机器人相关学术速递[7.14]

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cs.RO机器人相关，共计18篇

【1】 OpenCDA:An Open Cooperative Driving Automation FrameworkIntegrated with Co-Simulation 标题：OpenCDA：一种集成协同仿真的开放式协同驾驶自动化框架

作者：Runsheng Xu,Yi Guo,Xu Han,Xin Xia,Hao Xiang,Jiaqi Ma 机构： University of California, Los Angeles (UCLA) 2Yi Guo is with University of Cincinnati 备注：None 链接：https://arxiv.org/abs/2107.06260 摘要：尽管协同驾驶自动化（CDA）近年来受到了广泛的关注，但在这一领域仍然存在许多挑战。现有的主要集中于单车智能的仿真平台与CDA开发之间的差距是关键障碍之一，因为它阻碍了研究人员方便地验证和比较不同的CDA算法。为此，我们提出了OpenCDA，一个开发和测试CDA系统的通用框架和工具。具体来说，OpenCDA由三个主要组件组成：一个具有不同用途和分辨率的模拟器的协同仿真平台、一个全栈协同驱动系统和一个场景管理器。通过这三个组件的交互作用，我们的框架为研究人员提供了一种简单的方法，可以在流量和个体自治两个层次上测试不同的CDA算法。更重要的是，OpenCDA是高度模块化的，并且安装了基准算法和测试用例。用户可以方便地用定制算法替换任何默认模块，并使用CDA平台的其他默认模块来评估新功能在增强CDA整体性能方面的有效性。通过一个排程实现的例子说明了该框架对CDA研究的能力。OpenCDA的代码在https://github.com/ucla-mobility/OpenCDA. 摘要：Although Cooperative Driving Automation (CDA) has attracted considerable attention in recent years, there remain numerous open challenges in this field. The gap between existing simulation platforms that mainly concentrate on single-vehicle intelligence and CDA development is one of the critical barriers, as it inhibits researchers from validating and comparing different CDA algorithms conveniently. To this end, we propose OpenCDA, a generalized framework and tool for developing and testing CDA systems. Specifically, OpenCDA is composed of three major components: a co-simulation platform with simulators of different purposes and resolutions, a full-stack cooperative driving system, and a scenario manager. Through the interactions of these three components, our framework offers a straightforward way for researchers to test different CDA algorithms at both levels of traffic and individual autonomy. More importantly, OpenCDA is highly modularized and installed with benchmark algorithms and test cases. Users can conveniently replace any default module with customized algorithms and use other default modules of the CDA platform to perform evaluations of the effectiveness of new functionalities in enhancing the overall CDA performance. An example of platooning implementation is used to illustrate the framework's capability for CDA research. The codes of OpenCDA are available in the https://github.com/ucla-mobility/OpenCDA.

【2】 Object Tracking and Geo-localization from Street Images 标题：基于街道图像的目标跟踪与地理定位

作者：Daniel Wilson,Thayer Alshaabi,Colin Van Oort,Xiaohan Zhang,Jonathan Nelson,Safwan Wshah 机构：• A large and realistic dataset to support research in the field of object geolo-, calization, • An object detector designed to predict GPS locations using a local offset, and coordinate transform 备注：28 pages, 7 figures, to be submitted to Elsevier Pattern Recognition 链接：https://arxiv.org/abs/2107.06257 摘要：从街道图像中对静态物体进行地理定位是一项挑战，但对于道路资源测绘和自动驾驶也非常重要。在本文中，我们提出了一个两阶段的框架，检测和地理定位交通标志从低帧速率街道视频。我们提出的系统使用了一种改进的视网膜网（GPS-RetinaNet），除了执行标准分类和边界盒回归外，还可以预测每个标志相对于相机的位置偏移。我们的自定义跟踪器由学习的度量网络和匈牙利算法的变体组成，将GPS视网膜网中的候选符号检测浓缩为地理定位符号。我们的度量网络估计检测对之间的相似性，然后匈牙利算法使用度量网络提供的相似性分数匹配图像中的检测。我们的模型是使用更新版本的ARTS数据集训练的，该数据集包含25544幅图像和47.589个符号注释~\cite{ARTS}。拟议的数据集涵盖了从广泛的道路选择中收集的各种环境。每个注释都包含一个标志类标签、其地理空间位置、装配标签、路侧指示器，以及有助于评估的唯一标识符。该数据集将支持该领域的未来进展，并且所提出的系统演示了如何利用真实地理定位数据集的一些独特特性。 摘要：Geo-localizing static objects from street images is challenging but also very important for road asset mapping and autonomous driving. In this paper we present a two-stage framework that detects and geolocalizes traffic signs from low frame rate street videos. Our proposed system uses a modified version of RetinaNet (GPS-RetinaNet), which predicts a positional offset for each sign relative to the camera, in addition to performing the standard classification and bounding box regression. Candidate sign detections from GPS-RetinaNet are condensed into geolocalized signs by our custom tracker, which consists of a learned metric network and a variant of the Hungarian Algorithm. Our metric network estimates the similarity between pairs of detections, then the Hungarian Algorithm matches detections across images using the similarity scores provided by the metric network. Our models were trained using an updated version of the ARTS dataset, which contains 25,544 images and 47.589 sign annotations ~\cite{arts}. The proposed dataset covers a diverse set of environments gathered from a broad selection of roads. Each annotaiton contains a sign class label, its geospatial location, an assembly label, a side of road indicator, and unique identifiers that aid in the evaluation. This dataset will support future progress in the field, and the proposed system demonstrates how to take advantage of some of the unique characteristics of a realistic geolocalization dataset.

【3】 Everybody Is Unique: Towards Unbiased Human Mesh Recovery 标题：每个人都是独一无二的：走向不偏不倚的人脉恢复

作者：Ren Li,Meng Zheng,Srikrishna Karanam,Terrence Chen,Ziyan Wu 机构：United Imaging Intelligence, Cambridge MA 备注：10 pages, 5 figures, 4 tables 链接：https://arxiv.org/abs/2107.06239 摘要：我们考虑肥胖人网格恢复的问题，即，将参数人类网格拟合到肥胖人群的图像。尽管肥胖者的网格拟合是许多应用（如医疗保健）中的一个重要问题，但网格恢复方面的许多最新进展仅限于非肥胖者的图像。在这项工作中，我们通过介绍和讨论现有算法的局限性，找出了当前文献中的这一关键差距。接下来，我们将提供一个简单的基线来解决这个问题，它是可伸缩的，并且可以很容易地与现有算法结合使用，以提高它们的性能。最后，我们提出了一个广义人体网格优化算法，大大提高了现有方法在肥胖者图像和社区标准基准数据集上的性能。该技术的一个关键创新是，它不依赖于昂贵的监视来创建网格参数。取而代之的是，从广泛和廉价的二维关键点注释开始，我们的方法自动生成网格参数，这些参数可以用来重新训练和微调任何现有的网格估计算法。通过这种方式，我们展示了我们的方法作为一个下降，以提高性能的各种当代网格估计方法。我们进行了广泛的实验，在多个数据集，包括标准和肥胖的人的图像，并证明了我们提出的技术的有效性。 摘要：We consider the problem of obese human mesh recovery, i.e., fitting a parametric human mesh to images of obese people. Despite obese person mesh fitting being an important problem with numerous applications (e.g., healthcare), much recent progress in mesh recovery has been restricted to images of non-obese people. In this work, we identify this crucial gap in the current literature by presenting and discussing limitations of existing algorithms. Next, we present a simple baseline to address this problem that is scalable and can be easily used in conjunction with existing algorithms to improve their performance. Finally, we present a generalized human mesh optimization algorithm that substantially improves the performance of existing methods on both obese person images as well as community-standard benchmark datasets. A key innovation of this technique is that it does not rely on supervision from expensive-to-create mesh parameters. Instead, starting from widely and cheaply available 2D keypoints annotations, our method automatically generates mesh parameters that can in turn be used to re-train and fine-tune any existing mesh estimation algorithm. This way, we show our method acts as a drop-in to improve the performance of a wide variety of contemporary mesh estimation methods. We conduct extensive experiments on multiple datasets comprising both standard and obese person images and demonstrate the efficacy of our proposed techniques.

【4】 Efficient and Reactive Planning for High Speed Robot Air Hockey 标题：高速机器人曲棍球的高效反应性规划

作者：Puze Liu,Davide Tateo,Haitham Bou-Ammar,Jan Peters 备注：IEEE/RJS International Conference on Intelligent RObots and Systems (IROS) 链接：https://arxiv.org/abs/2107.06140 摘要：高度动态的机器人任务需要高速反应的机器人。由于物理限制、硬件限制以及动力学和传感器测量的高度不确定性，这些任务尤其具有挑战性。为了面对这些问题，设计出精确快速的轨迹并对环境变化做出快速反应的机器人代理至关重要。空中曲棍球就是这种任务的一个例子。由于环境的特点，将问题形式化并导出清晰的数学解是可能的。基于这些原因，这种环境非常适合将当前可用的通用机械手的性能推向极限。利用两个Kuka-iiwa14，我们展示了如何设计一个用于空中曲棍球游戏的通用机械手策略。我们证明了一个真实的机器人手臂可以进行快速的击球动作，并且两个机器人可以在一个中型的空中曲棍球台上进行对抗。 摘要：Highly dynamic robotic tasks require high-speed and reactive robots. These tasks are particularly challenging due to the physical constraints, hardware limitations, and the high uncertainty of dynamics and sensor measures. To face these issues, it's crucial to design robotics agents that generate precise and fast trajectories and react immediately to environmental changes. Air hockey is an example of this kind of task. Due to the environment's characteristics, it is possible to formalize the problem and derive clean mathematical solutions. For these reasons, this environment is perfect for pushing to the limit the performance of currently available general-purpose robotic manipulators. Using two Kuka Iiwa 14, we show how to design a policy for general-purpose robotic manipulators for the air hockey game. We demonstrate that a real robot arm can perform fast-hitting movements and that the two robots can play against each other on a medium-size air hockey table in simulation.

【5】 A Novel Dual Quaternion Based Dynamic Motion Primitives for Acrobatic Flight 标题：一种新颖的基于对偶四元数的杂技飞行动态运动基元

作者：Renshan Zhang,Yongyang Hu,Kuang Zhao,Su Cao 机构：Nanjing Telecommunication Technology Research Institute, Nanjing, China, Institute of Unmanned Systems, National University of Defense Technology, Changsha, China 备注：6 pages 链接：https://arxiv.org/abs/2107.06116 摘要：对于固定翼无人机（UAV）的杂技飞行来说，由于平移旋转运动固有的耦合问题，运动描述的实现是一项具有挑战性的工作。本文利用模拟学习的思想，提出了一种新的机动描述方法，主要贡献有两个方面：1）提出了一种基于双四元数的动态运动原语（DQ-DMP），在不损失精度的前提下，将位置和姿态的状态方程结合起来。2） 建立了在线半实物训练系统。基于DQDMP方法，可以实时获得演示机动的几何特征，并从理论上证明了DQ-DMP方法的稳定性。仿真结果表明，与传统的位姿解耦方法相比，该方法具有明显的优越性。 摘要：The realization of motion description is a challenging work for fixed-wing Unmanned Aerial Vehicle (UAV) acrobatic flight, due to the inherent coupling problem in ranslational-rotational motion. This paper aims to develop a novel maneuver description method through the idea of imitation learning, and there are two main contributions of our work: 1) A dual quaternion based dynamic motion primitives (DQ-DMP) is proposed and the state equations of the position and attitude can be combined without loss of accuracy. 2) An online hardware-inthe-loop (HITL) training system is established. Based on the DQDMP method, the geometric features of the demonstrated maneuver can be obtained in real-time, and the stability of the DQ-DMP is theoretically proved. The simulation results illustrate the superiority of the proposed method compared to the traditional position/attitude decoupling method.

【6】 Teaching Agents how to Map: Spatial Reasoning for Multi-Object Navigation 标题：教Agent如何绘制地图：多目标导航的空间推理

作者：Pierre Marza,Laetitia Matignon,Olivier Simonin,Christian Wolf 机构： LIRIS, UMR CNRS , Université de Lyon, INSA Lyon, Villeurbanne, France, Université de Lyon, Univ. Lyon , CITI Lab, INRIA Chroma team 链接：https://arxiv.org/abs/2107.06011 摘要：在视觉导航的背景下，为了使agent能够在所考虑的地点利用其观察历史并有效地达到已知的目标，绘制一个新环境的能力是必要的。这种能力可以与空间推理相联系，在空间推理中，智能体能够感知空间关系和规律，并发现对象的启示。在经典的强化学习（RL）设置中，这种能力仅从奖励中学习。我们引入了辅助任务形式的辅助监督，旨在帮助为达到下游目标而训练的代理出现空间感知能力。我们发现，学习估计量化给定位置的代理和目标之间的空间关系的度量在多目标导航设置中具有很高的积极影响。我们的方法显著提高了不同基线代理的性能，这些代理可以构建环境的显式或隐式表示，甚至可以匹配以地面真值图作为输入的不可比较的oracle代理的性能。 摘要：In the context of visual navigation, the capacity to map a novel environment is necessary for an agent to exploit its observation history in the considered place and efficiently reach known goals. This ability can be associated with spatial reasoning, where an agent is able to perceive spatial relationships and regularities, and discover object affordances. In classical Reinforcement Learning (RL) setups, this capacity is learned from reward alone. We introduce supplementary supervision in the form of auxiliary tasks designed to favor the emergence of spatial perception capabilities in agents trained for a goal-reaching downstream objective. We show that learning to estimate metrics quantifying the spatial relationships between an agent at a given location and a goal to reach has a high positive impact in Multi-Object Navigation settings. Our method significantly improves the performance of different baseline agents, that either build an explicit or implicit representation of the environment, even matching the performance of incomparable oracle agents taking ground-truth maps as input.

【7】 Motion-Aware Robotic 3D Ultrasound 标题：运动感知机器人三维超声

作者：Zhongliang Jiang,Hanyu Wang,Zhenyu Li,Matthias Grimm,Mingchuan Zhou,Ulrich Eck,Sandra V. Brecht,Tim C. Lueth,Thomas Wendler,Nassir Navab 机构： Lueth are with the Institute of Micro Technologyand Medical Device Technology, Technical University of Munich 备注：Accepted to ICRA2021 链接：https://arxiv.org/abs/2107.05998 摘要：机器人三维超声成像（3D）已被用来克服传统超声检查的缺点，如高操作间的可变性和缺乏可重复性。然而，物体运动仍然是一个挑战，因为意外的运动会降低三维合成的质量。此外，传统的机器人US系统不允许尝试调整对象，例如调整肢体以显示整个肢体动脉树。为了应对这一挑战，我们提出了一种基于视觉的机器人超声系统，该系统能够监测物体的运动，并自动更新扫描轨迹，无缝地提供目标解剖结构的三维复合图像。为了实现这些功能，使用深度相机提取手动规划的扫描轨迹，然后利用提取的三维轨迹估计物体的法向。随后，为了监控运动并进一步补偿这种运动以精确跟踪轨迹，实时跟踪固定被动标记的位置。最后，进行分步复合。在凝胶体模上的实验表明，在扫描过程中，当物体不静止时，系统可以恢复扫描。 摘要：Robotic three-dimensional (3D) ultrasound (US) imaging has been employed to overcome the drawbacks of traditional US examinations, such as high inter-operator variability and lack of repeatability. However, object movement remains a challenge as unexpected motion decreases the quality of the 3D compounding. Furthermore, attempted adjustment of objects, e.g., adjusting limbs to display the entire limb artery tree, is not allowed for conventional robotic US systems. To address this challenge, we propose a vision-based robotic US system that can monitor the object's motion and automatically update the sweep trajectory to provide 3D compounded images of the target anatomy seamlessly. To achieve these functions, a depth camera is employed to extract the manually planned sweep trajectory after which the normal direction of the object is estimated using the extracted 3D trajectory. Subsequently, to monitor the movement and further compensate for this motion to accurately follow the trajectory, the position of firmly attached passive markers is tracked in real-time. Finally, a step-wise compounding was performed. The experiments on a gel phantom demonstrate that the system can resume a sweep when the object is not stationary during scanning.

【8】 Multi-Objective Graph Heuristic Search for Terrestrial Robot Design 标题：多目标图启发式搜索在地面机器人设计中的应用

作者：Jie Xu,Andrew Spielberg,Allan Zhao,Daniela Rus,Wojciech Matusik 机构：MassachusettsInstituteofTechnology 备注：IEEE International Conference on Robotics and Automation (ICRA 2021) 链接：https://arxiv.org/abs/2107.05858 摘要：提出了一种基于控制和形态学（包括离散拓扑）的多目标刚性机器人协同设计方法。以往的工作都是针对单目标机器人协同设计或多目标控制的问题。然而，关节多目标协同设计问题对于生成功能强大、通用性强、算法设计简单的机器人是非常重要的。在这项工作中，我们提出了多目标图启发式搜索，它扩展了单目标图启发式搜索，使一个高效的多目标搜索组合设计拓扑空间。该方法的核心是引入一种新的基于图神经网络的通用多目标启发式函数，能够有效地利用不同任务之间的学习信息。我们在七个地面运动和设计任务的六个组合上展示了我们的方法，包括一个三目标的例子。我们比较了不同方法捕获的Pareto前沿，并证明了我们的多目标图启发式搜索在数量和质量上都优于其他方法。 摘要：We present methods for co-designing rigid robots over control and morphology (including discrete topology) over multiple objectives. Previous work has addressed problems in single-objective robot co-design or multi-objective control. However, the joint multi-objective co-design problem is extremely important for generating capable, versatile, algorithmically designed robots. In this work, we present Multi-Objective Graph Heuristic Search, which extends a single-objective graph heuristic search from previous work to enable a highly efficient multi-objective search in a combinatorial design topology space. Core to this approach, we introduce a new universal, multi-objective heuristic function based on graph neural networks that is able to effectively leverage learned information between different task trade-offs. We demonstrate our approach on six combinations of seven terrestrial locomotion and design tasks, including one three-objective example. We compare the captured Pareto fronts across different methods and demonstrate that our multi-objective graph heuristic search quantitatively and qualitatively outperforms other techniques.

【9】 Precise Visual-Inertial Localization for UAV with the Aid of A 2D Georeferenced Map 标题：基于二维地理参考图的无人机精确视觉惯性定位

作者：Jun Mao,Lilian Zhang,Xiaofeng He,Hao Qu,Xiaoping Hu 机构： National University of Defense Technology 链接：https://arxiv.org/abs/2107.05851 摘要：精确的地理定位是无人机的关键。然而，目前部署的无人机大多依赖全球导航卫星系统（GNSS）或高精度惯性导航系统（INS）进行地理定位。在本文中，我们建议使用一个轻量级的视觉惯性系统和一个二维的地理参考地图，以获得准确和连续的无人机大地测量位置。该系统首先利用微型惯性测量单元（MIMU）和单目摄像机作为里程计，在局部世界框架内连续估计导航状态，重建观测到的视觉特征的三维位置。为了获得地理位置，通过里程计跟踪的视觉特征被进一步注册到二维地理参考地图上。在传统的航空影像配准方法中，我们提出将重建点与大地坐标系中的地图点对齐；这有助于过滤掉大部分离群值，并从水平角度解耦负面影响。然后使用注册点在大地坐标系中重新定位车辆。最后，利用位姿图融合航空图像配准的定位结果和视觉惯性里程计（VIO）的局部导航结果，实现连续无漂移的定位性能。通过将传感器刚性安装在无人机机身上，并在不同环境下进行了两次未知初始值的飞行试验，验证了该方法的有效性。结果表明，该方法在100m高度飞行时可获得小于4m的位置误差，在300m高度飞行时可获得小于9m的位置误差。 摘要：Precise geolocalization is crucial for unmanned aerial vehicles (UAVs). However, most current deployed UAVs rely on the global navigation satellite systems (GNSS) or high precision inertial navigation systems (INS) for geolocalization. In this paper, we propose to use a lightweight visual-inertial system with a 2D georeference map to obtain accurate and consecutive geodetic positions for UAVs. The proposed system firstly integrates a micro inertial measurement unit (MIMU) and a monocular camera as odometry to consecutively estimate the navigation states and reconstruct the 3D position of the observed visual features in the local world frame. To obtain the geolocation, the visual features tracked by the odometry are further registered to the 2D georeferenced map. While most conventional methods perform image-level aerial image registration, we propose to align the reconstructed points to the map points in the geodetic frame; this helps to filter out the large portion of outliers and decouples the negative effects from the horizontal angles. The registered points are then used to relocalize the vehicle in the geodetic frame. Finally, a pose graph is deployed to fuse the geolocation from the aerial image registration and the local navigation result from the visual-inertial odometry (VIO) to achieve consecutive and drift-free geolocalization performance. We have validated the proposed method by installing the sensors to a UAV body rigidly and have conducted two flights in different environments with unknown initials. The results show that the proposed method can achieve less than 4m position error in flight at 100m high and less than 9m position error in flight about 300m high.

【10】 Motion Planning by Learning the Solution Manifold in Trajectory Optimization 标题：轨迹优化中基于解流形学习的运动规划

作者：Takayuki Osa 机构： 1Kyushu Institute of Technology, Kyushu Institute of Technology Department of HumanIntelligence Systems & Research Center for Neuromorphic AI HardwareBehavior Learning Systems Loboratory 备注：24 pages, to appear in the International Journal of Robotics Research 链接：https://arxiv.org/abs/2107.05842 摘要：轨迹优化中使用的目标函数通常是非凸的，可以有无穷多个局部最优解。在这种情况下，有不同的解决方案来执行给定的任务。虽然有一些方法可以找到运动规划的多个解决方案，但它们仅限于生成一组有限的解决方案。为了解决这个问题，我们提出了一种优化方法，学习无穷多个解决方案的轨迹优化。在我们的框架中，通过学习解的潜在表示来获得不同的解。我们的方法可以解释为训练一个深层的无碰撞轨迹生成模型来进行运动规划。实验结果表明，训练后的模型代表了运动规划问题的无穷多个同伦解。 摘要：The objective function used in trajectory optimization is often non-convex and can have an infinite set of local optima. In such cases, there are diverse solutions to perform a given task. Although there are a few methods to find multiple solutions for motion planning, they are limited to generating a finite set of solutions. To address this issue, we presents an optimization method that learns an infinite set of solutions in trajectory optimization. In our framework, diverse solutions are obtained by learning latent representations of solutions. Our approach can be interpreted as training a deep generative model of collision-free trajectories for motion planning. The experimental results indicate that the trained model represents an infinite set of homotopic solutions for motion planning problems.

【11】 A Hierarchical Bayesian model for Inverse RL in Partially-Controlled Environments 标题：部分受控环境下逆RL的分层贝叶斯模型

作者：Kenneth Bogert,Prashant Doshi 机构： other 1Kenneth Bogert is with Department of Computer Science, University ofNorth Carolina, University of Georgia 备注：8 pages, 10 figures 链接：https://arxiv.org/abs/2107.05818 摘要：在真实世界中，使用逆强化学习（IRL）从观察中学习的机器人在演示过程中可能会遇到环境中的物体或代理，而不是专家。在完全受控的环境（如虚拟仿真或实验室设置）中，这些混杂元素通常会被移除。当无法完全清除时，必须过滤掉有害的观察结果。然而，在进行大量观测时，很难确定观测的来源。为了解决这个问题，我们提出了一个分层贝叶斯模型，它结合了专家和混杂元素的观察结果，从而明确地为机器人可能接收到的各种观察结果建模。我们扩展现有的ILL算法最初设计工作在部分遮挡的专家考虑不同的意见。在一个包含遮挡和混杂元素的模拟机器人排序域中，我们证明了该模型的有效性。特别是，我们的技术优于其他几种比较方法，仅次于对受试者轨迹的完美了解。 摘要：Robots learning from observations in the real world using inverse reinforcement learning (IRL) may encounter objects or agents in the environment, other than the expert, that cause nuisance observations during the demonstration. These confounding elements are typically removed in fully-controlled environments such as virtual simulations or lab settings. When complete removal is impossible the nuisance observations must be filtered out. However, identifying the source of observations when large amounts of observations are made is difficult. To address this, we present a hierarchical Bayesian model that incorporates both the expert's and the confounding elements' observations thereby explicitly modeling the diverse observations a robot may receive. We extend an existing IRL algorithm originally designed to work under partial occlusion of the expert to consider the diverse observations. In a simulated robotic sorting domain containing both occlusion and confounding elements, we demonstrate the model's effectiveness. In particular, our technique outperforms several other comparative methods, second only to having perfect knowledge of the subject's trajectory.

【12】 Safety and progress proofs for a reactive planner and controller for autonomous driving 标题：用于自动驾驶的反应式规划器和控制器的安全和进度证明

作者：Abolfazl Karimi,Manish Goyal,Parasara Sridhar Duggirala 机构：Department of Computer Science, University of North Carolina, Chapel Hill, United States 链接：https://arxiv.org/abs/2107.05815 摘要：在本文中，我们进行了安全性和性能分析的自主车辆，实现反应式规划和控制器导航一圈比赛。与能够访问环境地图的传统规划算法不同，反应式规划器仅基于传感器的当前输入生成规划。我们的反应式计划者在本地Voronoi图上选择一个航路点，我们使用一个纯追踪控制器导航到该航路点。我们的安全性和性能分析分为两部分。第一部分证明了反应式规划器计算的规划与用全映射计算的Voronoi规划是局部一致的。第二部分将车辆沿Voronoi图导航的演化建模为一个混合自动机。为了证明该混合自动机的安全性和性能指标，我们计算了该混合自动机的可达集，并对其进行了改进，使其计算更加容易。我们证明了一个自主车辆实现我们的反应式规划和控制器是安全的，并成功地完成了一圈五个不同的电路。此外，我们在模拟环境中以及在小型自主车辆上实现了我们的规划器和控制器，并证明了我们的规划器在各种电路中都能很好地工作。 摘要：In this paper, we perform safety and performance analysis of an autonomous vehicle that implements reactive planner and controller for navigating a race lap. Unlike traditional planning algorithms that have access to a map of the environment, reactive planner generates the plan purely based on the current input from sensors. Our reactive planner selects a waypoint on the local Voronoi diagram and we use a pure-pursuit controller to navigate towards the waypoint. Our safety and performance analysis has two parts. The first part demonstrates that the reactive planner computes a plan that is locally consistent with the Voronoi plan computed with full map. The second part involves modeling of the evolution of vehicle navigating along the Voronoi diagram as a hybrid automata. For proving the safety and performance specification, we compute the reachable set of this hybrid automata and employ some enhancements that make this computation easier. We demonstrate that an autonomous vehicle implementing our reactive planner and controller is safe and successfully completes a lap for five different circuits. In addition, we have implemented our planner and controller in a simulation environment as well as a scaled down autonomous vehicle and demonstrate that our planner works well for a wide variety of circuits.

【13】 Design of a Smooth Landing Trajectory Tracking System for a Fixed-wing Aircraft 标题：固定翼飞机平稳着陆轨迹跟踪系统设计

作者：Solomon Gudeta,Ali Karimoddini 备注：6 pages, 9 figures, American Control Conference 链接：https://arxiv.org/abs/2107.05803 摘要：本文提出了一种固定翼飞机在着陆阶段的着陆控制器，以保证飞机平稳到达着陆点。将着陆问题转化为有限时间线性二次跟踪（LQT）问题，即飞机在满足性能要求和飞行约束的前提下，在纵向垂直面上跟踪所需的着陆路径。首先，我们设计一个满足飞行性能要求和约束的平滑轨迹。然后，设计了一个最优控制器，使飞机在期望时间内着陆时跟踪误差最小化。为此，在小航迹角和恒定进近速度的假设下建立了飞机的线性化模型。由此产生的微分Riccati方程求解时间向后使用休眠王子算法。仿真结果表明，在不同初始条件下，系统具有良好的跟踪性能和跟踪误差的有限时间收敛性。 摘要：This paper presents a landing controller for a fixed-wing aircraft during the landing phase, ensuring the aircraft reaches the touchdown point smoothly. The landing problem is converted to a finite-time linear quadratic tracking (LQT) problem in which an aircraft needs to track the desired landing path in the longitudinal-vertical plane while satisfying performance requirements and flight constraints. First, we design a smooth trajectory that meets flight performance requirements and constraints. Then, an optimal controller is designed to minimize the tracking error, while landing the aircraft within the desired time frame. For this purpose, a linearized model of an aircraft developed under the assumption of a small flight path angle and a constant approach speed is used. The resulting Differential Riccati equation is solved backward in time using the Dormand Prince algorithm. Simulation results show a satisfactory tracking performance and the finite-time convergence of tracking errors for different initial conditions of the flare-out phase of landing.

【14】 Kit-Net: Self-Supervised Learning to Kit Novel 3D Objects into Novel 3D Cavities 标题：KIT-Net：将新的3D对象装入新的3D腔的自监督学习

作者：Shivin Devgon,Jeffrey Ichnowski,Michael Danielczuk,Daniel S. Brown,Ashwin Balakrishna,Shirin Joshi,Eduardo M. C. Rocha,Eugen Solowjow,Ken Goldberg 机构： 1TheAUTOLABattheUniversityofCalifornia 备注：None 链接：https://arxiv.org/abs/2107.05789 摘要：在工业零件装配中，三维物体被插入型腔中进行运输或后续装配。配套是一个关键的步骤，因为它可以减少下游加工和处理时间，并使较低的存储和运输成本。我们提出了Kit-Net，一个框架，用于将以前看不见的三维物体装配成空腔，给出目标空腔和一个物体在未知初始方向上被夹钳夹住的深度图像。Kit-Net采用自监督深度学习和数据增强的方法训练卷积神经网络（CNN），利用模拟深度图像对的大型训练数据集，鲁棒地估计物体之间的三维旋转，并匹配凹腔或凸腔。然后，Kit-Net使用训练好的CNN来实现一个控制器来定位和定位新的物体，以便插入到新的棱柱形和共形三维腔中。仿真实验表明，Kit网能使目标网格与目标空腔的平均相交体积达到98.9%。用工业物体进行的物理实验在使用基线方法的试验中成功率为18%，在使用Kit-Net的试验中成功率为63%。视频、代码和数据可在https://github.com/BerkeleyAutomation/Kit-Net. 摘要：In industrial part kitting, 3D objects are inserted into cavities for transportation or subsequent assembly. Kitting is a critical step as it can decrease downstream processing and handling times and enable lower storage and shipping costs. We present Kit-Net, a framework for kitting previously unseen 3D objects into cavities given depth images of both the target cavity and an object held by a gripper in an unknown initial orientation. Kit-Net uses self-supervised deep learning and data augmentation to train a convolutional neural network (CNN) to robustly estimate 3D rotations between objects and matching concave or convex cavities using a large training dataset of simulated depth images pairs. Kit-Net then uses the trained CNN to implement a controller to orient and position novel objects for insertion into novel prismatic and conformal 3D cavities. Experiments in simulation suggest that Kit-Net can orient objects to have a 98.9% average intersection volume between the object mesh and that of the target cavity. Physical experiments with industrial objects succeed in 18% of trials using a baseline method and in 63% of trials with Kit-Net. Video, code, and data are available at https://github.com/BerkeleyAutomation/Kit-Net.

【15】 DefGraspSim: Simulation-based grasping of 3D deformable objects 标题：DefGraspSim：基于仿真的三维可变形物体抓取

作者：Isabella Huang,Yashraj Narang,Clemens Eppner,Balakumar Sundaralingam,Miles Macklin,Tucker Hermans,Dieter Fox 机构： USA; 3School ofComputing, University of Utah, Allen Schoolof Computer Science & Engineering, University of Washington 备注：11 pages, 19 figures. For associated website and code repository, see this https URL and this https URL Published in DO-Sim: Workshop on Deformable Object Simulation in Robotics at Robotics: Science and Systems (RSS) 2021 链接：https://arxiv.org/abs/2107.05778 摘要：机器人抓取三维可变形物体（如水果/蔬菜、内脏、瓶子/盒子）对于食品加工、机器人手术和家庭自动化等实际应用至关重要。然而，为这些物体开发抓取策略是一个独特的挑战。在这项工作中，我们使用基于GPU的共旋转有限元法（FEM）来有效地模拟对广泛的3D可变形物体的抓取。为了便于将来的研究，我们开放了我们的模拟数据集（34个对象，1e5 Pa弹性范围，6800个抓握评估，1.1M抓握测量），以及一个代码库，允许研究人员在他们选择的任意三维对象模型上运行我们基于FEM的完整抓握评估管道。我们还对6个对象原语进行了详细的分析。对于每个原语，我们系统地描述不同抓取策略的效果，计算一组性能指标（例如，变形、应力），以充分捕捉对象响应，并识别简单的抓取特征（例如，夹持器位移，接触面积）在拾取和预测这些性能指标之前由机器人测量。最后，我们展示了在模拟对象上的抓取与真实对象上的抓取之间的良好对应。 摘要：Robotic grasping of 3D deformable objects (e.g., fruits/vegetables, internal organs, bottles/boxes) is critical for real-world applications such as food processing, robotic surgery, and household automation. However, developing grasp strategies for such objects is uniquely challenging. In this work, we efficiently simulate grasps on a wide range of 3D deformable objects using a GPU-based implementation of the corotational finite element method (FEM). To facilitate future research, we open-source our simulated dataset (34 objects, 1e5 Pa elasticity range, 6800 grasp evaluations, 1.1M grasp measurements), as well as a code repository that allows researchers to run our full FEM-based grasp evaluation pipeline on arbitrary 3D object models of their choice. We also provide a detailed analysis on 6 object primitives. For each primitive, we methodically describe the effects of different grasp strategies, compute a set of performance metrics (e.g., deformation, stress) that fully capture the object response, and identify simple grasp features (e.g., gripper displacement, contact area) measurable by robots prior to pickup and predictive of these performance metrics. Finally, we demonstrate good correspondence between grasps on simulated objects and their real-world counterparts.

【16】 Evaluation of an Inflated Beam Model Applied to Everted Tubes 标题：一种适用于外翻管的充气梁模型的评价

作者：Joel Hwee,Andrew Lewis,Allison Raines,Blake Hannaford 链接：https://arxiv.org/abs/2107.05748 摘要：外翻管通常被建模为膨胀梁，以确定横向和轴向屈曲条件。本文旨在验证外翻管可以用这种方法建模的假设。将外翻梁和非外翻梁在横向悬臂荷载作用下的端部挠度与首次为航空航天应用开发的端部挠度模型进行了比较。测试了LDPE和有机硅涂层尼龙梁；外翻和非外翻梁显示出类似的尖端偏转。文献模型最适合LDPE管的端部挠度，平均端部挠度误差为6mm，而尼龙管的平均端部挠度误差为16.4mm。两种材料的外翻梁在83%的理论屈曲条件下发生屈曲，而直梁在109%的理论屈曲条件下发生屈曲。根据端部载荷和已知位移估算了外翻梁的曲率，LDPE梁和尼龙梁的相对误差分别为14.2%和17.3%。本文给出了一种确定膨胀梁挠度的数值方法。它还提供了一种迭代方法来计算静态尖端姿态和在已知环境中施加的壁力。 摘要：Everted tubes have often been modeled as inflated beams to determine transverse and axial buckling conditions. This paper seeks to validate the assumption that an everted tube can be modeled in this way. The tip deflections of everted and uneverted beams under transverse cantilever loads are compared with a tip deflection model that was first developed for aerospace applications. LDPE and silicone coated nylon beams were tested; everted and uneverted beams showed similar tip deflection. The literature model best fit the tip deflection of LDPE tubes with an average tip deflection error of 6 mm, while the nylon tubes had an average tip deflection error of 16.4 mm. Everted beams of both materials buckled at 83% of the theoretical buckling condition while straight beams collapsed at 109% of the theoretical buckling condition. The curvature of everted beams was estimated from a tip load and a known displacement showing relative errors of 14.2% and 17.3% for LDPE and nylon beams respectively. This paper shows a numerical method for determining inflated beam deflection. It also provides an iterative method for computing static tip pose and applied wall forces in a known environment.

【17】 Altruistic Maneuver Planning for Cooperative Autonomous Vehicles Using Multi-agent Advantage Actor-Critic 标题：基于多智能体优势主体-批评者的协作自主车辆利他主义机动规划

作者：Behrad Toghi,Rodolfo Valiente,Dorsa Sadigh,Ramtin Pedarsani,Yaser P. Fallah 备注：Accepted to 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021) - Workshop on Autonomous Driving: Perception, Prediction and Planning 链接：https://arxiv.org/abs/2107.05664 摘要：随着自动驾驶汽车在我们的道路上的采用，我们将见证一个混合的自主环境，在这个环境中，自动驾驶汽车和人类驾驶汽车必须学会通过共享相同的道路基础设施而共存。为了达到社会期望的行为，自主车辆必须被指导考虑在他们的决策过程中周围的其他车辆的效用。特别地，我们研究了自主车辆的机动规划问题，并研究了分散的奖励结构如何在其行为中诱导利他主义并激励他们考虑其他自主车辆和人类驾驶车辆的利益。这是一个具有挑战性的问题，因为人类驾驶员与自主车辆合作的意愿不明确。因此，与现有的依赖于驾驶员行为模型的研究相比，本文采用了端到端的方法，让自主代理仅从经验中隐式地学习驾驶员的决策过程。我们引入了一种多智能体的同步优势-行动者-批评家（A2C）算法，并训练了相互协调的智能体，这些智能体可以影响人类驾驶员的行为，从而改善交通流和安全。 摘要：With the adoption of autonomous vehicles on our roads, we will witness a mixed-autonomy environment where autonomous and human-driven vehicles must learn to co-exist by sharing the same road infrastructure. To attain socially-desirable behaviors, autonomous vehicles must be instructed to consider the utility of other vehicles around them in their decision-making process. Particularly, we study the maneuver planning problem for autonomous vehicles and investigate how a decentralized reward structure can induce altruism in their behavior and incentivize them to account for the interest of other autonomous and human-driven vehicles. This is a challenging problem due to the ambiguity of a human driver's willingness to cooperate with an autonomous vehicle. Thus, in contrast with the existing works which rely on behavior models of human drivers, we take an end-to-end approach and let the autonomous agents to implicitly learn the decision-making process of human drivers only from experience. We introduce a multi-agent variant of the synchronous Advantage Actor-Critic (A2C) algorithm and train agents that coordinate with each other and can affect the behavior of human drivers to improve traffic flow and safety.

【18】 Raspberry PI for compact autonomous home farm control 标题：用于紧凑型自主家庭农场控制的树莓PI

作者：R. Ildar 机构： PhD Department of Power Plants Networks and Systems, South Ural State University ildar 链接：https://arxiv.org/abs/2107.06180 摘要：这篇手稿介绍了一个用于预测计量特性的自主家庭农场，它不仅可以自动化作物生长过程，而且由于神经网络，还可以显著提高农场的生产率。发达的农场监测和管理以下指标：光照、土壤PH值、气温、地温、空气湿度、CO2浓度和土壤湿度。所提出的农场也可以被视为一个设备，用于测试各种天气条件，以确定不同作物的最佳温度特性。因此，这个农场是完全自主的，在家里种植西红柿。 摘要：This manuscript presented an autonomous home farm for predicting metrological characteristics that can not only automate the process of growing crops but also, due to a neural network, significantly increase the productivity of the farm. The developed farm monitors and manages the following indicators: illumination, soil PH, air temperature, ground temperature, air humidity, CO2 concentration, and soil moisture. The presented farm can also be considered as a device for testing various weather conditions to determine the optimal temperature characteristics for different crops. This farm as a result is completely autonomous grows tomatoes at home.