When nodes in a mobile network use relative noisy measurements with respect to their neighbors to estimate their positions, the overall connectivity and geometry of the measurement network has a critical influence on the achievable localization accuracy. This paper considers the problem of deploying a mobile robotic network implementing a cooperative localization scheme based on range measurements only, while attempting to maintain a network geometry that is favorable to estimating the robots' positions with high accuracy. The quality of the network geometry is measured by a "localizability" function serving as potential field for robot motion planning. This function is built from the Cramér-Rao bound, which provides for a given geometry a lower bound on the covariance matrix achievable by any unbiased position estimator that the robots might implement using their relative measurements. We describe gradient descent-based motion planners for the robots that attempt to optimize or constrain different variations of the network's localizability function, and discuss ways of implementing these controllers in a distributed manner. Finally, the paper also establishes formal connections between our statistical point of view and maintaining a form of weighted rigidity for the graph capturing the relative range measurements.
A Step-by-step Guide on Nonlinear Model Predictive Control for Safe Mobile Robot Navigation
department of Cognitive Robotics, Delft University of Technology（认知机器人系，代尔夫特理工大学）；Institute for Dynamic Systems and Control, ETH Zürich（动态系统与控制研究所，苏黎世联邦理工学院）
Open-Source, Cost-Aware Kinematically Feasible Planning for Mobile and Surface Robotics
Open Navigation LLC；Carnegie Mellon University（卡内基梅隆大学）；Locus Robotics；QUT Centre for Robotics and School of Electrical Engineering and Robotics（QUT机器人中心和电气工程与机器人学院）
Evaluation of a Smart Mobile Robotic System for Industrial Plant Inspection and Supervision
Fraunhofer Institute for Highspeed Dynamics, Ernst-Mach-Institute (EMI)（弗劳恩霍夫高速动力学研究所恩斯特·马赫研究所）；Endress+Hauser Digital Solutions GmbH（恩德斯豪斯数字解决方案有限公司）；University of Freiburg（弗赖堡大学）；dotscene GmbH（dotscene有限公司）；Evonik Operations GmbH（赢创运营有限公司）；Telocate GmbH（Telocate有限公司）；University of Technology Nuremberg（纽伦堡工业大学）
Printing-while-moving: a new paradigm for large-scale robotic 3D Printing
Singapore Centre for 3D Printing（新加坡3D打印中心）；Nanyang Technological University（南洋理工大学）
建筑和施工最近已成为机器人技术令人兴奋的应用领域。特别是，材料配方和机器人技术的快速发展使混凝土的机器人3D打印成为现场施工的一种有前途的技术。然而，可扩展性仍然是广泛采用的重要障碍：打印系统（基于台架或基于臂）通常比要打印的结构大得多，因此笨重。最近，提出了一种移动打印系统-安装在移动基座上的操纵器-来缓解这个问题：这样的系统通过移动其基座，可以潜在地打印比其自身更大的结构。然而，所提出的系统只能在静止时打印，从而对可以在单次拍摄中打印的结构的尺寸施加限制。在这里，我们开发了一个系统，实现了打印，而移动的范例，这使得打印任意大小的单件结构与一个单一的机器人。这种发展需要解决移动3D打印特有的运动规划、定位和运动控制问题。我们报告了我们的框架来解决这些问题，并首次展示了移动打印实验，其中一个210 cm x 45 cm x 10 cm的混凝土结构由一个达到87 cm的机器人手臂打印。
From Pixels to Shelf: An Integrated Robotic System for Autonomous Supermarket Stocking with a Mobile Manipulator
Department of Information Engineering, University of Padova（帕多瓦大学信息工程系）；Division of Decision and Control Systems, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology（皇家理工学院电气工程与计算机科学学院决策与控制系统系）；Animum AB（Animum公司）
M2Diffuser: Diffusion-based Trajectory Optimization for Mobile Manipulation in 3D Scenes
School of Electronic Information and Communications, Huazhong University of Science and Technology（华中科技大学电子信息与通信学院）；State Key Laboratory of General Artificial Intelligence, Beijing Institute for General Artificial Intelligence (BIGAI)（北京通用人工智能研究院通用人工智能国家重点实验室）；Center for Vision, Cognition, Learning, and Autonomy (VCLA), Statistics Department, University of California, Los Angeles (UCLA)（加州大学洛杉矶分校视觉、认知、学习与自主中心（VCLA）统计系）
