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2022 International Conference on Robotics and Automation (ICRA)最新文献

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Parametric Path Optimization for Wheeled Robots Navigation 轮式机器人导航参数路径优化
Pub Date : 2022-05-23 DOI: 10.1109/icra46639.2022.9812167
Zhiqiang Jian, Songyi Zhang, Jiahui Zhang, Shi-tao Chen, N. Zheng
Collision risk and smoothness are the most important factors in global path planning. Currently, planning methods that reduce global path collision risk and improve its smoothness through numerical optimization have achieved good results. However, these methods cannot always optimize the path. The reason is all points on the path are considered as decision variables, which leads to the high dimensionality of the defined optimization problem. Therefore, we propose a novel global path optimization method. The method characterizes the path as a parametric curve and then optimizes the curve's parameters with a defined objective function, which successfully reduces the dimension of optimization problem. The proposed method is compared with baseline and state-of-the-art methods. Experimental results show the path optimized by our method is not only optimal in collision risk, but also in efficiency and smoothness. Furthermore, the proposed method is also implemented and tested in both simulation and real robots.
碰撞风险和平滑性是全局路径规划中最重要的因素。目前,通过数值优化降低全局路径碰撞风险、提高全局路径平滑度的规划方法已经取得了较好的效果。然而,这些方法不能总是优化路径。原因是路径上的所有点都被视为决策变量,这导致了所定义的优化问题的高维性。因此,我们提出了一种新的全局路径优化方法。该方法将路径描述为参数化曲线,通过定义目标函数对曲线参数进行优化,成功地降低了优化问题的维数。将所提出的方法与基线方法和最先进的方法进行了比较。实验结果表明,该方法优化的路径不仅在碰撞风险上最优,而且在效率和平滑度上也最优。此外,该方法还在仿真和真实机器人中进行了实现和测试。
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引用次数: 1
The Second Generation (G2) Fingertip Sensor for Near-Distance Ranging and Material Sensing in Robotic Grasping* 第二代(G2)指尖传感器在机器人抓取中的近距离测距和材料传感*
Pub Date : 2022-05-23 DOI: 10.1109/icra46639.2022.9811902
Cheng Fang, Di Wang, Dezhen Song, Jun Zou
To continuously improve robotic grasping, we are interested in developing a contactless fingertip-mounted sensor for near-distance ranging and material sensing. Previously, we demonstrated a dual-modal and dual sensing mechanisms (DMDSM) pretouch sensor prototype based on pulse-echo ultrasound and optoacoustics. However, the complex system, the bulky and expensive pulser-receiver, and the omni-directionally sensitive microphone block the sensor from practical applications in real robotic fingers. To address these issues, we report the second generation (G2) DMDSM sensor without the pulser-receiver and microphone, which is made possible by redesigning the ultrasound transmitter and receiver to gain much wider acoustic bandwidth. To verify our design, a prototype of the G2 DMDSM sensor has been fabricated and tested. The testing results show that the G2 DMDSM sensor can achieve better ranging and similar material/structure sensing performance, but with much-simplified configuration and operation. The primary results indicate that the G2 DMDSM sensor could provide a promising solution for fingertip pretouch sensing in robotic grasping.
为了不断提高机器人的抓取能力,我们有兴趣开发一种用于近距离测距和材料传感的非接触式指尖传感器。在此之前,我们展示了一个基于脉冲回波超声和光声学的双模态双传感机制(DMDSM)预触传感器原型。然而,复杂的系统,庞大而昂贵的脉冲接收器,以及全方位灵敏的麦克风阻碍了传感器在真正的机器人手指上的实际应用。为了解决这些问题,我们报告了第二代(G2)没有脉冲接收器和麦克风的DMDSM传感器,这是通过重新设计超声发射器和接收器来获得更宽的声学带宽而实现的。为了验证我们的设计,制作了G2 DMDSM传感器的原型并进行了测试。测试结果表明,G2 DMDSM传感器可以获得更好的测距和相似的材料/结构传感性能,但配置和操作大大简化。初步结果表明,G2 DMDSM传感器为机器人抓握中的指尖预触感提供了一种很有前景的解决方案。
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引用次数: 4
Efficient and Robust Training of Dense Object Nets for Multi-Object Robot Manipulation 面向多目标机器人操作的密集目标网络的高效鲁棒训练
Pub Date : 2022-05-23 DOI: 10.48550/arXiv.2206.12145
David B. Adrian, A. Kupcsik, Markus Spies, H. Neumann
We propose a framework for robust and efficient training of Dense Object Nets (DON) [1] with a focus on industrial multi-object robot manipulation scenarios. DON is a popular approach to obtain dense, view-invariant object descriptors, which can be used for a multitude of downstream tasks in robot manipulation, such as, pose estimation, state representation for control, etc. However, the original work [1] focused training on singulated objects, with limited results on instance-specific, multi-object applications. Additionally, a complex data collection pipeline, including 3D reconstruction and mask annotation of each object, is required for training. In this paper, we further improve the efficacy of DON with a simplified data collection and training regime, that consistently yields higher precision and enables robust tracking of keypoints with less data requirements. In particular, we focus on training with multi-object data instead of singulated objects, combined with a well-chosen augmentation scheme. We additionally propose an alternative loss formulation to the original pixel wise formulation that offers better results and is less sensitive to hyperparameters. Finally, we demonstrate the robustness and accuracy of our proposed framework on a real-world robotic grasping task.
我们提出了一个框架,用于密集对象网络(DON)的鲁棒和高效训练[1],重点关注工业多目标机器人操作场景。DON是一种获得密集的、视图不变的对象描述符的流行方法,它可以用于机器人操作中的大量下游任务,例如姿态估计、控制的状态表示等。然而,最初的工作[1]侧重于单一对象的训练,在特定于实例的多对象应用上的结果有限。此外,训练还需要一个复杂的数据收集管道,包括每个对象的3D重建和掩码注释。在本文中,我们通过简化的数据收集和训练制度进一步提高了DON的有效性,该制度始终产生更高的精度,并以更少的数据需求实现对关键点的鲁棒跟踪。特别是,我们专注于使用多目标数据而不是单一对象进行训练,并结合精心选择的增强方案。我们还提出了一种替代损失公式,以替代原始的像素明智公式,该公式提供了更好的结果,并且对超参数不那么敏感。最后,我们在一个真实的机器人抓取任务上证明了我们提出的框架的鲁棒性和准确性。
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引用次数: 3
Safe endoscope holding in minimally invasive surgery: zero stiffness and adaptive weight compensation 微创手术中安全的内窥镜保持:零刚度和适应性重量补偿
Pub Date : 2022-05-23 DOI: 10.1109/icra46639.2022.9811359
Jesus Mago, F. Louveau, M. Vitrani, G. Morel
One of the major functions brought by robots in Minimally Invasive Surgery is endoscope holding. This consists, for the user, in placing the camera at a desired location which the robot will maintain still once he/she releases it. This behavior is usually achieved with rigid position servoing, leading to possibly high forces generated and safety issues. Model-based weight compensation is an alternative solution. However, endoscopic cameras' weight is difficult to model as their gravity parameters can change during the same surgery. In this paper, an algorithm is presented as an option to cope with this variability in the gravity model without using rigid position servoing. The surgeon first positions the camera in a comanipulation mode (gravity compensation). When he/she releases the camera, if the gravity model is not accurate, the endoscope presents a drift. In this case, a controller brings the endoscope back to its release position by combining low gain position control and model adaptation. Once stabilized, the system is switched back to a zero-stiffness mode. Two in-vitro experiments were performed in which a user manipulates an endoscope whose configuration of mass is changed. In one case, the mass in the gravity model was set to half of the actual one. In the second case, a variable weight was attached to the endoscope. The algorithm successfully updated the model for each experiment reducing position errors by 95% and 57%, respectively.
机器人在微创手术中带来的主要功能之一是持镜。对于用户来说,这包括将相机放置在一个期望的位置,一旦他/她松开相机,机器人将保持静止。这种行为通常是通过刚性位置伺服实现的,导致可能产生的高力和安全问题。基于模型的权重补偿是另一种解决方案。然而,内窥镜相机的重量很难建模,因为它们的重力参数在同一手术中会发生变化。本文提出了一种不使用刚性位置伺服的算法来处理重力模型中的这种可变性。外科医生首先将相机置于协同操作模式(重力补偿)。当他/她松开相机时,如果重力模型不准确,内窥镜就会出现漂移。在这种情况下,控制器通过结合低增益位置控制和模型自适应将内窥镜带回其释放位置。一旦稳定,系统将切换回零刚度模式。进行了两个体外实验,其中用户操纵一个内窥镜,其质量配置被改变。在一种情况下,重力模型中的质量被设置为实际质量的一半。在第二种情况下,在内窥镜上附加了可变重量。该算法成功地更新了每次实验的模型,分别减少了95%和57%的位置误差。
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引用次数: 0
A Data-Driven Multiple Model Framework for Intention Estimation 一种数据驱动的多模型意图估计框架
Pub Date : 2022-05-23 DOI: 10.1109/icra46639.2022.9812432
Yongming Qin, M. Kumon, T. Furukawa
This paper presents a data-driven multiple model framework for estimating the intention of a target from observations. Multiple model (MM) state estimation methods have been extensively used for intention estimation by mapping one intention to one dynamic model assuming one-to-one relations. However, intentions are subjective to humans and it is difficult to establish the one-to-one relations explicitly. The proposed framework infers the multiple-to-multiple relations between intentions and models directly from observations that are labeled with intentions. For intention estimation, both the relations and model probabilities of an Interacting Multiple Model (IMM) state estimation approach are integrated into a recursive Bayesian framework. Taking advantage of the inferred multiple-to-multiple relations, the framework incorpo-rates more accurate relations and avoids following the strict one-to-one relations. Numerical and real experiments were performed to investigate the framework through the intention estimation of a maneuvered quadrotor. Results show higher estimation accuracy and superior flexibility in designing mod-els over the conventional approach that assumes one-to-one relations.
本文提出了一种数据驱动的多模型框架,用于从观测中估计目标的意图。多模型状态估计方法通过将一个意图映射到假设一对一关系的动态模型,已广泛用于意图估计。然而,意图对人来说是主观的,很难明确地建立一对一的关系。提出的框架直接从标记有意图的观察中推断意图和模型之间的多对多关系。在意图估计方面,将交互多模型(IMM)状态估计方法的关系和模型概率集成到递归贝叶斯框架中。利用推导出的多对多关系,该框架结合了更精确的关系,避免了遵循严格的一对一关系。通过对机动四旋翼飞行器的意图估计,对该框架进行了数值和实际实验研究。结果表明,与假设一对一关系的传统方法相比,模型设计具有更高的估计精度和灵活性。
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引用次数: 0
WeakLabel3D-Net: A Complete Framework for Real-Scene LiDAR Point Clouds Weakly Supervised Multi-Tasks Understanding WeakLabel3D-Net:弱监督多任务理解的真实场景激光雷达点云完整框架
Pub Date : 2022-05-23 DOI: 10.1109/icra46639.2022.9811959
Kangcheng Liu, Yuzhi Zhao, Z. Gao, Ben M. Chen
Existing state-of-the-art 3D point clouds understanding methods only perform well in a fully supervised manner. To the best of our knowledge, there exists no unified framework which simultaneously solves the downstream high-level understanding tasks, especially when labels are extremely limited. This work presents a general and simple framework to tackle point clouds understanding when labels are limited. We propose a novel unsupervised region expansion based clustering method for generating clusters. More importantly, we innovatively propose to learn to merge the over-divided clusters based on the local low-level geometric property similarities and the learned high-level feature similarities supervised by weak labels. Hence, the true weak labels guide pseudo labels merging taking both geometric and semantic feature correlations into consideration. Finally, the self-supervised data augmentation optimization module is proposed to guide the propagation of labels among semantically similar points within a scene. Experimental Results demonstrate that our framework has the best performance among the three most important weakly supervised point clouds understanding tasks including semantic segmentation, instance segmentation, and object detection even when limited points are labeled.
现有的最先进的3D点云理解方法只有在完全监督的方式下才能表现良好。据我们所知,目前还没有统一的框架可以同时解决下游的高层次理解任务,尤其是在标签极其有限的情况下。这项工作提出了一个通用的和简单的框架来解决点云理解时,标签是有限的。提出了一种新的基于无监督区域展开的聚类生成方法。更重要的是,我们创新地提出了基于局部低层次几何属性相似度和学习到的由弱标签监督的高层次特征相似度来学习合并过分聚类的方法。因此,真正的弱标签引导伪标签合并,同时考虑几何和语义特征的相关性。最后,提出了自监督数据增强优化模块,用于指导场景中语义相似点之间的标签传播。实验结果表明,即使在有限的点被标记时,我们的框架在三个最重要的弱监督点云理解任务(包括语义分割、实例分割和目标检测)中也具有最好的性能。
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引用次数: 21
A Detumbling Strategy for an Orbital Manipulator in the Post-Grasp Phase 轨道机械臂后抓取阶段的跌落策略
Pub Date : 2022-05-23 DOI: 10.1109/icra46639.2022.9812067
R. Vijayan, M. Stefano, C. Ott
In this paper, we propose a detumbling strategy that stabilizes the motion of a tumbling client satellite using an orbital servicing manipulator, which is the goal of the post-grasp phase. One of the critical aspects in this phase is ensuring that excessive contact forces are not generated at the grasp interface. In addition, space mission requirements might demand a nominal manipulator configuration that is suitable for further manipulation/servicing activities. The proposed strategy allows the detumbling of the client motion while ensuring that the contact forces developed at the grasp interface do not violate a safety threshold. Further, it allows the reconfiguration of the manipulator arm by exploiting the full actuation capability of the manipulator-equipped servicing spacecraft. The controller guarantees joint task convergence in the nullspace of the manipulator's end-effector, and is also valid for kinematically singular configurations of the manipulator. It is further augmented using a quadratic programming based approach to optimally constrain the contact forces. Finally, simulation results for a post-grasp detumbling scenario are shown to validate the effectiveness of the proposed method.
在本文中,我们提出了一种使用轨道服务机械臂稳定翻滚客户端卫星运动的坠落策略,这是抓取后阶段的目标。这一阶段的一个关键方面是确保在抓握界面处不会产生过大的接触力。此外,空间任务需求可能需要一个适用于进一步操作/服务活动的名义机械臂配置。所提出的策略允许客户端运动的下降,同时确保在抓取界面产生的接触力不违反安全阈值。此外,它允许通过利用机械臂装备的服务航天器的全驱动能力来重新配置机械臂。该控制器保证了机械臂末端执行器在零空间上的联合任务收敛性,并对机械臂的运动奇异构型有效。它是进一步扩大使用二次规划为基础的方法,以最佳地约束接触力。最后,给出了抓握后跌落场景的仿真结果,验证了该方法的有效性。
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引用次数: 2
Strawberry picking point localization ripeness and weight estimation 草莓采摘点定位、成熟度及重量估算
Pub Date : 2022-05-23 DOI: 10.1109/icra46639.2022.9812303
Alessandra Tafuro, Adeayo Adewumi, Soran Parsa, Ghalamzan E. Amir, Bappaditya Debnath
Labour shortage, difficulties in labour management, the digitalization of fruit production pipeline to reduce the fruit production costs have made robotic systems for selective harvesting of strawberries an important industry and academic research. One of the important components of such technologies yet to be developed is fruit picking perception. For picking strawberries, a robot needs to infer the location of picking points from the images of strawberries. Moreover, the size and weight of strawberries to be picked can help the robot to place the picked strawberries in proper punnets directly to be delivered to customers in supermarkets. This can save significant time and packing costs in packhouses. Geometry-based approaches are the most common approach to determine the picking point but they suffer from inaccuracies due to noise, occlusion, and varying shape and orientation of the berries. In contrast, we present two novel datasets of strawberries annotated with picking points, key-points (such as the shoulder points, the contact point between the calyx and flesh, and the point on the flesh farthest from the calyx), and the weight and size of the berries. We performed experiments with Detectron-2, which is an extended version of Mask-RCNN with key-points detection capability. The results show that the key-points detection approach works well for picking and grasping point localization. The second dataset also presents the dimensions and weight of strawberries. Our novel baseline model for weight estimation outperforms many state-of-the-art deep networks. The datasets and annotations are available at https://github.com/imanlab/strawberry-pp-w-r-dataset.
劳动力短缺、劳动力管理困难、水果生产流水线数字化以降低水果生产成本,使得草莓选择性采收机器人系统成为重要的产业和学术研究。这种技术的重要组成部分之一是尚未开发的水果采摘感知。对于采摘草莓,机器人需要从草莓的图像中推断出采摘点的位置。此外,要采摘的草莓的大小和重量可以帮助机器人将采摘的草莓放在合适的篮子里,直接送到超市的顾客手中。这可以节省大量的时间和包装成本。基于几何的方法是确定采摘点的最常用方法,但由于噪声,遮挡以及浆果形状和方向的变化,它们存在不准确性。相比之下,我们提出了两个新的草莓数据集,这些数据集标注了采摘点、关键点(如肩点、花萼和果肉之间的接触点、离花萼最远的果肉上的点)以及浆果的重量和大小。我们用Detectron-2进行了实验,它是Mask-RCNN的扩展版本,具有关键点检测功能。结果表明,该关键点检测方法能够很好地实现抓取点定位。第二个数据集也显示了草莓的尺寸和重量。我们的新基线模型的权重估计优于许多最先进的深度网络。数据集和注释可在https://github.com/imanlab/strawberry-pp-w-r-dataset上获得。
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引用次数: 4
Autonomous Ultrasound Scanning using Bayesian Optimization and Hybrid Force Control 基于贝叶斯优化和混合力控制的自主超声扫描
Pub Date : 2022-05-23 DOI: 10.1109/icra46639.2022.9812410
Raghavv Goel, Fnu Abhimanyu, Kirtan Patel, J. Galeotti, H. Choset
Ultrasound scanning is an imaging technique that aids medical professionals in diagnostics and interventional procedures. However, a trained human-in-the-loop (HITL) with a radiologist is required to perform the scanning procedure. We seek to create a novel ultrasound system that can provide imaging in the absence of a trained radiologist, say for patients in the field who suffered injuries after a natural disaster. One challenge of automating ultrasound scanning involves finding the optimal area to scan and then performing the actual scan. This task requires simultaneously maintaining contact with the surface while moving along it to capture high quality images. In this work, we present an automated Robotic Ultrasound System (RUS) to tackle these challenges. Our approach introduces a Bayesian Optimization framework to guide the probe to multiple points on the unknown surface. Our proposed framework collects the ultrasound images as well as the pose information at every probed point to estimate regions with high vessel density (information map) and the surface contour. Based on the information map and the surface contour, an area of interest is selected for scanning. Furthermore, to scan the proposed region, a novel 6-axis hybrid force-position controller is presented to ensure acoustic coupling. Lastly, we provide experimental results on two different phantom models to corroborate our approach.
超声扫描是一种成像技术,可以帮助医疗专业人员进行诊断和介入治疗。然而,需要一个训练有素的人在环(HITL)与放射科医生执行扫描过程。我们试图创造一种新的超声系统,可以在没有训练有素的放射科医生的情况下提供成像,比如为在自然灾害后受伤的现场病人提供成像。自动化超声扫描的一个挑战是找到最佳的扫描区域,然后进行实际的扫描。这项任务需要在沿着表面移动的同时保持与表面的接触,以捕获高质量的图像。在这项工作中,我们提出了一个自动化机器人超声系统(RUS)来解决这些挑战。我们的方法引入了一个贝叶斯优化框架来引导探针到未知表面上的多个点。我们提出的框架收集超声图像以及每个探测点的位姿信息,以估计血管密度高的区域(信息图)和表面轮廓。基于信息图和表面轮廓,选择感兴趣的区域进行扫描。此外,为了扫描所提出的区域,提出了一种新型的六轴混合力-位置控制器,以确保声耦合。最后,我们提供了两种不同的幻影模型的实验结果来证实我们的方法。
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引用次数: 8
Value learning from trajectory optimization and Sobolev descent: A step toward reinforcement learning with superlinear convergence properties 基于轨迹优化和Sobolev下降的价值学习:向具有超线性收敛特性的强化学习迈出的一步
Pub Date : 2022-05-23 DOI: 10.1109/icra46639.2022.9811993
Amit Parag, Sébastien Kleff, Léo Saci, N. Mansard, O. Stasse
The recent successes in deep reinforcement learning largely rely on the capabilities of generating masses of data, which in turn implies the use of a simulator. In particular, current progress in multi body dynamic simulators are under-pinning the implementation of reinforcement learning for end-to-end control of robotic systems. Yet simulators are mostly considered as black boxes while we have the knowledge to make them produce a richer information. In this paper, we are proposing to use the derivatives of the simulator to help with the convergence of the learning. For that, we combine model-based trajectory optimization to produce informative trials using 1st- and 2nd-order simulation derivatives. These locally-optimal runs give fair estimates of the value function and its derivatives, that we use to accelerate the convergence of the critics using Sobolev learning. We empirically demonstrate that the algorithm leads to a faster and more accurate estimation of the value function. The resulting value estimate is used in model-predictive controller as a proxy for shortening the preview horizon. We believe that it is also a first step toward superlinear reinforcement learning algorithm using simulation derivatives, that we need for end-to-end legged locomotion.
最近深度强化学习的成功很大程度上依赖于生成大量数据的能力,而这反过来又意味着模拟器的使用。特别是,当前多体动态模拟器的进展为机器人系统端到端控制的强化学习的实施奠定了基础。然而,模拟器大多被认为是黑盒子,而我们有知识让它们产生更丰富的信息。在本文中,我们建议使用模拟器的导数来帮助学习的收敛。为此,我们结合基于模型的轨迹优化,使用一阶和二阶仿真导数产生信息丰富的试验。这些局部最优运行给出了价值函数及其衍生物的公平估计,我们使用Sobolev学习来加速批评者的收敛。我们的经验证明,该算法导致一个更快,更准确的估计值函数。在模型预测控制器中,将得到的估计值作为缩短预测视界的代理。我们相信这也是使用模拟导数的超线性强化学习算法的第一步,我们需要端到端腿运动。
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引用次数: 7
期刊
2022 International Conference on Robotics and Automation (ICRA)
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