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cs.RO机器人相关，共计18篇
【1】 Automatic Centralized Control of Underactuated Large-scale Mutli-robot Systems using a Generalized Coordinate Transformation 标题：基于广义坐标变换的欠驱动多机器人系统自动集中控制 链接：https://arxiv.org/abs/2108.10153
作者：Babak Salamat,Christopher Johannes Starck,Heiko Hamann 机构：Heiko Hamann is with University of Luebeck, Institute of ComputerEngineering 摘要：控制大规模粒子或机器人系统是一项具有挑战性的任务，因为它们具有高维性。我们使用一种集中的随机方法，它允许以一个中心元素为代价进行最优控制，而不是分散的方法。以前的工作通常局限于全驱动机器人的假设。在这里，我们提出了一种欠驱动机器人的方法，允许对机器人系统进行节能控制。我们考虑一个简单的任务，收集机器人（最小化位置方差），并将它们转向有界区域内的目标点，无障碍。我们有两个主要贡献。首先，我们提出了欠驱动机器人的广义坐标变换，它的物理性质需要考虑。我们选择描述一大类机器人系统的欧拉-拉格朗日系统。其次，我们提出了一种以能源效率为主要目标的最优控制机制。我们在数值仿真和机器人仿真中证明了该方法的可行性。 摘要：Controlling large-scale particle or robot systems is challenging because of their high dimensionality. We use a centralized stochastic approach that allows for optimal control at the cost of a central element instead of a decentralized approach. Previous works are often restricted to the assumption of fully actuated robots. Here we propose an approach for underactuated robots that allows for energy-efficient control of the robot system. We consider a simple task of gathering the robots (minimizing positional variance) and steering them towards a goal point within a bounded area without obstacles. We make two main contributions. First, we present a generalized coordinate transformation for underactuated robots, whose physical properties should be considered. We choose Euler- Lagrange systems that describe a large class of robot systems. Second, we propose an optimal control mechanism with the prime objective of energy efficiency. We show the feasibility of our approach in numerical simulations and robot simulations.
【2】 Indoor Path Planning for an Unmanned Aerial Vehicle via Curriculum Learning 标题：基于课程学习的无人机室内路径规划 链接：https://arxiv.org/abs/2108.09986
作者：Jongmin Park,Sooyoung Jang,Younghoon Shin 机构：School of Integrated Technology, Yonsei University, Electronics and Telecommunications Research Institute, ∗ Corresponding author 备注：Submitted to ICCAS 2021 摘要：在本研究中，强化学习被应用于室内环境中的二维路径规划学习，包括无人机（UAV）的避障。分配给无人机的任务是在最短的时间内到达目标位置，而不与任何障碍物发生碰撞。强化学习是在使用Gazebo（虚拟环境模拟器）创建的虚拟环境中进行的，以减少学习时间和成本。课程学习分为两个阶段，旨在提高学习效率。通过两种奖励模式的学习，获得的最高目标率分别为71.2%和88.0%。 摘要：In this study, reinforcement learning was applied to learning two-dimensional path planning including obstacle avoidance by unmanned aerial vehicle (UAV) in an indoor environment. The task assigned to the UAV was to reach the goal position in the shortest amount of time without colliding with any obstacles. Reinforcement learning was performed in a virtual environment created using Gazebo, a virtual environment simulator, to reduce the learning time and cost. Curriculum learning, which consists of two stages was performed for more efficient learning. As a result of learning with two reward models, the maximum goal rates achieved were 71.2% and 88.0%.
【3】 Burst Imaging for Light-Constrained Structure-From-Motion 标题：光约束结构运动的猝发成像 链接：https://arxiv.org/abs/2108.09895
作者：Ahalya Ravendran,Mitch Bryson,Donald G. Dansereau 机构：School of Aerospace, The Uni-versity of Sydney and with the Sydney Institute for Robotics and Intelli-gent Systems 备注：8 pages, 8 figures, 2 tables, for associated project page, see: this https URL 摘要：在极低光照条件下拍摄的图像受噪声限制，这可能导致现有的机器人视觉算法失败。在本文中，我们开发了一种图像处理技术，用于帮助在弱光条件下获取的图像进行三维重建。我们的技术基于突发摄影，使用直接方法在短曝光时间图像的突发内进行图像配准，以提高基于特征的运动结构（SfM）的鲁棒性和准确性。我们在具有挑战性的光约束场景中展示了改进的SfM性能，包括显示改进的特征性能和相机姿势估计的定量评估。此外，我们还表明，我们的方法比最先进的方法更容易收敛到正确的重建。我们的方法是朝着允许机器人在弱光条件下工作迈出的重要一步，有可能应用于在地下矿山和夜间作业等环境中工作的机器人。 摘要：Images captured under extremely low light conditions are noise-limited, which can cause existing robotic vision algorithms to fail. In this paper we develop an image processing technique for aiding 3D reconstruction from images acquired in low light conditions. Our technique, based on burst photography, uses direct methods for image registration within bursts of short exposure time images to improve the robustness and accuracy of feature-based structure-from-motion (SfM). We demonstrate improved SfM performance in challenging light-constrained scenes, including quantitative evaluations that show improved feature performance and camera pose estimates. Additionally, we show that our method converges more frequently to correct reconstructions than the state-of-the-art. Our method is a significant step towards allowing robots to operate in low light conditions, with potential applications to robots operating in environments such as underground mines and night time operation.
【4】 CoMet: Modeling Group Cohesion for Socially Compliant Robot Navigation in Crowded Scenes 标题：Comet：拥挤场景中社会顺应型机器人导航的群体凝聚力建模 链接：https://arxiv.org/abs/2108.09848
作者：Adarsh Jagan Sathyamoorthy,Utsav Patel,Moumita Paul,Nithish K Sanjeev Kumar,Yash Savle,Dinesh Manocha 备注：10 pages, 6 figures 摘要：我们提出CoMet，一种计算群体凝聚力的新方法，并使用它来改善机器人在拥挤场景中的导航。我们的方法使用了一种新的内聚度量，它建立在社会心理学先前工作的基础上。我们通过利用机器人上的RGB-D摄像机中行人的各种视觉特征来计算该度量。具体地说，我们检测与人与人之间的接近程度、他们的相对行走速度、群体规模以及群体成员之间的互动相对应的特征。我们使用我们的凝聚力指标来设计和改进导航方案，该方案考虑了机器人在人群中移动时不同级别的群体凝聚力。我们基于感知评估来评估内聚度量的精确度和召回率。我们重点介绍了我们的社交导航算法在Turtlebot机器人上的性能，并展示了它在多个指标方面的优势：冻结率（减少57%）、低偏差（减少35.7%）、轨迹路径长度（减少23.2%）。 摘要：We present CoMet, a novel approach for computing a group's cohesion and using that to improve a robot's navigation in crowded scenes. Our approach uses a novel cohesion-metric that builds on prior work in social psychology. We compute this metric by utilizing various visual features of pedestrians from an RGB-D camera on-board a robot. Specifically, we detect characteristics corresponding to the proximity between people, their relative walking speeds, the group size, and interactions between group members. We use our cohesion-metric to design and improve a navigation scheme that accounts for different levels of group cohesion while a robot moves through a crowd. We evaluate the precision and recall of our cohesion-metric based on perceptual evaluations. We highlight the performance of our social navigation algorithm on a Turtlebot robot and demonstrate its benefits in terms of multiple metrics: freezing rate (57% decrease), low deviation (35.7% decrease), path length of the trajectory(23.2% decrease).
【5】 Angular Velocity Estimation using Non-coplanar Accelerometer Array 标题：基于非共面加速度计阵列的角速度估计 链接：https://arxiv.org/abs/2108.09834
作者：Michael Maynard,Vishesh Vikas 机构：University of Alabama 备注：8 pages, IEEE Sensors Journal 摘要：在过去的几十年中，为了克服陀螺的局限性，无陀螺惯性测量单元得到了广泛的研究。本研究提出了一种非共面加速度计阵列（NAA），用于估算具有非共面约束的四个或更多三轴加速度计的非特定几何排列的角速度。提出的非共面空间排列的证明也提供了对传感器噪声传播和噪声协方差矩阵构造的见解。系统噪声取决于（传感器之间）相对位移矩阵的奇异值。提出了一个过程和测量噪声不相关的动态系统模型，其中加速度计读数同时作为过程和测量输入。角速度估计使用EKF离散和线性化连续离散时间动力系统。模拟是在立方体NAA（Cu NAA）上进行的，该立方体NAA包括放置在立方体不同顶点的四个加速度计。他们分析了加速度计之间距离变化时静态和动态运动的估计误差。在这里，观察到系统噪声随着立方体边缘的长度呈反比下降，因为排列保持不变。因此，仿真结果表明，估计的标准误差随边缘长度逐渐减小。实验是在具有五个反射光学标记的Cu-NAA上进行的。使用维康视觉跟踪反射标记以构建地面真相。这种独特的实验装置，除了提供三个自由度的旋转运动外，还允许三个自由度的空间平移（Cu NAA在空间中的线性加速度）。仿真和实验结果表明，与具有相关过程和测量噪声的EKF相比，该EKF具有更好的性能。 摘要：Over the last few decades, Gyro-Free IMUs have been extensively researched to overcome the limitations of gyroscopes. This research presents a Non-coplanar Accelerometer Array (NAA) for estimating angular velocity with non-specific geometric arrangement of four or more triaxial accelerometers with non-coplanarity constraint. The presented proof of non-coplanar spacial arrangement also provides insights into propagation of the sensor noise and construction of the noise covariance matrices. The system noise depends on the singular values of the relative displacement matrix (between the sensors). A dynamical system model with uncorrelated process and measurement noise is proposed where the accelerometer readings are used simultaneously as process and measurement inputs. The angular velocity is estimated using an EKF that discretizes and linearizes the continuous-discrete time dynamical system. The simulations are performed on a Cube-NAA (Cu-NAA) comprising four accelerometers placed at different vertices of a cube. They analyze the estimation error for static and dynamic movement as the distance between the accelerometers is varied. Here, the system noise is observed to decrease inversely with the length of the cube edge as the arrangement is kept identical. Consequently, the simulation results indicate asymptotic decrease in the standard error of estimation with edge length. The experiments are conducted on a Cu-NAA with five reflective optical markers. The reflective markers are visually tracked using VICON to construct the ground truth. This unique experimental setup, apart from providing three degrees of rotational freedom of movement, also allows for three degrees of spacial translation (linear acceleration of the Cu-NAA in space). The simulation and experimental results indicate better performance of the proposed EKF as compared to one with correlated process and measurement noises.
【6】 APPLE: Adaptive Planner Parameter Learning from Evaluative Feedback 标题：APPLE：评估反馈中的自适应计划器参数学习 链接：https://arxiv.org/abs/2108.09801
作者：Zizhao Wang,Xuesu Xiao,Garrett Warnell,Peter Stone 备注：6 pages, 4 figures, accepted in IROS 2021. arXiv admin note: substantial text overlap with arXiv:2105.07620 摘要：经典的自主导航系统能够以无碰撞的方式控制机器人，通常具有可验证的安全性和可解释性。然而，面对新环境时，通常需要专家对系统参数进行微调，然后系统才能按预期导航。为了缓解这一需求，最近提出的自适应规划器参数学习范式允许机器人使用远程操作演示或非专家用户的纠正干预来动态调整规划器参数。然而，这些交互方式要求用户完全控制移动机器人，这要求用户熟悉机器人遥操作。作为替代方案，我们引入textsc{apple}，从emph{Evaluative Feedback}（行为的实时标量值评估）自适应规划器参数学习，这代表了一种要求较低的交互方式。模拟和物理实验表明textsc{apple}与具有静态默认参数的计划者相比，可以获得更好的性能，甚至比从更丰富的交互模式中学习到的参数更高。 摘要：Classical autonomous navigation systems can control robots in a collision-free manner, oftentimes with verifiable safety and explainability. When facing new environments, however, fine-tuning of the system parameters by an expert is typically required before the system can navigate as expected. To alleviate this requirement, the recently-proposed Adaptive Planner Parameter Learning paradigm allows robots to emph{learn} how to dynamically adjust planner parameters using a teleoperated demonstration or corrective interventions from non-expert users. However, these interaction modalities require users to take full control of the moving robot, which requires the users to be familiar with robot teleoperation. As an alternative, we introduce textsc{apple}, Adaptive Planner Parameter Learning from emph{Evaluative Feedback} (real-time, scalar-valued assessments of behavior), which represents a less-demanding modality of interaction. Simulated and physical experiments show textsc{apple} can achieve better performance compared to the planner with static default parameters and even yield improvement over learned parameters from richer interaction modalities.
【7】 From Agile Ground to Aerial Navigation: Learning from Learned Hallucination 标题：从敏捷的地面导航到空中导航：从习得的幻觉中学习 链接：https://arxiv.org/abs/2108.09793
作者：Zizhao Wang,Xuesu Xiao,Alexander J Nettekoven,Kadhiravan Umasankar,Anika Singh,Sriram Bommakanti,Ufuk Topcu,Peter Stone 备注：6 pages, 5 figures, accepted in IROS 2021 摘要：本文提出了一种自监督学习幻觉（LfLH）方法，用于学习地面和空中机器人在高度受限环境中导航的快速反应式运动规划器。最近的自主导航自幻觉学习（LfH）范式通过在完全安全的无障碍空间中随机探索来执行运动计划，使用手工制作的幻觉技术为机器人的感知添加假想障碍，然后学习运动规划人员在真实、高度受限的环境中导航，危险空间。然而，当前手工制作的幻觉技术需要针对特定的机器人类型（例如，差速驱动地面车辆）进行定制，并使用严重依赖于某些假设的近似值（例如，短期规划）。在这项工作中，LfLH不是手动设计幻觉功能，而是以自我监督的方式学习幻觉障碍物配置，其中来自开放空间随机探索的运动计划是最优的。LfLH对不同类型的机器人具有鲁棒性，并且不会对规划范围进行假设。通过在地面和空中机器人的模拟和物理环境中进行评估，LfLH的性能优于或与以前的幻觉方法相当，以及基于采样和优化的经典方法。 摘要：This paper presents a self-supervised Learning from Learned Hallucination (LfLH) method to learn fast and reactive motion planners for ground and aerial robots to navigate through highly constrained environments. The recent Learning from Hallucination (LfH) paradigm for autonomous navigation executes motion plans by random exploration in completely safe obstacle-free spaces, uses hand-crafted hallucination techniques to add imaginary obstacles to the robot's perception, and then learns motion planners to navigate in realistic, highly-constrained, dangerous spaces. However, current hand-crafted hallucination techniques need to be tailored for specific robot types (e.g., a differential drive ground vehicle), and use approximations heavily dependent on certain assumptions (e.g., a short planning horizon). In this work, instead of manually designing hallucination functions, LfLH learns to hallucinate obstacle configurations, where the motion plans from random exploration in open space are optimal, in a self-supervised manner. LfLH is robust to different robot types and does not make assumptions about the planning horizon. Evaluated in both simulated and physical environments with a ground and an aerial robot, LfLH outperforms or performs comparably to previous hallucination approaches, along with sampling- and optimization-based classical methods.
【8】 UltraBot: Autonomous Mobile Robot for Indoor UV-C Disinfection with Non-trivial Shape of Disinfection Zone 标题：UltraBot：消毒区形状非平凡的室内UV-C消毒自主式移动机器人 链接：https://arxiv.org/abs/2108.09792
作者：Nikita Mikhailovskiy,Alexander Sedunin,Stepan Perminov,Ivan Kalinov,Dzmitry Tsetserukou 机构：Skolkovo Institute of Science and Technology Moscow, Russia 备注：Accepted to the 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). 2021. IEEE copyright 摘要：本文着重研究2019冠状病毒疾病的发展，以减少COVID-19传播，同时也减少其他有害细菌和病毒的传播。这项研究背后的动机是开发这样一种机器人，它能够在不使用有害喷雾剂和化学品的情况下执行消毒任务，这些有害喷雾剂和化学品会留下残留物，并且需要在消毒后长时间通风。UltraBot技术有潜力提供最佳的自主消毒性能，同时照顾人们，防止他们受到UV-C辐射。本文重点介绍了UltraBot的机械和电气设计以及消毒性能。通过实验验证了UV-C灯阵列对机器人消毒能力和每侧实际消毒面积的有效性。消毒效果结果显示了多道次技术的实际性能，该技术在两次机器人以0.14 m/s的速度通过后，通过组合直接UV-C暴露和基于臭氧的空气净化，提供1 log的减少。该技术与10分钟静态消毒具有相同的性能。最后，我们通过两个连续实验计算了机器人消毒区域的非平凡形式，以产生最佳路径规划，并在选定区域提供全面消毒。 摘要：The paper focuses on the development of an autonomous disinfection robot UltraBot to reduce COVID-19 transmission along with other harmful bacteria and viruses. The motivation behind the research is to develop such a robot that is capable of performing disinfection tasks without the use of harmful sprays and chemicals that can leave residues and require airing the room afterward for a long time. UltraBot technology has the potential to offer the most optimal autonomous disinfection performance along with taking care of people, keeping them from getting under the UV-C radiation. The paper highlights UltraBot's mechanical and electrical design as well as disinfection performance. The conducted experiments demonstrate the effectiveness of robot disinfection ability and actual disinfection area per each side with UV-C lamp array. The disinfection effectiveness results show actual performance for the multi-pass technique that provides 1-log reduction with combined direct UV-C exposure and ozone-based air purification after two robot passes at a speed of 0.14 m/s. This technique has the same performance as ten minutes static disinfection. Finally, we have calculated the non-trivial form of the robot disinfection zone by two consecutive experiment to produce optimal path planning and to provide full disinfection in selected areas.
【9】 Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World TriFinger 标题：将GPU仿真中的灵巧操作转移到远程真实的三指格上 链接：https://arxiv.org/abs/2108.09779
作者：Arthur Allshire,Mayank Mittal,Varun Lodaya,Viktor Makoviychuk,Denys Makoviichuk,Felix Widmaier,Manuel Wüthrich,Stefan Bauer,Ankur Handa,Animesh Garg 机构：University of Toronto, Vector Institute,ETH Zurich,Nvidia,Snap,MPI Tubingen 备注：13 pages, 11 figures 摘要：我们提出了一个学习具有挑战性的灵巧操作任务的系统，该任务涉及使用NVIDIA的IsaacGym模拟器训练3个手指，将立方体移动到任意6自由度姿势。我们展示了在模拟和模拟到真实转换中，使用关键点（而不是位置 四元数表示）对6-DoF中的对象姿势进行策略观察和奖励计算来训练无模型强化学习代理的经验优势。通过利用区域随机化策略以及操纵对象姿势的关键点表示，我们在由真实机器人挑战赛组织者维护的远程三指系统上实现了83%的高成功率。为了帮助进一步研究手部操作的学习，我们在https://s2r2-ig.github.io 摘要：We present a system for learning a challenging dexterous manipulation task involving moving a cube to an arbitrary 6-DoF pose with only 3-fingers trained with NVIDIA's IsaacGym simulator. We show empirical benefits, both in simulation and sim-to-real transfer, of using keypoints as opposed to position quaternion representations for the object pose in 6-DoF for policy observations and in reward calculation to train a model-free reinforcement learning agent. By utilizing domain randomization strategies along with the keypoint representation of the pose of the manipulated object, we achieve a high success rate of 83% on a remote TriFinger system maintained by the organizers of the Real Robot Challenge. With the aim of assisting further research in learning in-hand manipulation, we make the codebase of our system, along with trained checkpoints that come with billions of steps of experience available, at https://s2r2-ig.github.io
【10】 UltraBot: Autonomous Mobile Robot for Indoor UV-C Disinfection 标题：UltraBot：室内UV-C消毒自主式移动机器人 链接：https://arxiv.org/abs/2108.09772
作者：Stepan Perminov,Nikita Mikhailovskiy,Alexander Sedunin,Iaroslav Okunevich,Ivan Kalinov,Mikhail Kurenkov,Dzmitry Tsetserukou 机构： Skolkovo Institute of Science and Technology 备注：Accepted to 17th International Conference on Automation Science and Engineering (CASE). 2021. IEEE copyright 摘要：2019冠状病毒疾病的研究进展主要集中在自主机器人超轻型机器人的研制上，以减少COVID-19传播和其他有害细菌和病毒。这项研究背后的动机是开发这样一种机器人，它能够在不使用有害喷雾剂和化学品的情况下执行消毒任务，而这些有害喷雾剂和化学品可能会留下残留物，之后需要长时间通风房间，并可能导致金属结构的腐蚀。UltraBot技术有潜力提供最佳的自主消毒性能，同时照顾人们，防止他们受到UV-C辐射。本文重点介绍了UltraBot的机械和电气结构以及低级和高级控制系统。进行的实验证明了机器人定位模块的有效性和UV-C消毒的最佳轨迹。UV-C消毒性能的结果显示，在UV-C照射10分钟后，在距离机器人2.8米的距离上，细菌总数（TBC）减少了94%。 摘要：The paper focuses on the development of the autonomous robot UltraBot to reduce COVID-19 transmission and other harmful bacteria and viruses. The motivation behind the research is to develop such a robot that is capable of performing disinfection tasks without the use of harmful sprays and chemicals that can leave residues, require airing the room afterward for a long time, and can cause the corrosion of the metal structures. UltraBot technology has the potential to offer the most optimal autonomous disinfection performance along with taking care of people, keeping them from getting under UV-C radiation. The paper highlights UltraBot's mechanical and electrical structures as well as low-level and high-level control systems. The conducted experiments demonstrate the effectiveness of the robot localization module and optimal trajectories for UV-C disinfection. The results of UV-C disinfection performance revealed a decrease of the total bacterial count (TBC) by 94% on the distance of 2.8 meters from the robot after 10 minutes of UV-C irradiation.
【11】 DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets 标题：DenseTNT：密集目标集的端到端轨迹预测 链接：https://arxiv.org/abs/2108.09640
作者：Junru Gu,Chen Sun,Hang Zhao 机构：IIIS, Tsinghua University, Brown University 备注：Accepted to ICCV 2021 摘要：由于人类行为的随机性，预测道路代理的未来轨迹对于自动驾驶来说是一个挑战。最近，基于目标的多轨迹预测方法被证明是有效的，它们首先对抽样的目标候选进行评分，然后从中选择最终的一组。然而，这些方法通常涉及基于稀疏预定义锚和启发式目标选择算法的目标预测。在这项工作中，我们提出了一个无锚和端到端的轨迹预测模型，命名为Densett，它直接从密集的候选目标输出一组轨迹。此外，我们引入了一种基于离线优化的技术，为最终的在线模型提供多个未来的伪标签。实验表明，Densett实现了最先进的性能，在Argoverse运动预测基准中排名第一，并在2021 Waymo开放数据集运动预测挑战赛中获得第一名。 摘要：Due to the stochasticity of human behaviors, predicting the future trajectories of road agents is challenging for autonomous driving. Recently, goal-based multi-trajectory prediction methods are proved to be effective, where they first score over-sampled goal candidates and then select a final set from them. However, these methods usually involve goal predictions based on sparse pre-defined anchors and heuristic goal selection algorithms. In this work, we propose an anchor-free and end-to-end trajectory prediction model, named DenseTNT, that directly outputs a set of trajectories from dense goal candidates. In addition, we introduce an offline optimization-based technique to provide multi-future pseudo-labels for our final online model. Experiments show that DenseTNT achieves state-of-the-art performance, ranking 1st on the Argoverse motion forecasting benchmark and being the 1st place winner of the 2021 Waymo Open Dataset Motion Prediction Challenge.
【12】 Event-Triggered Control for Weight-Unbalanced Directed Networks 标题：权重不平衡有向网络的事件触发控制 链接：https://arxiv.org/abs/2108.09609
作者：Juan D. Pabon,Gustavo A. Cardona,Nestor I. Ospina,Juan Calderon,Eduardo Mojica-Nava 备注：6 Pages, 4 Figures, IROS 2021 摘要：在这项工作中，我们为加权不平衡定向同质机器人网络开发了一种事件触发控制策略，以达到动态一致性。我们提出了当所有机器人都可以访问参考时以及当有限数量的机器人可以访问时，同步机器人网络的一些保证。所提出的事件触发控制可以减少和避免信号的周期性更新。与当前的一些控制方法不同，我们通过使用对数范数证明了稳定性，这扩展了控制律应用于广泛的有向图的可能性，与其他工作不同，在其他工作中，事件触发控制只能在强连通和权重平衡有向图上实现。我们通过在仿真和真实的机器人团队中进行实验来测试我们算法的性能。 摘要：We develop an event-triggered control strategy for a weighted-unbalanced directed homogeneous robot network to reach a dynamic consensus in this work. We present some guarantees for synchronizing a robot network when all robots have access to the reference and when a limited number of robots have access. The proposed event-triggered control can reduce and avoid the periodic updating of the signals. Unlike some current control methods, we prove stability by making use of a logarithmic norm, which extends the possibilities of the control law to be applied to a wide range of directed graphs, in contrast to other works where the event-triggered control can be only implemented over strongly connected and weight-balanced digraphs. We test the performance of our algorithm by carrying out experiments both in simulation and in a real team of robots.
【13】 Geometric Perspectives on Fundamental Solutions in the Linearized Satellite Relative Motion Problem 标题：线性化卫星相对运动问题基本解的几何观点 链接：https://arxiv.org/abs/2108.09608
作者：Ethan Burnett,Hanspeter Schaub 机构： University of Colorado Boulder, Smead Department of Aerospace Engineering Sciences, University ofColorado Boulder 1arXiv 摘要：了解自然相对运动轨迹对于在复杂环境中执行节能型多卫星任务至关重要。本文研究闭合主轨道线性化的卫星相对运动解的计算和有效参数化问题。通过识别不同坐标系下相对运动动力学的Lyapunov-Floquet变换之间的解析关系，为快速计算和探索各种应用中可用的近距离自然相对运动类型提供了新的手段。该方法被证明为开普勒相对运动问题的一般偏心在多个坐标表示。开普勒假设实现了一种分析方法，带来了新的几何见解，并允许与先前线性化的相对运动解进行比较。 摘要：Understanding natural relative motion trajectories is critical to enable fuel-efficient multi-satellite missions operating in complex environments. This paper studies the problem of computing and efficiently parameterizing satellite relative motion solutions for linearization about a closed chief orbit. By identifying the analytic relationship between Lyapunov-Floquet transformations of the relative motion dynamics in different coordinate systems, new means are provided for rapid computation and exploration of the types of close-proximity natural relative motion available in various applications. The approach is demonstrated for the Keplerian relative motion problem with general eccentricities in multiple coordinate representations. The Keplerian assumption enables an analytic approach, leads to new geometric insights, and allows for comparison to prior linearized relative motion solutions.
【14】 A Technical Survey and Evaluation of Traditional Point Cloud Clustering Methods for LiDAR Panoptic Segmentation 标题：LiDAR全景图像分割中传统点云聚类方法的技术综述与评价 链接：https://arxiv.org/abs/2108.09522
作者：Yiming Zhao,Xiao Zhang,Xinming Huang 机构：Worcester Polytechnic Institute, Institute Rd, Worcester, MA, USA 备注：1. A hybrid SOTA solution. 2. Accept by ICCV2021 Workshop on Traditional Computer Vision in the Age of Deep Learning. 3. Code: this https URL 摘要：激光雷达全景图像分割是一项新提出的自主驾驶技术。与流行的端到端深度学习解决方案相比，我们提出了一种混合方法，使用现有的语义分割网络提取语义信息，使用传统的激光雷达点云聚类算法分割每个实例对象。我们认为，通过在SemanticKITTI数据集的全景分割排行榜上展示所有已发布的端到端深度学习解决方案的最新性能，基于几何的传统聚类算法值得考虑。据我们所知，我们是第一个尝试使用聚类算法进行点云全景分割的人。因此，在本文中，我们没有研究新的模型，而是通过实现四种典型的集群方法进行了全面的技术调查，并在基准上报告了它们的性能。这四种聚类方法最具代表性，具有实时运行速度。在本文中，它们用C 实现，然后被封装为Python函数，与现有的深度学习框架无缝集成。我们为可能对此问题感兴趣的同行研究人员发布代码。 摘要：LiDAR panoptic segmentation is a newly proposed technical task for autonomous driving. In contrast to popular end-to-end deep learning solutions, we propose a hybrid method with an existing semantic segmentation network to extract semantic information and a traditional LiDAR point cloud cluster algorithm to split each instance object. We argue geometry-based traditional clustering algorithms are worth being considered by showing a state-of-the-art performance among all published end-to-end deep learning solutions on the panoptic segmentation leaderboard of the SemanticKITTI dataset. To our best knowledge, we are the first to attempt the point cloud panoptic segmentation with clustering algorithms. Therefore, instead of working on new models, we give a comprehensive technical survey in this paper by implementing four typical cluster methods and report their performances on the benchmark. Those four cluster methods are the most representative ones with real-time running speed. They are implemented with C in this paper and then wrapped as a python function for seamless integration with the existing deep learning frameworks. We release our code for peer researchers who might be interested in this problem.
【15】 Incrementally Stochastic and Accelerated Gradient Information mixed Optimization for Manipulator Motion Planning 标题：机械手运动规划的增量随机加速梯度信息混合优化 链接：https://arxiv.org/abs/2108.09490
作者：Yichang Feng,Haiyun Zhang,Jin Wang,Guodong Lu 机构： State Key Laboratory of Fluid Powerand Mechatronic Systems, School of Mechanical Engineering, ZheJiangUniversity 摘要：针对狭窄工作空间中的机器人，提出了一种新的运动规划算法——增量随机加速梯度信息混合优化算法（iSAGO）。首先，我们提出了综合加速和随机梯度信息的iSAGO整体方案，以实现惩罚方法中的有效下降。在随机部分，我们通过随机选择碰撞复选框、间隔时间序列和基于Adam的惩罚因子来生成自适应随机矩，以解决身体障碍物卡住的情况。由于气孔收敛速度慢，我们将加速梯度积分，并在Lipschitz常数重建框架中刺激下降速率。此外，我们引入贝叶斯树推理（BTI）方法，将整个轨迹优化（SAGO）转化为增量子轨迹优化（iSAGO），以提高计算效率和成功率。最后，我们演示了关键系数调整，将iSAGO与其他规划器（CHOMP、GPMP2、TrajOpt、STOMP和RRT Connect）进行基准测试，并在存储架中的AUBO-i5上实现iSAGO。结果表明，iSAGO的成功率最高，求解效率适中。 摘要：This paper introduces a novel motion planning algorithm, incrementally stochastic and accelerated gradient information mixed optimization (iSAGO), for robotic manipulators in a narrow workspace. Primarily, we propose the overall scheme of iSAGO integrating the accelerated and stochastic gradient information for efficient descent in the penalty method. In the stochastic part, we generate the adaptive stochastic moment via the random selection of collision checkboxes, interval time-series, and penalty factor based on Adam to solve the body-obstacle stuck case. Due to the slow convergence of STOMA, we integrate the accelerated gradient and stimulate the descent rate in a Lipschitz constant reestimation framework. Moreover, we introduce the Bayesian tree inference (BTI) method, transforming the whole trajectory optimization (SAGO) into an incremental sub-trajectory optimization (iSAGO) to improve the computational efficiency and success rate. Finally, we demonstrate the key coefficient tuning, benchmark iSAGO against other planners (CHOMP, GPMP2, TrajOpt, STOMP, and RRT-Connect), and implement iSAGO on AUBO-i5 in a storage shelf. The result shows the highest success rate and moderate solving efficiency of iSAGO.
【16】 DSP-SLAM: Object Oriented SLAM with Deep Shape Priors 标题：DSP-SLAM：具有深度形状先验的面向对象SLAM 链接：https://arxiv.org/abs/2108.09481
作者：Jingwen Wang,Martin Rünz,Lourdes Agapito 机构：Martin R¨unz, Department of Computer Science, University College London 摘要：我们提出了DSP-SLAM，这是一个面向对象的SLAM系统，它为前景对象构建了一个丰富而精确的密集3D模型的联合地图，并用稀疏的地标点来表示背景。DSP-SLAM将基于特征的SLAM系统重建的三维点云作为输入，并使其具备通过密集重建检测对象来增强其稀疏地图的能力。通过语义实例分割检测目标，并通过一种新的二阶优化算法，以特定类别的深形状嵌入作为先验估计目标的形状和姿态。我们的对象感知束调整构建姿势图，以联合优化摄影机姿势、对象位置和特征点。DSP-SLAM可以在3种不同的输入模式下以每秒10帧的速度工作：单目、立体声或立体声 激光雷达。我们演示了DSP-SLAM在Friburg和Redwood OS数据集的单目RGB序列和KITTI里程计数据集的stereo LiDAR序列上以几乎帧速率运行，表明它实现了高质量的全对象重建，即使是部分观测，同时保持了一致的全局地图。我们的评估显示，与最近基于深度先验的重建方法相比，物体姿态和形状重建有了改进，并减少了KITTI数据集上的相机跟踪漂移。 摘要：We propose DSP-SLAM, an object-oriented SLAM system that builds a rich and accurate joint map of dense 3D models for foreground objects, and sparse landmark points to represent the background. DSP-SLAM takes as input the 3D point cloud reconstructed by a feature-based SLAM system and equips it with the ability to enhance its sparse map with dense reconstructions of detected objects. Objects are detected via semantic instance segmentation, and their shape and pose is estimated using category-specific deep shape embeddings as priors, via a novel second order optimization. Our object-aware bundle adjustment builds a pose-graph to jointly optimize camera poses, object locations and feature points. DSP-SLAM can operate at 10 frames per second on 3 different input modalities: monocular, stereo, or stereo LiDAR. We demonstrate DSP-SLAM operating at almost frame rate on monocular-RGB sequences from the Friburg and Redwood-OS datasets, and on stereo LiDAR sequences on the KITTI odometry dataset showing that it achieves high-quality full object reconstructions, even from partial observations, while maintaining a consistent global map. Our evaluation shows improvements in object pose and shape reconstruction with respect to recent deep prior-based reconstruction methods and reductions in camera tracking drift on the KITTI dataset.
【17】 A Geometric Kinematic Model for Flexible Voxel-Based Robots 标题：基于柔性体素的机器人几何运动学模型 链接：https://arxiv.org/abs/2108.09442
作者：Maryam Tebyani,Alex Spaeth,Nicholas Cramer,Mircea Teodorescu 机构： Department of Electrical and Computer En-, gineering, University of California, Santa Cruz, Genomics Institute, University of California, Santa Cruz, Santa Cruz, California, United States, NASA Ames Research Center, Moffett Field 备注：10 pages, 7 figures 摘要：基于体素的结构提供了一个模块化的、机械柔性的周期晶格，可以通过内部变形作为软机器人使用。为了将这些结构用于机器人任务，我们使用有限元方法来描述单自由度变形引起的运动，并开发简化的运动学模型。我们发现节点平移在晶格内沿几何平面周期性传播，并简要说明平移模式支配着致动器的能量使用。由此产生的运动学模型根据用户定义的控制和末端效应器节点来框定结构变形，这进一步减小了模型尺寸。通过机车体素机器人三脚架稳定步态的设计和准静态模型的物理实验验证，导出的运动平面（POM）模型可以等效地用于正运动学和逆运动学。 摘要：Voxel-based structures provide a modular, mechanically flexible periodic lattice which can be used as a soft robot through internal deformations. To engage these structures for robotic tasks, we use a finite element method to characterize the motion caused by deforming single degrees of freedom and develop a reduced kinematic model. We find that node translations propagate periodically along geometric planes within the lattice, and briefly show that translational modes dominate the energy usage of the actuators. The resulting kinematic model frames the structural deformations in terms of user-defined control and end effector nodes, which further reduces the model size. The derived Planes of Motion (POM) model can be equivalently used for forward and inverse kinematics, as demonstrated by the design of a tripod stable gait for a locomotive voxel robot and validation of the quasi-static model through physical experiments.
【18】 Deadlock and Noise in Self-Organized Aggregation Without Computation 标题：无计算自组织聚集中的死锁和噪声 链接：https://arxiv.org/abs/2108.09403
作者：Joshua J. Daymude,Noble C. Harasha,Andréa W. Richa,Ryan Yiu 机构：Biodesign Center for Biocomputing, Security and Society, Arizona State University, Tempe, AZ, The Peggy Payne Academy at McClintock High School, Tempe, AZ, School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 备注：17 pages, 11 figures 摘要：聚集是群机器人技术的一个基本行为，它需要一个系统聚集在一个紧凑、连接的集群中。2014年，Gauci等人提出了一种令人惊讶的算法，该算法仅使用二进制视线传感器，无需算术计算或持久内存，就能可靠地实现群体聚集。已经严格证明，该算法将一个机器人聚合到另一个机器人，但它是否总是聚合一个$n>2$机器人的系统，如在实验和模拟中所观察到的，仍然是开放的。我们证明了存在死锁配置，当机器人的运动是均匀的和确定性的时，该算法无法实现$n>3$机器人的聚集。从积极的方面来看，我们证明了算法（i）对少量错误具有鲁棒性，能够避免死锁，并且（ii）在使用视锥传感器时，可证明在$n=2$的情况下实现了线性运行时加速。最后，我们介绍了该算法的一种噪声离散自适应算法，该算法更易于对噪声进行严格分析，其仿真结果与原始的连续算法定性一致。 摘要：Aggregation is a fundamental behavior for swarm robotics that requires a system to gather together in a compact, connected cluster. In 2014, Gauci et al. proposed a surprising algorithm that reliably achieves swarm aggregation using only a binary line-of-sight sensor and no arithmetic computation or persistent memory. It has been rigorously proven that this algorithm will aggregate one robot to another, but it remained open whether it would always aggregate a system of $n > 2$ robots as was observed in experiments and simulations. We prove that there exist deadlocked configurations from which this algorithm cannot achieve aggregation for $n > 3$ robots when the robots' motion is uniform and deterministic. On the positive side, we show that the algorithm (i) is robust to small amounts of error, enabling deadlock avoidance, and (ii) provably achieves a linear runtime speedup for the $n = 2$ case when using a cone-of-sight sensor. Finally, we introduce a noisy, discrete adaptation of this algorithm that is more amenable to rigorous analysis of noise and whose simulation results align qualitatively with the original, continuous algorithm.
