Robotica最新文献

英文中文

FabricFolding: learning efficient fabric folding without expert demonstrations 织物折叠：无需专家示范即可学习高效织物折叠

IF 2.7 4区计算机科学 Q1 Mathematics

Robotica

Pub Date : 2024-03-04 DOI: 10.1017/s0263574724000250

Can He, Lingxiao Meng, Zhirui Sun, Jiankun Wang, Max Q.-H. Meng

Autonomous fabric manipulation is a challenging task due to complex dynamics and potential self-occlusion during fabric handling. An intuitive method of fabric-folding manipulation first involves obtaining a smooth and unfolded fabric configuration before the folding process begins. However, the combination of quasi-static actions like pick & place and dynamic action like fling proves inadequate in effectively unfolding long-sleeved T-shirts with sleeves mostly tucked inside the garment. To address this limitation, this paper introduces an enhanced quasi-static action called pick & drag, specifically designed to handle this type of fabric configuration. Additionally, an efficient dual-arm manipulation system is designed in this paper, which combines quasi-static (including pick & place and pick & drag) and dynamic fling actions to flexibly manipulate fabrics into unfolded and smooth configurations. Subsequently, once it is confirmed that the fabric is sufficiently unfolded and all fabric keypoints are detected, the keypoint-based heuristic folding algorithm is employed for the fabric-folding process. To address the scarcity of publicly available keypoint detection datasets for real fabric, we gathered images of various fabric configurations and types in real scenes to create a comprehensive keypoint dataset for fabric folding. This dataset aims to enhance the success rate of keypoint detection. Moreover, we evaluate the effectiveness of our proposed system in real-world settings, where it consistently and reliably unfolds and folds various types of fabrics, including challenging situations such as long-sleeved T-shirts with most parts of sleeves tucked inside the garment. Specifically, our method achieves a coverage rate of 0.822 and a success rate of 0.88 for long-sleeved T-shirts folding. Supplemental materials and dataset are available on our project webpage at https://sites.google.com/view/fabricfolding.

由于在织物处理过程中存在复杂的动力学和潜在的自闭性，自主织物操作是一项具有挑战性的任务。织物折叠操作的直观方法首先是在折叠过程开始前获得平滑和展开的织物配置。然而，事实证明，将取放等准静态动作和甩动等动态动作结合起来，不足以有效地展开袖子大多塞在衣服里面的长袖 T 恤。为了解决这一局限性，本文引入了一种增强型准静态动作，即 "拾放拖"，专门用于处理这种类型的织物配置。此外，本文还设计了一种高效的双臂操纵系统，该系统结合了准静态动作（包括取放和拾取拖拽）和动态甩动动作，可灵活地将织物操纵成展开和平滑的配置。随后，在确认织物已充分展开并检测到所有织物关键点后，就会采用基于关键点的启发式折叠算法进行织物折叠处理。为了解决真实织物公开关键点检测数据集稀缺的问题，我们收集了真实场景中各种织物配置和类型的图像，创建了一个全面的织物折叠关键点数据集。该数据集旨在提高关键点检测的成功率。此外，我们还评估了我们提出的系统在真实世界环境中的有效性，该系统可以稳定可靠地展开和折叠各种类型的织物，包括具有挑战性的情况，例如长袖 T 恤衫的大部分袖子都塞进了衣服里面。具体来说，我们的方法在长袖 T 恤衫的折叠方面达到了 0.822 的覆盖率和 0.88 的成功率。补充材料和数据集可在我们的项目网页 https://sites.google.com/view/fabricfolding 上查阅。

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引用次数: 0

Design and motion mechanism analysis of screw-driven in-pipe inspection robot based on novel adapting mechanism 基于新型自适应机构的螺杆驱动管道内检测机器人的设计与运动机构分析

IF 2.7 4区计算机科学 Q1 Mathematics

Robotica

Pub Date : 2024-03-04 DOI: 10.1017/s0263574724000316

Jihua Yin, Xuemei Liu, Youqiang Wang, Yucheng Wang

In the pipeline industry, it is often necessary to monitor cracks and damage in pipelines, or need to clean the inside of the pipeline regularly, or collect adhesive on the inner wall of the pipe, but the pipe is too narrow and difficult for humans to enter, it is necessary to use a pipe machine to complete the work. In this paper, a newly designed screw-driven in-pipe inspection robot (IPIR) is proposed. Compared with common robots, this robot innovatively designs adapting mechanism. The robot can not only adapt to the change of the inner diameter size of the pipeline by using the bionic principle and the deformation characteristics of flexible components but also can pass smoothly in the horizontal/oblique/vertical pipelines and has a certain ability to cross obstacles. In addition, it can transmit images of the inner wall of the pipeline wirelessly for data analysis. Finally, through theoretical analysis and prototype construction, the performance of the robot is verified. The results show that the prototype robot can not only smoothly pass through the acrylic pipe with inner diameter of 120–138 mm but also pass through boss with a height of 3 mm.

在管道行业中，经常需要监测管道的裂缝和损坏情况，或需要定期清洁管道内部，或收集管道内壁上的粘合剂，但管道过于狭窄，人类难以进入，这就需要使用管道机械来完成工作。本文提出了一种全新设计的螺杆驱动管道内检测机器人（IPIR）。与普通机器人相比，该机器人创新性地设计了自适应机构。利用仿生学原理和柔性部件的变形特性，机器人不仅能适应管道内径尺寸的变化，还能在水平/倾斜/垂直管道中平稳通过，并具有一定的越障能力。此外，它还能无线传输管道内壁的图像，以便进行数据分析。最后，通过理论分析和原型构建，对机器人的性能进行了验证。结果表明，机器人原型不仅能顺利通过内径为 120-138 毫米的丙烯酸管道，还能通过高度为 3 毫米的凸台。

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引用次数: 0

Computer-aided design tool for typical flexible mechanisms synthesis by means of evolutionary algorithms 利用进化算法合成典型柔性机构的计算机辅助设计工具

IF 2.7 4区计算机科学 Q1 Mathematics

Robotica

Pub Date : 2024-03-04 DOI: 10.1017/s0263574724000171

Mohamed Amine Ben Abdallah, Imed Khemili, Nizar Aifaoui, Med Amine Laribi

Accurate prediction for mechanisms’ dynamic responses has always been a challenging task for designers. For modeling easiness purposes, mechanisms’ synthesis and optimization have been mostly limited to rigid systems, making consequently the designer unable to vow that the manufactured mechanism satisfies the target responses. To address this limitation, flexible mechanism synthesis is aimed in this work. Two benchmark mechanisms being the core of myriad mechanical devices are of scope, mainly, the flexible slider-crank and the four-bar. In addition to the mechanism dimensions, materials properties have been embedded in the synthesis problem. Two responses are of interest for the slider-crank mechanism, the slider velocity, and the midpoint axial displacement for the flexible connecting rod. Whereas five responses have been compiled for the four-bar mechanism synthesis. A comparative analysis of seven optimization techniques to solve the synthesis problem for both mechanisms has been performed. Subsequently, an executable computer-aided design tool for mechanisms synthesis has been developed under MATLAB®. Numerical outcomes emphasize the limits of a single-response-based synthesis for a flexible mechanism. It has been proven that combining different responses alleviates possible error and fulfill high-accuracy requirement.

对于设计人员来说，准确预测机构的动态响应一直是一项具有挑战性的任务。出于建模简便性的考虑，机构的合成和优化大多局限于刚性系统，因此设计人员无法保证制造出的机构满足目标响应。为了解决这一局限性，本研究以柔性机构合成为目标。作为无数机械装置核心的两个基准机构是研究的范围，主要是柔性滑块-曲柄和四杆。除机构尺寸外，合成问题还包括材料属性。滑块曲柄机构有两个响应，即滑块速度和柔性连杆的中点轴向位移。而四杆机构的合成则有五个响应。对解决两种机构合成问题的七种优化技术进行了比较分析。随后，在 MATLAB® 下开发了用于机构合成的可执行计算机辅助设计工具。数值结果强调了基于单一响应的柔性机构合成的局限性。事实证明，将不同的响应结合起来可以减少可能出现的误差，并满足高精度要求。

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引用次数: 0

Outdoor LiDAR-inertial SLAM using ground constraints 利用地面约束的室外激光雷达-惯性 SLAM

IF 2.7 4区计算机科学 Q1 Mathematics

Robotica

Pub Date : 2024-02-26 DOI: 10.1017/s0263574724000237

Yating Hu, Qigao Zhou, Zhejun Miao, Hang Yuan, Shuang Liu

The current LiDAR-inertial odometry is prone to cumulative Z-axis error when it runs for a long time. This error can easily lead to the failure to detect the loop-closing in the correct scenario. In this paper, a ground-constrained LiDAR-inertial SLAM is proposed to solve this problem. Reasonable constraints on the ground motion of the mobile robot are incorporated to limit the Z-axis drift error. At the same time, considering the influence of initial positioning error on navigation, a keyframe selection strategy is designed to effectively improve the flatness and accuracy of positioning and the efficiency of loop detection. If GNSS is available, the GNSS factor is added to eliminate the cumulative error of the trajectory. Finally, a large number of experiments are carried out on the self-developed robot platform to verify the effectiveness of the algorithm. The results show that this method can effectively improve location accuracy in outdoor environments, especially in environments of feature degradation and large scale.

目前的激光雷达-惯性里程计在长时间运行时容易产生累积 Z 轴误差。这种误差很容易导致无法在正确的情况下检测到闭环。本文提出了一种地面约束激光雷达-惯性 SLAM 来解决这一问题。本文对移动机器人的地面运动进行了合理的约束，以限制 Z 轴漂移误差。同时，考虑到初始定位误差对导航的影响，设计了一种关键帧选择策略，以有效提高定位的平整度和精度以及环路检测的效率。如果有 GNSS，则加入 GNSS 因子以消除轨迹的累积误差。最后，在自主研发的机器人平台上进行了大量实验，以验证算法的有效性。结果表明，该方法能有效提高室外环境下的定位精度，尤其是在特征退化和大尺度环境下。

引用次数: 0

Safe and socially compliant robot navigation in crowds with fast-moving pedestrians via deep reinforcement learning 通过深度强化学习，在有快速移动行人的人群中实现安全且符合社会要求的机器人导航

IF 2.7 4区计算机科学 Q1 Mathematics

Robotica

Pub Date : 2024-02-26 DOI: 10.1017/s0263574724000183

Zhen Feng, Bingxin Xue, Chaoqun Wang, Fengyu Zhou

Safe and socially compliant navigation in a crowded environment is essential for social robots. Numerous research efforts have shown the advantages of deep reinforcement learning techniques in training efficient policies, while most of them ignore fast-moving pedestrians in the crowd. In this paper, we present a novel design of safety measure, named Risk-Area, considering collision theory and motion characteristics of different robots and humans. The geometry of Risk-Area is formed based on the real-time relative positions and velocities of the agents in the environment. Our approach perceives risk in the environment and encourages the robot to take safe and socially compliant navigation behaviors. The proposed method is verified with three existing well-known deep reinforcement learning models in densely populated environments. Experiment results demonstrate that our approach combined with the reinforcement learning techniques can efficiently perceive risk in the environment and navigate the robot with high safety in the crowds with fast-moving pedestrians.

在拥挤的环境中进行安全且符合社交规则的导航对于社交机器人来说至关重要。大量研究表明，深度强化学习技术在训练高效策略方面具有优势，但大多数研究都忽略了人群中快速移动的行人。在本文中，我们考虑到碰撞理论以及不同机器人和人类的运动特性，提出了一种新颖的安全措施设计，命名为 "风险区域"。风险区域的几何形状是根据环境中各代理的实时相对位置和速度形成的。我们的方法能感知环境中的风险，并鼓励机器人采取安全且符合社会要求的导航行为。在人口稠密的环境中，提出的方法与现有的三个著名深度强化学习模型进行了验证。实验结果表明，我们的方法与强化学习技术相结合，可以有效地感知环境中的风险，并在有快速移动行人的人群中为机器人提供高安全性导航。

引用次数: 0

Vision-based food handling system for high-resemblance random food items 基于视觉的食品处理系统，用于处理高重现性随机食品

IF 2.7 4区计算机科学 Q1 Mathematics

Robotica

Pub Date : 2024-02-21 DOI: 10.1017/s0263574724000122

Yadan Zeng, Yee Seng Teoh, Guoniu Zhu, Elvin Toh, I-Ming Chen

The rise in the number of automated robotic kitchens accelerated the need for advanced food handling system, emphasizing food analysis including ingredient classification pose recognition and assembling strategy. Selecting the optimal piece from a pile of similarly shaped food items is a challenge to automated meal assembling system. To address this, we present a constructive assembling algorithm, introducing a unique approach for food pose detection–Fast Image to Pose Detection (FI2PD), and a closed-loop packing strategy. Powered by a convolutional neural network (CNN) and a pose retrieval model, FI2PD is adept at constructing a 6D pose from only RGB images. The method employs a coarse-to-fine approach, leveraging the CNN to pinpoint object orientation and position, alongside a pose retrieval process for target selection and 6D pose derivation. Our closed-loop packing strategy, aided by the Item Arrangement Verifier, ensures precise arrangement and system robustness. Additionally, we introduce our FdIngred328 dataset of nine food categories ranging from fake foods to real foods, and the automatically generated data based on synthetic techniques. The performance of our method for object recognition and pose detection has been demonstrated to achieve a success rate of 97.9%. Impressively, the integration of a closed-loop strategy into our meal-assembly process resulted in a notable success rate of 90%, outperforming the results of systems lacking the closed-loop mechanism.

自动化机器人厨房数量的增加加速了对先进食品处理系统的需求，强调食品分析，包括配料分类姿势识别和装配策略。从一堆形状相似的食品中挑选出最佳部件是自动配餐系统面临的一项挑战。为此，我们提出了一种建设性的组装算法，引入了一种独特的食品姿态检测方法--快速图像姿态检测（FI2PD）和一种闭环包装策略。在卷积神经网络（CNN）和姿势检索模型的支持下，FI2PD 擅长从仅有的 RGB 图像中构建 6D 姿势。该方法采用了从粗到细的方法，利用卷积神经网络精确定位物体的方向和位置，同时利用姿态检索过程进行目标选择和 6D 姿态推导。我们的闭环包装策略在 "物品排列验证器 "的辅助下，确保了精确的排列和系统的鲁棒性。此外，我们还介绍了我们的 FdIngred328 数据集，该数据集包含从假冒食品到真实食品的九个食品类别，以及基于合成技术自动生成的数据。结果表明，我们的方法在物体识别和姿势检测方面的成功率高达 97.9%。令人印象深刻的是，将闭环策略整合到我们的膳食组装流程中后，成功率达到了 90%，明显优于缺乏闭环机制的系统。

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引用次数: 0

Oscillation-free point-to-point motions of planar differentially flat under-actuated robots: a Laplace transform method 平面差动平动下机器人的无振荡点对点运动：拉普拉斯变换方法

IF 2.7 4区计算机科学 Q1 Mathematics

Robotica

Pub Date : 2024-02-20 DOI: 10.1017/s0263574724000249

Michele Tonan, Alberto Doria, Matteo Bottin, Giulio Rosati

Differentially flat under-actuated robots are characterized by more degrees of freedom (DOF) than actuators: this makes possible the design of lightweight cheap robots with high dexterity. The main issue of such robots is the control of the passive joint, which requires accurate dynamic modeling of the robot. Friction is usually discarded to simplify the models, especially in the case of low-speed trajectories. However, this simplification leads to oscillations of the end-effector about the final position, which are incompatible with fast and accurate motions. This paper focuses on planar

$n$

-DOF serial robotic arms with

$n-1$

actuated rotational joints plus one final passive rotational joint with stiffness and friction properties. These robots, if properly balanced, are differentially flat. When the non-actuated joint can be considered frictionless, differentially flat robots can be controlled in open loop, calculating the motor torques demanded by point-to-point motions. This paper extends the open-loop control to robots with a passive joint with viscous friction adopting a Laplace transform method. This method can be adopted by exploiting the particular structure of the equations of motion of differentially flat under-actuated robots in which the last equations are linear. Analytical expressions of the motor torques are obtained. The work is enriched by an experimental validation of a

$2$

-DOF under-actuated robot and by numerical simulations of the

$2$

- and

$4$

-DOF robots showing the suppression of unwanted oscillations.

差分扁平欠驱动机器人的特点是自由度（DOF）大于执行器：这使得设计具有高灵活性的轻型廉价机器人成为可能。这类机器人的主要问题是被动关节的控制，这需要对机器人进行精确的动态建模。为了简化模型，特别是在低速轨迹的情况下，通常会舍弃摩擦力。然而，这种简化会导致末端执行器围绕最终位置产生摆动，这与快速精确的运动不相容。本文的重点是具有 n-1 个致动旋转关节和一个具有刚度和摩擦特性的最终被动旋转关节的 n$ -DOF 平面串行机械臂。如果平衡得当，这些机器人都是不同平面的。当非致动关节可视为无摩擦时，可通过开环计算点对点运动所需的电机扭矩来控制差动平坦机器人。本文采用拉普拉斯变换方法，将开环控制扩展到具有粘性摩擦的被动关节的机器人。这种方法可以利用差动平动下机器人运动方程的特殊结构，其中最后一个方程是线性的。该方法获得了电机扭矩的分析表达式。通过对 2 美元 -DOF 下驱动机器人的实验验证，以及对 2 美元 - 和 4 美元 -DOF 机器人的数值模拟，显示了对不必要振荡的抑制，从而丰富了这一研究成果。

{"title":"Oscillation-free point-to-point motions of planar differentially flat under-actuated robots: a Laplace transform method","authors":"Michele Tonan, Alberto Doria, Matteo Bottin, Giulio Rosati","doi":"10.1017/s0263574724000249","DOIUrl":"https://doi.org/10.1017/s0263574724000249","url":null,"abstract":"Differentially flat under-actuated robots are characterized by more degrees of freedom (DOF) than actuators: this makes possible the design of lightweight cheap robots with high dexterity. The main issue of such robots is the control of the passive joint, which requires accurate dynamic modeling of the robot. Friction is usually discarded to simplify the models, especially in the case of low-speed trajectories. However, this simplification leads to oscillations of the end-effector about the final position, which are incompatible with fast and accurate motions. This paper focuses on planar $n$ -DOF serial robotic arms with $n-1$ actuated rotational joints plus one final passive rotational joint with stiffness and friction properties. These robots, if properly balanced, are differentially flat. When the non-actuated joint can be considered frictionless, differentially flat robots can be controlled in open loop, calculating the motor torques demanded by point-to-point motions. This paper extends the open-loop control to robots with a passive joint with viscous friction adopting a Laplace transform method. This method can be adopted by exploiting the particular structure of the equations of motion of differentially flat under-actuated robots in which the last equations are linear. Analytical expressions of the motor torques are obtained. The work is enriched by an experimental validation of a $2$ -DOF under-actuated robot and by numerical simulations of the $2$ - and $4$ -DOF robots showing the suppression of unwanted oscillations.","PeriodicalId":49593,"journal":{"name":"Robotica","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139927989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ROB volume 42 issue 3 Cover and Front matter ROB 第 42 卷第 3 期封面和封底

IF 2.7 4区计算机科学 Q1 Mathematics

Robotica

Pub Date : 2024-02-13 DOI: 10.1017/s0263574724000201

引用次数: 0

End-to-end deep learning-based framework for path planning and collision checking: bin-picking application 基于端到端深度学习的路径规划和碰撞检查框架：垃圾箱拣选应用

IF 2.7 4区计算机科学 Q1 Mathematics

Robotica

Pub Date : 2024-02-13 DOI: 10.1017/s0263574724000109

Mehran Ghafarian Tamizi, Homayoun Honari, Aleksey Nozdryn-Plotnicki, Homayoun Najjaran

Real-time and efficient path planning is critical for all robotic systems. In particular, it is of greater importance for industrial robots since the overall planning and execution time directly impact the cycle time and automation economics in production lines. While the problem may not be complex in static environments, classical approaches are inefficient in high-dimensional environments in terms of planning time and optimality. Collision checking poses another challenge in obtaining a real-time solution for path planning in complex environments. To address these issues, we propose an end-to-end learning-based framework viz., Path Planning and Collision checking Network (PPCNet). The PPCNet generates the path by computing waypoints sequentially using two networks: the first network generates a waypoint, and the second one determines whether the waypoint is on a collision-free segment of the path. The end-to-end training process is based on imitation learning that uses data aggregation from the experience of an expert planner to train the two networks, simultaneously. We utilize two approaches for training a network that efficiently approximates the exact geometrical collision checking function. Finally, the PPCNet is evaluated in two different simulation environments and a practical implementation on a robotic arm for a bin-picking application. Compared to the state-of-the-art path-planning methods, our results show significant improvement in performance by greatly reducing the planning time with comparable success rates and path lengths.

实时高效的路径规划对所有机器人系统都至关重要。特别是对于工业机器人来说，由于整体规划和执行时间直接影响到生产线的周期时间和自动化经济性，因此它显得更为重要。虽然在静态环境中问题可能并不复杂，但在高维环境中，传统方法在规划时间和最优性方面效率低下。碰撞检查是在复杂环境中获得路径规划实时解决方案的另一个挑战。为了解决这些问题，我们提出了一个基于端到端学习的框架，即路径规划和碰撞检查网络（PPCNet）。PPCNet 通过使用两个网络依次计算航点来生成路径：第一个网络生成一个航点，第二个网络确定该航点是否在路径的无碰撞段上。端到端的训练过程基于模仿学习，利用专家规划师的经验数据汇总来同时训练两个网络。我们采用两种方法训练网络，以有效逼近精确的几何碰撞检查函数。最后，我们在两种不同的模拟环境中对 PPCNet 进行了评估，并在一个用于拣选垃圾的机械臂上进行了实际应用。与最先进的路径规划方法相比，我们的结果表明，在成功率和路径长度相当的情况下，规划时间大大缩短，性能显著提高。

{"title":"End-to-end deep learning-based framework for path planning and collision checking: bin-picking application","authors":"Mehran Ghafarian Tamizi, Homayoun Honari, Aleksey Nozdryn-Plotnicki, Homayoun Najjaran","doi":"10.1017/s0263574724000109","DOIUrl":"https://doi.org/10.1017/s0263574724000109","url":null,"abstract":"Real-time and efficient path planning is critical for all robotic systems. In particular, it is of greater importance for industrial robots since the overall planning and execution time directly impact the cycle time and automation economics in production lines. While the problem may not be complex in static environments, classical approaches are inefficient in high-dimensional environments in terms of planning time and optimality. Collision checking poses another challenge in obtaining a real-time solution for path planning in complex environments. To address these issues, we propose an end-to-end learning-based framework viz., Path Planning and Collision checking Network (PPCNet). The PPCNet generates the path by computing waypoints sequentially using two networks: the first network generates a waypoint, and the second one determines whether the waypoint is on a collision-free segment of the path. The end-to-end training process is based on imitation learning that uses data aggregation from the experience of an expert planner to train the two networks, simultaneously. We utilize two approaches for training a network that efficiently approximates the exact geometrical collision checking function. Finally, the PPCNet is evaluated in two different simulation environments and a practical implementation on a robotic arm for a bin-picking application. Compared to the state-of-the-art path-planning methods, our results show significant improvement in performance by greatly reducing the planning time with comparable success rates and path lengths.","PeriodicalId":49593,"journal":{"name":"Robotica","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139771165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ROB volume 42 issue 3 Cover and Front matter ROB 第 42 卷第 3 期封面和封底

IF 2.7 4区计算机科学 Q1 Mathematics

Robotica

Pub Date : 2024-02-13 DOI: 10.1017/s0263574724000201

引用次数: 0

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