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cs.RO機器人相關，共計18篇
【1】 OpenCDA:An Open Cooperative Driving Automation FrameworkIntegrated with Co-Simulation
标題：OpenCDA：一種內建協同仿真的開放式協同駕駛自動化架構
作者：Runsheng Xu,Yi Guo,Xu Han,Xin Xia,Hao Xiang,Jiaqi Ma
機構： University of California, Los Angeles (UCLA) 2Yi Guo is with University of Cincinnati
備注：None
連結：https://arxiv.org/abs/2107.06260
摘要：盡管協同駕駛自動化（CDA）近年來受到了廣泛的關注，但在這一領域仍然存在許多挑戰。現有的主要集中于單車智能的仿真平台與CDA開發之間的差距是關鍵障礙之一，因為它阻礙了研究人員友善地驗證和比較不同的CDA算法。為此，我們提出了OpenCDA，一個開發和測試CDA系統的通用架構和工具。具體來說，OpenCDA由三個主要元件組成：一個具有不同用途和分辨率的模拟器的協同仿真平台、一個全棧協同驅動系統和一個場景管理器。通過這三個元件的互動作用，我們的架構為研究人員提供了一種簡單的方法，可以在流量和個體自治兩個層次上測試不同的CDA算法。更重要的是，OpenCDA是高度子產品化的，并且安裝了基準算法和測試用例。使用者可以友善地用定制算法替換任何預設子產品，并使用CDA平台的其他預設子產品來評估新功能在增強CDA整體性能方面的有效性。通過一個排程實作的例子說明了該架構對CDA研究的能力。OpenCDA的代碼在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
标題：基于街道圖像的目标跟蹤與地理定位
作者：Daniel Wilson,Thayer Alshaabi,Colin Van Oort,Xiaohan Zhang,Jonathan Nelson,Safwan Wshah
機構：• 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
備注：28 pages, 7 figures, to be submitted to Elsevier Pattern Recognition
連結：https://arxiv.org/abs/2107.06257
摘要：從街道圖像中對靜态物體進行地理定位是一項挑戰，但對于道路資源測繪和自動駕駛也非常重要。在本文中，我們提出了一個兩階段的架構，檢測和地理定位交通标志從低幀速率街道視訊。我們提出的系統使用了一種改進的視網膜網（GPS-RetinaNet），除了執行标準分類和邊界盒回歸外，還可以預測每個标志相對于相機的位置偏移。我們的自定義跟蹤器由學習的度量網絡和匈牙利算法的變體組成，将GPS視網膜網中的候選符号檢測濃縮為地理定位符号。我們的度量網絡估計檢測對之間的相似性，然後匈牙利算法使用度量網絡提供的相似性分數比對圖像中的檢測。我們的模型是使用更新版本的ARTS資料集訓練的，該資料集包含25544幅圖像和47.589個符号注釋~\cite{ARTS}。拟議的資料集涵蓋了從廣泛的道路選擇中收集的各種環境。每個注釋都包含一個标志類标簽、其地理空間位置、裝配标簽、路側訓示器，以及有助于評估的唯一辨別符。該資料集将支援該領域的未來進展，并且所提出的系統示範了如何利用真實地理定位資料集的一些獨特特性。
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
标題：每個人都是獨一無二的：走向不偏不倚的人脈恢複
作者：Ren Li,Meng Zheng,Srikrishna Karanam,Terrence Chen,Ziyan Wu
機構：United Imaging Intelligence, Cambridge MA
備注：10 pages, 5 figures, 4 tables
連結：https://arxiv.org/abs/2107.06239
摘要：我們考慮肥胖人網格恢複的問題，即，将參數人類網格拟合到肥胖人群的圖像。盡管肥胖者的網格拟合是許多應用（如醫療保健）中的一個重要問題，但網格恢複方面的許多最新進展僅限于非肥胖者的圖像。在這項工作中，我們通過介紹和讨論現有算法的局限性，找出了目前文獻中的這一關鍵差距。接下來，我們将提供一個簡單的基線來解決這個問題，它是可伸縮的，并且可以很容易地與現有算法結合使用，以提高它們的性能。最後，我們提出了一個廣義人體網格優化算法，大大提高了現有方法在肥胖者圖像和社群标準基準資料集上的性能。該技術的一個關鍵創新是，它不依賴于昂貴的監視來建立網格參數。取而代之的是，從廣泛和廉價的二維關鍵點注釋開始，我們的方法自動生成網格參數，這些參數可以用來重新訓練和微調任何現有的網格估計算法。通過這種方式，我們展示了我們的方法作為一個下降，以提高性能的各種當代網格估計方法。我們進行了廣泛的實驗，在多個資料集，包括标準和肥胖的人的圖像，并證明了我們提出的技術的有效性。
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
标題：高速機器人曲棍球的高效反應性規劃
作者：Puze Liu,Davide Tateo,Haitham Bou-Ammar,Jan Peters
備注：IEEE/RJS International Conference on Intelligent RObots and Systems (IROS)
連結：https://arxiv.org/abs/2107.06140
摘要：高度動态的機器人任務需要高速反應的機器人。由于實體限制、硬體限制以及動力學和傳感器測量的高度不确定性，這些任務尤其具有挑戰性。為了面對這些問題，設計出精确快速的軌迹并對環境變化做出快速反應的機器人代理至關重要。空中曲棍球就是這種任務的一個例子。由于環境的特點，将問題形式化并導出清晰的數學解是可能的。基于這些原因，這種環境非常适合将目前可用的通用機械手的性能推向極限。利用兩個Kuka-iiwa14，我們展示了如何設計一個用于空中曲棍球遊戲的通用機械手政策。我們證明了一個真實的機器人手臂可以進行快速的擊球動作，并且兩個機器人可以在一個中型的空中曲棍球台上進行對抗。
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
标題：一種新穎的基于對偶四元數的雜技飛行動态運動基元
作者：Renshan Zhang,Yongyang Hu,Kuang Zhao,Su Cao
機構：Nanjing Telecommunication Technology Research Institute, Nanjing, China, Institute of Unmanned Systems, National University of Defense Technology, Changsha, China
備注：6 pages
連結：https://arxiv.org/abs/2107.06116
摘要：對于固定翼無人機（UAV）的雜技飛行來說，由于平移旋轉運動固有的耦合問題，運動描述的實作是一項具有挑戰性的工作。本文利用模拟學習的思想，提出了一種新的機動描述方法，主要貢獻有兩個方面：1）提出了一種基于雙四元數的動态運動原語（DQ-DMP），在不損失精度的前提下，将位置和姿态的狀态方程結合起來。2） 建立了線上半實物訓練系統。基于DQDMP方法，可以實時獲得示範機動的幾何特征，并從理論上證明了DQ-DMP方法的穩定性。仿真結果表明，與傳統的位姿解耦方法相比，該方法具有明顯的優越性。
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
标題：教Agent如何繪制地圖：多目标導航的空間推理
作者：Pierre Marza,Laetitia Matignon,Olivier Simonin,Christian Wolf
機構： LIRIS, UMR CNRS , Université de Lyon, INSA Lyon, Villeurbanne, France, Université de Lyon, Univ. Lyon , CITI Lab, INRIA Chroma team
連結：https://arxiv.org/abs/2107.06011
摘要：在視覺導航的背景下，為了使agent能夠在所考慮的地點利用其觀察曆史并有效地達到已知的目标，繪制一個新環境的能力是必要的。這種能力可以與空間推理相聯系，在空間推理中，智能體能夠感覺空間關系和規律，并發現對象的啟示。在經典的強化學習（RL）設定中，這種能力僅從獎勵中學習。我們引入了輔助任務形式的輔助監督，旨在幫助為達到下遊目标而訓練的代理出現空間感覺能力。我們發現，學習估計量化給定位置的代理和目标之間的空間關系的度量在多目标導航設定中具有很高的積極影響。我們的方法顯著提高了不同基線代理的性能，這些代理可以建構環境的顯式或隐式表示，甚至可以比對以地面真值圖作為輸入的不可比較的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
标題：運動感覺機器人三維超聲
作者：Zhongliang Jiang,Hanyu Wang,Zhenyu Li,Matthias Grimm,Mingchuan Zhou,Ulrich Eck,Sandra V. Brecht,Tim C. Lueth,Thomas Wendler,Nassir Navab
機構： Lueth are with the Institute of Micro Technologyand Medical Device Technology, Technical University of Munich
備注：Accepted to ICRA2021
連結：https://arxiv.org/abs/2107.05998
摘要：機器人三維超聲成像（3D）已被用來克服傳統超聲檢查的缺點，如高操作間的可變性和缺乏可重複性。然而，物體運動仍然是一個挑戰，因為意外的運動會降低三維合成的品質。此外，傳統的機器人US系統不允許嘗試調整對象，例如調整肢體以顯示整個肢體動脈樹。為了應對這一挑戰，我們提出了一種基于視覺的機器人超聲系統，該系統能夠監測物體的運動，并自動更新掃描軌迹，無縫地提供目标解剖結構的三維複合圖像。為了實作這些功能，使用深度相機提取手動規劃的掃描軌迹，然後利用提取的三維軌迹估計物體的法向。随後，為了監控運動并進一步補償這種運動以精确跟蹤軌迹，實時跟蹤固定被動标記的位置。最後，進行分步複合。在凝膠體模上的實驗表明，在掃描過程中，當物體不靜止時，系統可以恢複掃描。
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
标題：多目标圖啟發式搜尋在地面機器人設計中的應用
作者：Jie Xu,Andrew Spielberg,Allan Zhao,Daniela Rus,Wojciech Matusik
機構：MassachusettsInstituteofTechnology
備注：IEEE International Conference on Robotics and Automation (ICRA 2021)
連結：https://arxiv.org/abs/2107.05858
摘要：提出了一種基于控制和形态學（包括離散拓撲）的多目标剛性機器人協同設計方法。以往的工作都是針對單目标機器人協同設計或多目标控制的問題。然而，關節多目标協同設計問題對于生成功能強大、通用性強、算法設計簡單的機器人是非常重要的。在這項工作中，我們提出了多目标圖啟發式搜尋，它擴充了單目标圖啟發式搜尋，使一個高效的多目标搜尋組合設計拓撲空間。該方法的核心是引入一種新的基于圖神經網絡的通用多目标啟發式函數，能夠有效地利用不同任務之間的學習資訊。我們在七個地面運動和設計任務的六個組合上展示了我們的方法，包括一個三目标的例子。我們比較了不同方法捕獲的Pareto前沿，并證明了我們的多目标圖啟發式搜尋在數量和品質上都優于其他方法。
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
标題：基于二維地理參考圖的無人機精确視覺慣性定位
作者：Jun Mao,Lilian Zhang,Xiaofeng He,Hao Qu,Xiaoping Hu
機構： National University of Defense Technology
連結：https://arxiv.org/abs/2107.05851
摘要：精确的地理定位是無人機的關鍵。然而，目前部署的無人機大多依賴全球導航衛星系統（GNSS）或高精度慣性導航系統（INS）進行地理定位。在本文中，我們建議使用一個輕量級的視覺慣性系統和一個二維的地理參考地圖，以獲得準确和連續的無人機大地測量位置。該系統首先利用微型慣性測量單元（MIMU）和單目錄影機作為裡程計，在局部世界架構内連續估計導航狀态，重建觀測到的視覺特征的三維位置。為了獲得地理位置，通過裡程計跟蹤的視覺特征被進一步注冊到二維地理參考地圖上。在傳統的航空影像配準方法中，我們提出将重建點與大地坐标系中的地圖點對齊；這有助于過濾掉大部分離群值，并從水準角度解耦負面影響。然後使用注冊點在大地坐标系中重新定位車輛。最後，利用位姿圖融合航空圖像配準的定位結果和視覺慣性裡程計（VIO）的局部導航結果，實作連續無漂移的定位性能。通過将傳感器剛性安裝在無人機機身上，并在不同環境下進行了兩次未知初始值的飛行試驗，驗證了該方法的有效性。結果表明，該方法在100m高度飛行時可獲得小于4m的位置誤差，在300m高度飛行時可獲得小于9m的位置誤差。
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
标題：軌迹優化中基于解流形學習的運動規劃
作者：Takayuki Osa
機構： 1Kyushu Institute of Technology, Kyushu Institute of Technology Department of HumanIntelligence Systems & Research Center for Neuromorphic AI HardwareBehavior Learning Systems Loboratory
備注：24 pages, to appear in the International Journal of Robotics Research
連結：https://arxiv.org/abs/2107.05842
摘要：軌迹優化中使用的目标函數通常是非凸的，可以有無窮多個局部最優解。在這種情況下，有不同的解決方案來執行給定的任務。雖然有一些方法可以找到運動規劃的多個解決方案，但它們僅限于生成一組有限的解決方案。為了解決這個問題，我們提出了一種優化方法，學習無窮多個解決方案的軌迹優化。在我們的架構中，通過學習解的潛在表示來獲得不同的解。我們的方法可以解釋為訓練一個深層的無碰撞軌迹生成模型來進行運動規劃。實驗結果表明，訓練後的模型代表了運動規劃問題的無窮多個同倫解。
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
标題：部分受控環境下逆RL的分層貝葉斯模型
作者：Kenneth Bogert,Prashant Doshi
機構： other 1Kenneth Bogert is with Department of Computer Science, University ofNorth Carolina, University of Georgia
備注：8 pages, 10 figures
連結：https://arxiv.org/abs/2107.05818
摘要：在真實世界中，使用逆強化學習（IRL）從觀察中學習的機器人在示範過程中可能會遇到環境中的物體或代理，而不是專家。在完全受控的環境（如虛拟仿真或實驗室設定）中，這些混雜元素通常會被移除。當無法完全清除時，必須過濾掉有害的觀察結果。然而，在進行大量觀測時，很難确定觀測的來源。為了解決這個問題，我們提出了一個分層貝葉斯模型，它結合了專家和混雜元素的觀察結果，進而明确地為機器人可能接收到的各種觀察結果模組化。我們擴充現有的ILL算法最初設計工作在部分遮擋的專家考慮不同的意見。在一個包含遮擋和混雜元素的模拟機器人排序域中，我們證明了該模型的有效性。特别是，我們的技術優于其他幾種比較方法，僅次于對受試者軌迹的完美了解。
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
标題：用于自動駕駛的反應式規劃器和控制器的安全和進度證明
作者：Abolfazl Karimi,Manish Goyal,Parasara Sridhar Duggirala
機構：Department of Computer Science, University of North Carolina, Chapel Hill, United States
連結：https://arxiv.org/abs/2107.05815
摘要：在本文中，我們進行了安全性和性能分析的自主車輛，實作反應式規劃和控制器導航一圈比賽。與能夠通路環境地圖的傳統規劃算法不同，反應式規劃器僅基于傳感器的目前輸入生成規劃。我們的反應式計劃者在本地Voronoi圖上選擇一個航路點，我們使用一個純追蹤控制器導航到該航路點。我們的安全性和性能分析分為兩部分。第一部分證明了反應式規劃器計算的規劃與用全映射計算的Voronoi規劃是局部一緻的。第二部分将車輛沿Voronoi圖導航的演化模組化為一個混合自動機。為了證明該混合自動機的安全性和性能名額，我們計算了該混合自動機的可達集，并對其進行了改進，使其計算更加容易。我們證明了一個自主車輛實作我們的反應式規劃和控制器是安全的，并成功地完成了一圈五個不同的電路。此外，我們在模拟環境中以及在小型自主車輛上實作了我們的規劃器和控制器，并證明了我們的規劃器在各種電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
标題：固定翼飛機平穩着陸軌迹跟蹤系統設計
作者：Solomon Gudeta,Ali Karimoddini
備注：6 pages, 9 figures, American Control Conference
連結：https://arxiv.org/abs/2107.05803
摘要：本文提出了一種固定翼飛機在着陸階段的着陸控制器，以保證飛機平穩到達着陸點。将着陸問題轉化為有限時間線性二次跟蹤（LQT）問題，即飛機在滿足性能要求和飛行限制的前提下，在縱向垂直面上跟蹤所需的着陸路徑。首先，我們設計一個滿足飛行性能要求和限制的平滑軌迹。然後，設計了一個最優控制器，使飛機在期望時間内着陸時跟蹤誤差最小化。為此，在小航迹角和恒定進近速度的假設下建立了飛機的線性化模型。由此産生的微分Riccati方程求解時間向後使用休眠王子算法。仿真結果表明，在不同初始條件下，系統具有良好的跟蹤性能和跟蹤誤差的有限時間收斂性。
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
标題：KIT-Net：将新的3D對象裝入新的3D腔的自監督學習
作者：Shivin Devgon,Jeffrey Ichnowski,Michael Danielczuk,Daniel S. Brown,Ashwin Balakrishna,Shirin Joshi,Eduardo M. C. Rocha,Eugen Solowjow,Ken Goldberg
機構： 1TheAUTOLABattheUniversityofCalifornia
備注：None
連結：https://arxiv.org/abs/2107.05789
摘要：在工業零件裝配中，三維物體被插入型腔中進行運輸或後續裝配。配套是一個關鍵的步驟，因為它可以減少下遊加工和處理時間，并使較低的存儲和運輸成本。我們提出了Kit-Net，一個架構，用于将以前看不見的三維物體裝配成空腔，給出目标空腔和一個物體在未知初始方向上被夾鉗夾住的深度圖像。Kit-Net采用自監督深度學習和資料增強的方法訓練卷積神經網絡（CNN），利用模拟深度圖像對的大型訓練資料集，魯棒地估計物體之間的三維旋轉，并比對凹腔或凸腔。然後，Kit-Net使用訓練好的CNN來實作一個控制器來定位和定位新的物體，以便插入到新的棱柱形和共形三維腔中。仿真實驗表明，Kit網能使目标網格與目标空腔的平均相交體積達到98.9%。用工業物體進行的實體實驗在使用基線方法的試驗中成功率為18%，在使用Kit-Net的試驗中成功率為63%。視訊、代碼和資料可在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
标題：DefGraspSim：基于仿真的三維可變形物體抓取
作者：Isabella Huang,Yashraj Narang,Clemens Eppner,Balakumar Sundaralingam,Miles Macklin,Tucker Hermans,Dieter Fox
機構： USA; 3School ofComputing, University of Utah, Allen Schoolof Computer Science & Engineering, University of Washington
備注：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
連結：https://arxiv.org/abs/2107.05778
摘要：機器人抓取三維可變形物體（如水果/蔬菜、内髒、瓶子/盒子）對于食品加工、機器人手術和家庭自動化等實際應用至關重要。然而，為這些物體開發抓取政策是一個獨特的挑戰。在這項工作中，我們使用基于GPU的共旋轉有限元法（FEM）來有效地模拟對廣泛的3D可變形物體的抓取。為了便于将來的研究，我們開放了我們的模拟資料集（34個對象，1e5 Pa彈性範圍，6800個抓握評估，1.1M抓握測量），以及一個代碼庫，允許研究人員在他們選擇的任意三維對象模型上運作我們基于FEM的完整抓握評估管道。我們還對6個對象原語進行了詳細的分析。對于每個原語，我們系統地描述不同抓取政策的效果，計算一組性能名額（例如，變形、應力），以充分捕捉對象響應，并識别簡單的抓取特征（例如，夾持器位移，接觸面積）在拾取和預測這些性能名額之前由機器人測量。最後，我們展示了在模拟對象上的抓取與真實對象上的抓取之間的良好對應。
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
标題：一種适用于外翻管的充氣梁模型的評價
作者：Joel Hwee,Andrew Lewis,Allison Raines,Blake Hannaford
連結：https://arxiv.org/abs/2107.05748
摘要：外翻管通常被模組化為膨脹梁，以确定橫向和軸向屈曲條件。本文旨在驗證外翻管可以用這種方法模組化的假設。将外翻梁和非外翻梁在橫向懸臂荷載作用下的端部撓度與首次為航空航天應用開發的端部撓度模型進行了比較。測試了LDPE和有機矽塗層尼龍梁；外翻和非外翻梁顯示出類似的尖端偏轉。文獻模型最适合LDPE管的端部撓度，平均端部撓度誤差為6mm，而尼龍管的平均端部撓度誤差為16.4mm。兩種材料的外翻梁在83%的理論屈曲條件下發生屈曲，而直梁在109%的理論屈曲條件下發生屈曲。根據端部載荷和已知位移估算了外翻梁的曲率，LDPE梁和尼龍梁的相對誤差分别為14.2%和17.3%。本文給出了一種确定膨脹梁撓度的數值方法。它還提供了一種疊代方法來計算靜态尖端姿态和在已知環境中施加的壁力。
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
标題：基于多智能體優勢主體-批評者的協作自主車輛利他主義機動規劃
作者：Behrad Toghi,Rodolfo Valiente,Dorsa Sadigh,Ramtin Pedarsani,Yaser P. Fallah
備注：Accepted to 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021) - Workshop on Autonomous Driving: Perception, Prediction and Planning
連結：https://arxiv.org/abs/2107.05664
摘要：随着自動駕駛汽車在我們的道路上的采用，我們将見證一個混合的自主環境，在這個環境中，自動駕駛汽車和人類駕駛汽車必須學會通過共享相同的道路基礎設施而共存。為了達到社會期望的行為，自主車輛必須被指導考慮在他們的決策過程中周圍的其他車輛的效用。特别地，我們研究了自主車輛的機動規劃問題，并研究了分散的獎勵結構如何在其行為中誘導利他主義并激勵他們考慮其他自主車輛和人類駕駛車輛的利益。這是一個具有挑戰性的問題，因為人類駕駛員與自主車輛合作的意願不明确。是以，與現有的依賴于駕駛員行為模型的研究相比，本文采用了端到端的方法，讓自主代理僅從經驗中隐式地學習駕駛員的決策過程。我們引入了一種多智能體的同步優勢-行動者-批評家（A2C）算法，并訓練了互相協調的智能體，這些智能體可以影響人類駕駛員的行為，進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
标題：用于緊湊型自主家庭農場控制的樹莓PI
作者：R. Ildar
機構： PhD Department of Power Plants Networks and Systems, South Ural State University ildar
連結：https://arxiv.org/abs/2107.06180
摘要：這篇手稿介紹了一個用于預測計量特性的自主家庭農場，它不僅可以自動化作物生長過程，而且由于神經網絡，還可以顯著提高農場的生産率。發達的農場監測和管理以下名額：光照、土壤PH值、氣溫、地溫、空氣濕度、CO2濃度和土壤濕度。所提出的農場也可以被視為一個裝置，用于測試各種天氣條件，以确定不同作物的最佳溫度特性。是以，這個農場是完全自主的，在家裡種植蕃茄。
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.