-pixel misalignment and sub-task suppression. The former means a pixel feature from the opaque object is projected to multiple point features of the same ray in the world space, and the latter means the classification prediction and bounding box regression may cause mutual suppression. In this paper, we propose a novel method named Conflict Resolution Network (CoreNet) to address the aforementioned issues. Specifically, we first propose a dual-stream transformation module to tackle point-pixel misalignment. It consists of ray-based and point-based 2D-to-BEV transformations. Both of them achieve approximately unique mapping from the image space to the world space. Moreover, we introduce a task-specific predictor to tackle sub-task suppression. It uses the dual-branch structure which adopts class-specific query and Bbox-specific query to corresponding sub-tasks. Each task-specific query is constructed of task-specific feature and general feature, which allows the heads to adaptively select information of interest based on different sub-tasks. Experiments on the large-scale nuScenes dataset demonstrate the superiority of our proposed CoreNet, by achieving 75.6% NDS and 73.3% mAP on the nuScenes test set without test-time augmentation and model ensemble techniques. The ample ablation study also demonstrates the effectiveness of each component. The code is released on https://***/liyih/CoreNet. Copyright © 2025, The Authors. All rights reserved.

关键词： Object detection

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Ultra-wideband Time Difference of Arrival Indoor Localization: From Sensor Placement to System Evaluation

arXiv

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arXiv 2024年

作者： Zhao, Wenda Goudar, Abhishek Tang, Mingliang Schoellig, Angela P. University of Toronto Institute for Aerospace Studies Vector Institute for Artificial Intelligence Canada Technische Universität München Munich Institute for Robotics and Machine Intelligence Germany University of California Berkeley United States

Wireless indoor localization has attracted significant research interest due to its high accuracy, low cost, lightweight design, and low power consumption. Specifically, ultra-wideband (UWB) time difference of arrival (TDOA)-based localization has emerged as a scalable positioning solution for mobile robots, consumer electronics, and wearable devices, featuring good accuracy and reliability. While UWB TDOA-based localization systems rely on the deployment of UWB radio sensors as positioning landmarks, existing works often assume these placements are predetermined or study the sensor placement problem alone without evaluating it in practical scenarios. In this article, we bridge this gap by approaching the UWB TDOA localization from a system-level perspective, integrating sensor placement as a key component and conducting practical evaluation in real-world scenarios. Through extensive real-world experiments, we demonstrate the accuracy and robustness of our localization system, comparing its performance to the theoretical lower bounds. Using a challenging multi-room environment as a case study, we illustrate the full system construction process, from sensor placement optimization to real-world deployment. Our evaluation, comprising a cumulative total of 39 minutes of real-world experiments involving up to five agents and covering 2608 meters across four distinct scenarios, provides valuable insights and guidelines for constructing UWB TDOA localization systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Ultra-wideband (UWB)

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Leveraging GNSS and Onboard Visual Data from Consumer Vehicles for Robust Road Network Estimation

Leveraging GNSS and Onboard Visual Data from Consumer Vehicl...

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

作者： Balázs Opra Betty Le Dem Jeffrey M. Walls Dimitar Lukarski Cyrill Stachniss Woven by Toyota Inc University of Bonn Germany Center for Robotics University of Bonn Lamarr Institute for Machine Learning and Artificial Intelligence Germany

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Maps are essential for diverse applications, such as vehicle navigation and autonomous robotics. Both require spatial models for effective route planning and localization. This paper addresses the challenge of road graph construction for autonomous vehicles. Despite recent advances, creating a road graph remains labor-intensive and has yet to achieve full automation. The goal of this paper is to generate such graphs automatically and accurately. Modern cars are equipped with onboard sensors used for today's advanced driver assistance systems like lane keeping. We propose using global navigation satellite system (GNSS) traces and basic image data acquired from these standard sensors in consumer vehicles to estimate road-level maps with minimal effort. We exploit the spatial information in the data by framing the problem as a road centerline semantic segmentation task using a convolutional neural network. We also utilize the data’s time series nature to refine the neural network’s output by using map matching. We implemented and evaluated our method using a fleet of real consumer vehicles, only using the deployed onboard sensors. Our evaluation demonstrates that our approach not only matches existing methods on simpler road configurations but also significantly outperforms them on more complex road geometries and topologies. This work received the 2023 Woven by Toyota Invention Award.

关键词： Global navigation satellite system Technological innovation Visualization Accuracy Roads Weaving Sensor systems Sensors Automobiles Vehicles

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Boosting Semi-Supervised Temporal Action Localization by Learning from Non-Target Classes

arXiv

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arXiv 2024年

作者： Xia, Kun Wang, Le Zhou, Sanping Hua, Gang Tang, Wei Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University China Wormpex AI Research China University of Illinois Chicago United States

The crux of semi-supervised temporal action localization (SS-TAL) lies in excavating valuable information from abundant unlabeled videos. However, current approaches predominantly focus on building models that are robust to the error-prone target class (i.e., the predicted class with the highest confidence) while ignoring informative semantics within non-target classes. This paper approaches SS-TAL from a novel perspective by advocating for learning from non-target classes, transcending the conventional focus solely on the target class. The proposed approach involves partitioning the label space of the predicted class distribution into distinct subspaces: target class, positive classes, negative classes, and ambiguous classes, aiming to mine both positive and negative semantics that are absent in the target class, while excluding ambiguous classes. To this end, we first devise innovative strategies to adaptively select high-quality positive and negative classes from the label space, by modeling both the confidence and rank of a class in relation to those of the target class. Then, we introduce novel positive and negative losses designed to guide the learning process, pushing predictions closer to positive classes and away from negative classes. Finally, the positive and negative processes are integrated into a hybrid positive-negative learning framework, facilitating the utilization of non-target classes in both labeled and unlabeled videos. Experimental results on THUMOS14 and ActivityNet v1.3 demonstrate the superiority of the proposed method over prior state-of-the-art approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Building Vision Transformers with Hierarchy Aware Feature Aggregation

Building Vision Transformers with Hierarchy Aware Feature Ag...

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International Conference on Computer Vision (ICCV)

作者： Yongjie Chen Hongmin Liu Haoran Yin Bin Fan School of Intelligence Science and Technology University of Science and Technology Beijing Institute of Artificial Intelligence University of Science and Technology Beijing Horizon Robotics

Thanks to the excellent global modeling capability of attention mechanisms, the Vision Transformer has achieved better results than ConvNet in many computer tasks. However, in generating hierarchical feature maps, the Transformer still adopts the ConvNet feature aggregation scheme. This leads to the problem that the semantic information of the grid area of image becomes confused after feature aggregation, making it difficult for attention to accurately model global relationships. To address this, we propose the Hierarchy Aware Feature Aggregation framework (HAFA). HAFA enhances the extraction of local features adaptively in shallow layers where semantic information is weak, while is able to aggregate patches with similar semantics in deep layers. The clear semantic information of the aggregated patches, enables the attention mechanism to more accurately model global information at the semantic level. Extensive experiments show that after using the HAFA framework, significant improvements have been achieved relative to the baseline models in image classification, object detection, and semantic segmentation tasks.

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Embedded 3D Printing of Silicone for Soft Actuator with Stiffness Gradient and Programmable Workspace

Embedded 3D Printing of Silicone for Soft Actuator with Stif...

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

作者： Fei Xiao Zhuoheng Wei Hao Wang Jisen Li Jian Zhu School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Soft pneumatic actuators can accomplish various customizable deformation/motion through the distribution of cavities and gradients in stiffness. However, traditional manufacturing methods, say molding, struggle to produce soft actuators with both complex cavities and desirable stiffness distributions. Regular 3D printing methods usually need extra printheads for support materials to fabricate soft actuators with cavities. In addition, the printing quality and fidelity of the whole structure cannot be uniform due to the effect of gravity, especially for a soft actuator with overhang features. To fabricate a soft actuator of uniform fidelity but desirable stiffness distributions, we propose an embedded 3D printing approach with only one active mixing printhead. By adjusting the mixing ratio of the dual-component silicone, we can achieve designated stiffness gradients, ranging from 30.2 kPa to 198 kPa. With this approach, we successfully fabricate soft pneumatic actuators with overhang features, which exhibit programmable elongation and radial expansion. Additionally, we fabricate soft bending actuators which can achieve programmable workspaces due to their predetermined stiffness distribution.

关键词： Pneumatic actuators Shape Switches Production Soft robotics Bending Pneumatic systems Three-dimensional printing Real-time systems Elongation

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Ball sition ature bedded Group Activity Recognition del for Team Sport Games 4

Ball sition ature bedded Group Activity Recognition del for ...

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4th International Conference on Machine Learning and Intelligent Systems, MLIS 2022

作者： Jamsrandorj, Ankhzaya Chao, Vanyi Oo, Yin May Mun, Kyung-Ryoul Kim, Jinwook Department of Human Computer Interface & Robotics Engineering University of Science & Technology Korea Republic of Department of Ai Robotics University of Science & Technology Korea Republic of Center for Artificial Intelligence Korea Institute of Science and Technology Korea Republic of

ISBN: (纸本)9781643683560

Most group activity recognition models focus mainly on spatio-temporal features from the players in sports games. Often they do not pay enough attention to the game object, which heavily affects not only individual action but also a group activity. We propose a new group activity recognition model for sports games that incorporates players' motion information and game object positional information. The proposed method uses a transformer encoder for temporal feature extraction and a 'simple' conventional convolutional neural network for extracting spatial features and fusing them with the relative ball position-embedded features. The experimental results show that our model achieved comparable results to state-of-the-art methods on the Volleyball dataset by using only one transformer encoder block and the ball position. © 2022 The authors and IOS Press.

关键词： Sports

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DTLM-DBP:Deep Transfer Learning Models for DNA Binding Proteins Identification

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Computers, Materials & Continua 2021年第9期68卷 3563-3576页

作者： Sara Saber Uswah Khairuddin Rubiyah Yusof Ahmed Madani Department of Computer Engineering Faculty of EngineeringArab Academy of Science and TechnologyEgypt Centre for Artificial Intelligence&Robotics Malaysia-Japan International Institute of TechnologyUniversiti Teknologi Malaysia

The identification of DNA binding proteins(DNABPs)is considered a major challenge in genome annotation because they are linked to several important applied and research applications of cellular functions e.g.,in the study of the biological,biophysical,and biochemical effects of antibiotics,drugs,and steroids on *** paper presents an efficient approach for DNABPs identification based on deep transfer learning,named“DTLM-DBP.”Two transfer learning methods are used in the identification *** first is based on the pre-trained deep learning model as a feature’s extractor and *** different pre-trained Convolutional Neural Networks(CNN),AlexNet 8 and VGG 16,are tested and *** second method uses the deep learning model as a feature’s extractor only and two different classifiers for the identification *** classifiers,Support Vector Machine(SVM)and Random Forest(RF),are tested and *** proposed approach is tested using different DNA proteins *** performance of the identification process is evaluated in terms of identification accuracy,sensitivity,specificity and MCC,with four available DNA proteins datasets:PDB1075,PDB186,PDNA-543,and *** results show that the RF classifier,with VGG-Net pre-trained deep transfer learning features,gives the highest ***-DBP was compared with other published methods and it provides a considerable improvement in the performance of DNABPs identification.

关键词： DNABPs deep transfer learning AlexNet 8 VGG 16 SVM RF

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