机器人相关学术速递[12.23]

cs.RO机器人相关，共计4篇

【1】 IDCAIS: Inter-Defender Collision-Aware Interception Strategy against Multiple Attackers 标题：IDCAIS：防御多攻击者的冲突感知拦截策略 链接：https://arxiv.org/abs/2112.12098

作者：Vishnu S. Chipade,Dimitra Panagou 机构： the authors solve the reach-avoid game for eachThe authors are with the Department of Aerospace Engineer-ing, University of Michigan, a National Science Foundation Indus-tryUniversity Cooperative Research Center (IUCRC) under NSFAward No 备注：9 pages, 17 figures 摘要：该文提出了一种防御者间冲突感知拦截策略（IDCAIS），用于防御者拦截攻击者，以保护保护区域，使每个防御者也避免与其他防御者发生冲突。特别是，防御者被指派使用混合整数二次规划（MIQP）拦截攻击者：1）最小化防御者在时间最优控制下捕获攻击者所花费的时间总和，2）帮助消除或延迟防御者在最优轨迹上可能发生的未来冲突。为了防止攻击者在最优轨迹上不可避免的碰撞或时间次优行为引起的碰撞，提出了一种利用指数控制屏障函数（ECBF）的最小增广控制。仿真结果表明了该方法的有效性。 摘要：This paper presents an Inter-Defender Collision-Aware Interception Strategy (IDCAIS) for defenders to intercept attackers in order to defend a protected area, such that each defender also avoids collision with other defenders. In particular, the defenders are assigned to intercept attackers using a mixed-integer quadratic program (MIQP) that: 1)minimizes the sum of times taken by defenders to capture the attackers under time-optimal control, and2) helps eliminate or delay possible future collisions among the defenders on the optimal trajectories. To prevent inevitable collisions on optimal trajectories or collisions arising due to time-sub-optimal behavior by the attackers, a minimally augmented control using exponential control barrier function (ECBF) is provided. Simulations show the efficacy of the approach.

【2】 Semantically enriched spatial modelling of industrial indoor environments enabling location-based services 标题：实现基于位置的服务的工业室内环境的语义丰富的空间建模 链接：https://arxiv.org/abs/2112.11856

作者：Arne Wendt,Michael Brand,Thorsten Schüppstuhl 机构：Institute of Aircraft Production Technology, Hamburg University of Technology, Denickestraße , Hamburg, Germany 备注：None 摘要：本文提出了一个称为RAIL的软件系统的概念，该系统在动态空间模型中表示工业室内环境，旨在简化基于位置的服务的开发和提供。RAIL通过统一的接口集成来自不同传感器模式的数据和其他上下文信息。对其他领域的环境建模方法进行了审查和分析，以确定其是否适合我们目标领域的要求；内部物流和生产。随后提出了一种新的室内空间数据建模方法和软件系统的体系结构。 摘要：This paper presents a concept for a software system called RAIL representing industrial indoor environments in a dynamic spatial model, aimed at easing development and provision of location-based services. RAIL integrates data from different sensor modalities and additional contextual information through a unified interface. Approaches to environmental modelling from other domains are reviewed and analyzed for their suitability regarding the requirements for our target domains; intralogistics and production. Subsequently a novel way of modelling data representing indoor space, and an architecture for the software system are proposed.

【3】 New metal-plastic hybrid additive manufacturing strategy: Fabrication of arbitrary metal-patterns on external and even internal surfaces of 3D plastic structures 标题：金属-塑料混合添加剂制造新策略：在三维塑料结构的外表面甚至内表面制造任意金属图案 链接：https://arxiv.org/abs/2112.11661

作者：Kewei Song,Yue Cui,Tiannan Tao,Xiangyi Meng,Michinari Sone,Masahiro Yoshino,Shinjiro Umezu,Hirotaka Sato 机构：Graduate School of Creative Science and Engineering, Department of Modern, Mechanical Engineering, Waseda University,-,-, Okubo, Shinjuku-ku, Tokyo ,-, Research and Development div., Yoshino Denka Kogyo, Inc., Japan. 摘要：在复杂的三维（3D）塑料零件上构建精确的微纳金属图案可以制造用于高级应用的功能性器件。然而，这种图案目前价格昂贵，需要复杂的工艺和较长的制造交付周期。本工作展示了一种具有任意复杂形状的微纳三维金属-塑料复合结构的制备工艺。在该方法中，对光固化树脂进行改性以制备能够允许后续化学镀（ELP）的活性前体。新开发了一种多材料数字光处理3D打印机，用于制造包含标准树脂或活性前体树脂相互嵌套区域的零件。这些零件的选择性3D ELP加工提供了各种金属塑料复合零件，这些零件具有复杂的空心微纳米结构，具有小到40μm的尺寸尺度上的特定拓扑关系。利用这项技术，传统方法无法制造的3D金属拓扑结构成为可能，并且金属图案可以在塑料零件内部生成，作为进一步小型化电子设备的手段。所提出的方法还可以生成金属涂层，显示出金属对塑料基材的改善附着力。基于该技术，设计并制作了几种由不同功能非金属材料和特定金属图案组成的传感器。目前的结果证明了所提出的方法的可行性，并提出了在智能三维微纳电子学、三维可穿戴设备、微/纳米传感器和医疗保健领域的潜在应用。 摘要：Constructing precise micro-nano metal patterns on complex three-dimensional (3D) plastic parts allows the fabrication of functional devices for advanced applications. However, this patterning is currently expensive and requires complex processes with long manufacturing lead time. The present work demonstrates a process for the fabrication of micro-nano 3D metal-plastic composite structures with arbitrarily complex shapes. In this approach, a light-cured resin is modified to prepare an active precursor capable of allowing subsequent electroless plating (ELP). A multi-material digital light processing 3D printer was newly developed to enable the fabrication of parts containing regions made of either standard resin or active precursor resin nested within each other. Selective 3D ELP processing of such parts provided various metal-plastic composite parts having complicated hollow micro-nano structures with specific topological relationships on a size scale as small as 40 um. Using this technique, 3D metal topologies that cannot be manufactured by traditional methods are possible, and metal patterns can be produced inside plastic parts as a means of further miniaturizing electronic devices. The proposed method can also generate metal coatings exhibiting improved adhesion of metal to plastic substrate. Based on this technique, several sensors composed of different functional nonmetallic materials and specific metal patterns were designed and fabricated. The present results demonstrate the viability of the proposed method and suggest potential applications in the fields of smart 3D micro-nano electronics, 3D wearable devices, micro/nano-sensors, and health care.

【4】 Off Environment Evaluation Using Convex Risk Minimization 标题：基于凸风险最小化的离岸环境评价 链接：https://arxiv.org/abs/2112.11532

作者：Pulkit Katdare,Shuijing Liu,Katherine Driggs-Campbell 机构： These synthetic perturba-tions are created under the assumption that the discrepancyThe authors are with the department of Electrical and Computer Engi-neering, University of Illinois at Urbana-Champaign 备注：7 pages, 3 figures (with sub-figures) 摘要：在机器人上应用强化学习（RL）方法通常涉及在模拟中训练策略，并将其部署到现实世界中的机器人上。由于真实世界和模拟器之间的模型不匹配，以这种方式部署的RL代理的性能往往不理想。为了解决这个问题，研究人员开发了基于合成噪声干扰的鲁棒策略学习算法。但是，这些方法不能保证在目标环境中的性能。我们提出了一种凸风险最小化算法，利用来自两种环境的轨迹数据估计模拟器和目标域之间的模型失配。我们表明，该估计器可与模拟器一起用于评估目标域中RL代理的性能，从而有效地弥合这两种环境之间的差距。我们还证明了我们的估计的收敛速度为${n^{-1/4}}$$，其中$n$是训练样本数。在模拟中，我们演示了我们的方法如何有效地近似和评估Gridworld、Cartpole和Reacher环境中一系列策略的性能。我们还表明，我们的方法能够估计性能的7自由度机械臂使用模拟器和远程收集的数据从机器人在现实世界中。 摘要：Applying reinforcement learning (RL) methods on robots typically involves training a policy in simulation and deploying it on a robot in the real world. Because of the model mismatch between the real world and the simulator, RL agents deployed in this manner tend to perform suboptimally. To tackle this problem, researchers have developed robust policy learning algorithms that rely on synthetic noise disturbances. However, such methods do not guarantee performance in the target environment. We propose a convex risk minimization algorithm to estimate the model mismatch between the simulator and the target domain using trajectory data from both environments. We show that this estimator can be used along with the simulator to evaluate performance of an RL agents in the target domain, effectively bridging the gap between these two environments. We also show that the convergence rate of our estimator to be of the order of ${n^{-1/4}}$, where $n$ is the number of training samples. In simulation, we demonstrate how our method effectively approximates and evaluates performance on Gridworld, Cartpole, and Reacher environments on a range of policies. We also show that the our method is able to estimate performance of a 7 DOF robotic arm using the simulator and remotely collected data from the robot in the real world.

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