机器人相关学术速递[6.21]

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cs.RO机器人相关，共计13篇

【1】 Semantic navigation with domain knowledge 标题：基于领域知识的语义导航

作者：Rafael Gomes Braga,Sina Karimi,Ulrich Dah-Achinanon,Ivanka Iordanova,David St-Onge 机构：Received: date Accepted: date 备注：12 pages, 10 figures. arXiv admin note: substantial text overlap with arXiv:2104.10296 链接：https://arxiv.org/abs/2106.10220 摘要：移动机器人系统的几个部署位置是人为的（即城市消防员、建筑检查、财产安全），管理者可以访问有关该位置的特定领域的知识，这些知识可以提供语义上下文信息，以便更好地进行推理和决策。在本文中，我们提出了一个系统，通过利用部署位置的语义信息并将其集成到机器人定位和导航系统中，使得移动机器人能够以位置感知和操作友好的方式进行操作。我们将建筑信息模型（BIM）集成到机器人操作系统（ROS）中，生成拓扑图和度量图，并提供给分层路径规划器（全局和局部）。地图合并算法将新发现的障碍物整合到度量地图中，而基于UWB的定位系统则检测要注册回语义数据库的设备。在建筑物和建筑工地的模拟和实际部署中验证了结果。 摘要：Several deployment locations of mobile robotic systems are human made (i.e. urban firefighter, building inspection, property security) and the manager may have access to domain-specific knowledge about the place, which can provide semantic contextual information allowing better reasoning and decision making. In this paper we propose a system that allows a mobile robot to operate in a location-aware and operator-friendly way, by leveraging semantic information from the deployment location and integrating it to the robots localization and navigation systems. We integrate Building Information Models (BIM) into the Robotic Operating System (ROS), to generate topological and metric maps fed to an layered path planner (global and local). A map merging algorithm integrates newly discovered obstacles into the metric map, while a UWB-based localization system detects equipment to be registered back into the semantic database. The results are validated in simulation and real-life deployments in buildings and construction sites.

【2】 Position-based Dynamics Simulator of Brain Deformations for Path Planning and Intra-Operative Control in Keyhole Neurosurgery 标题：基于位置的脑变形动力学模拟器在锁孔神经外科路径规划和术中控制中的应用

作者：Alice Segato,Chiara Di Vece,Sara Zucchelli,Marco Di Marzo,Thomas Wendler,Mohammad Farid Azampour,Stefano Galvan,Riccardo Secoli,Elena De Momi 备注：8 pages, 8 figures. This article has been accepted for publication in a future issue of IEEE Robotics and Automation Letters, but has not been fully edited. Content may change prior to final publication. 2377-3766 (c) 2021 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. A. Segato and C. Di Vece equally contributed 链接：https://arxiv.org/abs/2106.10206 摘要：机器人辅助手术中的许多任务需要规划和控制机械手与高度可变形物体交互的运动。本研究提出了一个基于位置动力学（PBD）模拟的真实的、有时间限制的模拟器，用于模拟因导管插入引起的脑变形，用于锁孔手术的术前路径规划和术中引导。通过考虑变形模型中的不确定性、噪声传感和不可预测的驱动，它最大限度地提高了成功的概率。在平行六面体模拟体模上对PBD变形参数进行初始化，得到脑白质的合理起始猜测。通过将获得的位移与复合水凝胶模型中导管插入的变形数据进行比较，对其进行校准。了解大脑灰质结构的不同行为，对参数进行微调，得到一个广义人脑模型。将脑结构的平均位移值与文献值进行比较。该模拟器的数值模型采用了一种与文献相关的新方法，并通过使用体内动物试验的变形数据进行验证，证明其与真实的大脑变形非常接近，平均错配率为4.73$\pm$2.15%。该模型的稳定性、准确性和实时性使其适合于为KN路径规划、术前路径规划和术中引导创造一个动态环境。 摘要：Many tasks in robot-assisted surgery require planning and controlling manipulators' motions that interact with highly deformable objects. This study proposes a realistic, time-bounded simulator based on Position-based Dynamics (PBD) simulation that mocks brain deformations due to catheter insertion for pre-operative path planning and intra-operative guidance in keyhole surgical procedures. It maximizes the probability of success by accounting for uncertainty in deformation models, noisy sensing, and unpredictable actuation. The PBD deformation parameters were initialized on a parallelepiped-shaped simulated phantom to obtain a reasonable starting guess for the brain white matter. They were calibrated by comparing the obtained displacements with deformation data for catheter insertion in a composite hydrogel phantom. Knowing the gray matter brain structures' different behaviors, the parameters were fine-tuned to obtain a generalized human brain model. The brain structures' average displacement was compared with values in the literature. The simulator's numerical model uses a novel approach with respect to the literature, and it has proved to be a close match with real brain deformations through validation using recorded deformation data of in-vivo animal trials with a mean mismatch of 4.73$\pm$2.15%. The stability, accuracy, and real-time performance make this model suitable for creating a dynamic environment for KN path planning, pre-operative path planning, and intra-operative guidance.

【3】 Towards Robotic Laboratory Automation Plug & Play: The "LAPP" Framework

作者：Ádám Wolf,David Wolton,Josef Trapl,Julien Janda,Stefan Romeder-Finger,Thomas Gatternig,Jean-Baptiste Farcet,Péter Galambos,Károly Széll 机构： P´eter Galambos, and K´aroly Sz´ell 链接：https://arxiv.org/abs/2106.10129 摘要：提高制药实验室和生产设施的自动化水平对向患者提供药品起着至关重要的作用。然而，这一领域的特殊要求使得采用其他行业的尖端技术具有挑战性。本文概述了相关的方法，以及如何在制药行业，特别是在开发实验室中使用这些方法。最近的进展包括能够处理复杂任务的柔性移动机械手的应用。然而，由于协议的多样性，将来自许多不同供应商的设备集成到端到端自动化系统中是非常复杂的。因此，人们考虑了各种标准化方法，并提出了一个概念，使之更进一步。这个概念使一个带有视觉系统的移动机械手能够“学习”每个设备的姿势，并利用条形码从通用云数据库中获取接口信息。这些信息包括控制和通信协议定义以及操作设备所需的机器人动作表示。为了定义与设备相关的移动，设备除了条形码外，还必须有一个基准标记作为标准。这一概念将在后续文件的适当研究活动之后加以阐述。 摘要：Increasing the level of automation in pharmaceutical laboratories and production facilities plays a crucial role in delivering medicine to patients. However, the particular requirements of this field make it challenging to adapt cutting-edge technologies present in other industries. This article provides an overview of relevant approaches and how they can be utilized in the pharmaceutical industry, especially in development laboratories. Recent advancements include the application of flexible mobile manipulators capable of handling complex tasks. However, integrating devices from many different vendors into an end-to-end automation system is complicated due to the diversity of protocols. Therefore, various approaches for standardization have been considered, and a concept has been proposed for taking them a step further. This concept enables a mobile manipulator with a vision system to ``learn'' the pose of each device and - utilizing a barcode - fetch interface information from a universal cloud database. This information includes control and communication protocol definitions and a representation of robot actions needed to operate the device. In order to define the movements in relation to the device, devices have to feature - besides the barcode - a fiducial marker as standard. The concept will be elaborated following appropriate research activities in follow-up papers.

【4】 Virtual Temporal Samples for Recurrent Neural Networks: applied to semantic segmentation in agriculture 标题：基于递归神经网络的虚拟时间样本在农业语义分割中的应用

作者：Alireza Ahmadi,Michael Halstead,Chris McCool 机构：University of Bonn, Nussallee , Bonn , Germany 链接：https://arxiv.org/abs/2106.10118 摘要：本文探讨了在没有时间标记数据的农业机器人环境下进行时间语义分割的可能性。我们建议从标记的静止图像生成虚拟时间样本来实现这一点。这使得我们无需额外的注释工作，就可以生成虚拟标记的时间序列。通常，为了训练递归神经网络（RNN），需要从视频（时间）序列中提取标记样本，这是一项费力的工作，并且阻碍了这方面的工作。通过生成虚拟时间样本，我们证明了训练一个轻量级RNN对两个具有挑战性的农业数据集进行语义分割是可能的。我们的结果表明，通过使用虚拟样本训练时间语义切分器，我们可以在甜椒和甜菜数据集上分别提高4.6和4.9的绝对性能。这表明我们的虚拟数据增强技术能够准确地对农业图像进行时间分类，而不需要使用复杂的合成数据生成技术，也不需要标记大量的时间序列。 摘要：This paper explores the potential for performing temporal semantic segmentation in the context of agricultural robotics without temporally labelled data. We achieve this by proposing to generate virtual temporal samples from labelled still images. This allows us, with no extra annotation effort, to generate virtually labelled temporal sequences. Normally, to train a recurrent neural network (RNN), labelled samples from a video (temporal) sequence are required which is laborious and has stymied work in this direction. By generating virtual temporal samples, we demonstrate that it is possible to train a lightweight RNN to perform semantic segmentation on two challenging agricultural datasets. Our results show that by training a temporal semantic segmenter using virtual samples we can increase the performance by an absolute amount of 4.6 and 4.9 on sweet pepper and sugar beet datasets, respectively. This indicates that our virtual data augmentation technique is able to accurately classify agricultural images temporally without the use of complicated synthetic data generation techniques nor with the overhead of labelling large amounts of temporal sequences.

【5】 Towards Distraction-Robust Active Visual Tracking 标题：朝向分散注意力的鲁棒主动视觉跟踪

作者：Fangwei Zhong,Peng Sun,Wenhan Luo,Tingyun Yan,Yizhou Wang 备注：To appear in ICML2021 链接：https://arxiv.org/abs/2106.10110 摘要：在主动视觉跟踪中，分心物体的出现是众所周知的困难，因为分心物常常通过遮挡目标或带来混乱的外观来误导跟踪器。为了解决这个问题，我们提出了一个混合的合作竞争多智能体博弈，其中一个目标和多个干扰者组成一个协作团队，与一个跟踪器对抗，使其无法跟踪。通过在游戏中的学习，分心者的各种分心行为自然出现，从而暴露了跟踪器的弱点，增强了跟踪器的分心鲁棒性。为了有效的学习，我们提出了一系列实用的方法，包括分心者的奖励函数、跨模式的师生学习策略和跟踪器的重复注意机制。实验结果表明，该跟踪器具有良好的分心鲁棒主动视觉跟踪性能，并能很好地推广到不可见环境中。我们还证明了多智能体博弈可以用来对抗地测试跟踪器的鲁棒性。 摘要：In active visual tracking, it is notoriously difficult when distracting objects appear, as distractors often mislead the tracker by occluding the target or bringing a confusing appearance. To address this issue, we propose a mixed cooperative-competitive multi-agent game, where a target and multiple distractors form a collaborative team to play against a tracker and make it fail to follow. Through learning in our game, diverse distracting behaviors of the distractors naturally emerge, thereby exposing the tracker's weakness, which helps enhance the distraction-robustness of the tracker. For effective learning, we then present a bunch of practical methods, including a reward function for distractors, a cross-modal teacher-student learning strategy, and a recurrent attention mechanism for the tracker. The experimental results show that our tracker performs desired distraction-robust active visual tracking and can be well generalized to unseen environments. We also show that the multi-agent game can be used to adversarially test the robustness of trackers.

【6】 Under the Sand: Navigation and Localization of a Small Unmanned Aerial Vehicle for Landmine Detection with Ground Penetrating Synthetic Aperture Radar 标题：沙下：探地合成孔径雷达探雷小型无人机的导航与定位

作者：Rik Bähnemann,Nicholas Lawrance,Lucas Streichenberg,Jen Jen Chung,Michael Pantic,Alexander Grathwohl,Christian Waldschmidt,Roland Siegwart 机构：Autonomous Systems Lab, ETH Zurich, Switzerland, Microwave Engineering, Ulm University, Germany 备注：Submitted to Field Robotics journal in June 2021 链接：https://arxiv.org/abs/2106.10108 摘要：安装在小型无人机上的探地雷达是协助人道主义扫雷的一种很有前途的工具。然而，合成孔径雷达图像的质量取决于雷达天线的精确运动估计以及无人机的信息视点生成。介绍了一种全自动机载探地合成孔径雷达（GPSAR）系统。该系统包括一个空间校准和时间同步的工业级传感器套件，可实现地面导航、雷达成像和光学成像。一个定制的任务规划框架允许生成和自动执行带状地图和圆形GPSAR轨迹，这些轨迹由地面控制，也可用于航空成像测量飞行。基于因子图的状态估计器融合来自双接收机实时运动学（RTK）全球导航卫星系统（GNSS）和惯性测量单元（IMU）的测量，以获得精确、高速的平台位置和方向。地面真值实验表明，在定位率为1khz的情况下，传感器定时精度为0.8{\mu}s和0.1{\mu}s。与不确定航向初始化的单位置因子相比，双位置因子法在线定位精度提高了40%，批量定位精度提高了59%。我们的现场试验验证了定位精度和精度，使相干雷达测量和探测雷达目标埋在沙子。这验证了作为空中地雷探测系统的潜力。 摘要：Ground penetrating radar mounted on a small unmanned aerial vehicle (UAV) is a promising tool to assist humanitarian landmine clearance. However, the quality of synthetic aperture radar images depends on accurate and precise motion estimation of the radar antennas as well as generating informative viewpoints with the UAV. This paper presents a complete and automatic airborne ground-penetrating synthetic aperture radar (GPSAR) system. The system consists of a spatially calibrated and temporally synchronized industrial grade sensor suite that enables navigation above ground level, radar imaging, and optical imaging. A custom mission planning framework allows generation and automatic execution of stripmap and circular GPSAR trajectories controlled above ground level as well as aerial imaging survey flights. A factor graph based state estimator fuses measurements from dual receiver real-time kinematic (RTK) global navigation satellite system (GNSS) and an inertial measurement unit (IMU) to obtain precise, high rate platform positions and orientations. Ground truth experiments showed sensor timing as accurate as 0.8 {\mu}s and as precise as 0.1 {\mu}s with localization rates of 1 kHz. The dual position factor formulation improves online localization accuracy up to 40 % and batch localization accuracy up to 59 % compared to a single position factor with uncertain heading initialization. Our field trials validated a localization accuracy and precision that enables coherent radar measurement addition and detection of radar targets buried in sand. This validates the potential as an aerial landmine detection system.

【7】 Learning to Plan via a Multi-Step Policy Regression Method 标题：通过多步策略回归方法学习计划

作者：Stefan Wagner,Michael Janschek,Tobias Uelwer,Stefan Harmeling 机构：Department of Computer Science, Heinrich Heine University D¨usseldorf, Germany 备注：Accepted at the 30th International Conference on Artificial Neural Networks (ICANN 2021) 链接：https://arxiv.org/abs/2106.10075 摘要：我们提出了一种新的方法来提高推理性能的环境中，需要一个特定的序列的行动，以解决。例如，在迷宫环境中，理想情况下确定了最佳路径。我们希望学习一个可以提前预测n个动作的策略，而不是一步一步地学习一个策略。我们提出的策略水平回归（PHR）方法利用A2C采样的环境知识，在一个策略蒸馏设置中学习一个n维的策略向量，每个观测值产生n个连续动作。我们在微网格和Pong环境下对我们的方法进行了测试，通过成功地预测单个观测的动作序列，在推理过程中显示出极大的加速。 摘要：We propose a new approach to increase inference performance in environments that require a specific sequence of actions in order to be solved. This is for example the case for maze environments where ideally an optimal path is determined. Instead of learning a policy for a single step, we want to learn a policy that can predict n actions in advance. Our proposed method called policy horizon regression (PHR) uses knowledge of the environment sampled by A2C to learn an n dimensional policy vector in a policy distillation setup which yields n sequential actions per observation. We test our method on the MiniGrid and Pong environments and show drastic speedup during inference time by successfully predicting sequences of actions on a single observation.

【8】 Improved Radar Localization on Lidar Maps Using Shared Embedding 标题：利用共享嵌入改进激光雷达地图上的雷达定位

作者：Huan Yin,Yue Wang,Rong Xiong 机构： m + pos(F) − neg(F))( 1)The authors are with the State Key Laboratory of Industrial Control Tech-nology and Institute of Cyber-Systems and Control, Zhejiang University 备注：Extended abstract. Spotlight Talk at Radar Perception for All-Weather Autonomy Workshop of ICRA 2021 链接：https://arxiv.org/abs/2106.10000 摘要：我们提出了一个异构定位框架，用于解决预先构建的lidar地图上的雷达全局定位和姿态跟踪问题。为了弥补传感模式之间的差距，构建了深度神经网络，为雷达扫描和激光雷达地图创建共享的嵌入空间。在这里学习到的特征嵌入支持相似性度量，从而分别提高地图检索和数据匹配。在RobotCar和MulRan数据集上，通过与Scan-Context和RaLL的比较，证明了该框架的有效性。此外，与原RaLL相比，本文提出的位姿跟踪流水线具有较少的神经网络。 摘要：We present a heterogeneous localization framework for solving radar global localization and pose tracking on pre-built lidar maps. To bridge the gap of sensing modalities, deep neural networks are constructed to create shared embedding space for radar scans and lidar maps. Herein learned feature embeddings are supportive for similarity measurement, thus improving map retrieval and data matching respectively. In RobotCar and MulRan datasets, we demonstrate the effectiveness of the proposed framework with the comparison to Scan Context and RaLL. In addition, the proposed pose tracking pipeline is with less neural networks compared to the original RaLL.

【9】 Human-Aware Navigation Planner for Diverse Human-Robot Contexts 标题：适用于不同人-机器人环境的人感知导航规划器

作者：Phani Singamaneni,Anthony Favier,Rachid Alami 机构： Universite de Toulouse, fr 2Authors are with Artificial and Natural Intelligence Toulouse Institute(ANITI)Our main contributions in this work are threefold and aresummarized below 链接：https://arxiv.org/abs/2106.09971 摘要：随着越来越多的机器人被部署到人类环境中，人类感知的导航规划器需要处理室内和室外环境中发生的多个上下文。在本文中，我们提出了一个可调的人类感知机器人导航规划器，可以处理各种人类机器人环境。本文介绍了规划器的体系结构，讨论了它的特点和一些实现细节。然后，我们提出了一个详细的分析各种模拟人类机器人的情况下使用所提出的规划以及一些定量的结果。最后，我们在一个真实的机器人上部署我们的系统后，在一个真实的场景中展示了结果。 摘要：As more robots are being deployed into human environments, a human-aware navigation planner needs to handle multiple contexts that occur in indoor and outdoor environments. In this paper, we propose a tunable human-aware robot navigation planner that can handle a variety of humanrobot contexts. We present the architecture of the planner and discuss the features and some implementation details. Then we present a detailed analysis of various simulated humanrobot contexts using the proposed planner along with some quantitative results. Finally, we show the results in a real-world scenario after deploying our system on a real robot.

【10】 Variable-Grasping-Mode Gripper With Different Finger Structures For Grasping Small-Sized Items 标题：一种不同手指结构的抓取小型物品的可变抓取式抓取器

作者：Tetsuyou Watanabe,Kota Morino,Yoshitatsu Asama,Seiji Nishitani,Ryo Toshima 机构：Institute of, Science and Engineering, Kanazawa University, Kakuma-machi, Kanazawa, Nishitani, and R. Toshima are with the Panasonic Corporation., This letter has supplementary downloadable material available at 备注：None 链接：https://arxiv.org/abs/2106.09957 摘要：这封信提出了一种新型的小型夹具能够抓住各种类型的小型物品从平面组装小型设备。该夹持器采用单执行机构，实现了两种夹持方式：平行夹持和翻转夹持。夹持器的模式可以通过接触平面（如桌子）来切换。处理薄厚度和轻重量是在尝试抓住小尺寸物品时面临的关键挑战。虽然平行夹持器在处理小物件时是有效的，但是平行夹持器所能抓取的物件的厚度是有限的。因此，采用翻转模式来抓取超过此阈值的项目。在翻转模式下，一个手指抬起物品，而另一个手指从上方握住物品以防止其弹出。所提出的夹持器能够从桌子上拾取多种类型的物品，包括薄（0.05 mm）和轻（0.007 g）物品。 摘要：This letter presents a novel small gripper capable of grasping various types of small-sized items from flat surfaces for the assembly of small devices. Using a single actuator, the proposed gripper realizes two grasping modes: parallel-grip and turn-over modes. The gripper's mode can be switched via contact with a flat surface, such as a table. Handling thin thicknesses and light weights are the key challenges faced in attempts to grasp small-sized items. Although parallel grippers are effective in handling small items, there is a limit to the thinness of objects that can be grasped by parallel grippers. Accordingly, the turn-over mode was adopted to grasp items that exceeded this threshold. In the turn-over mode, one finger lifts the item, while another finger holds the item from above to keep it from flicking out. The proposed gripper is capable of picking up several types of items from a table, including thin (0.05 mm) and lightweight (0.007 g) items.

【11】 Goal-Directed Planning by Reinforcement Learning and Active Inference 标题：基于强化学习和主动推理的目标导向规划

作者：Dongqi Han,Kenji Doya,Jun Tani 机构：Cognitive Neurorobotics Research Unit, Okinawa Institute of Science and Technology, Okinawa, Japan, Neural Computation Unit 备注：Work in progress 链接：https://arxiv.org/abs/2106.09938 摘要：目标导向行为和习惯性行为有什么区别？我们提出了一种新的贝叶斯推理决策计算框架，其中所有的东西都集成为一个完整的神经网络模型。该模型通过自我探索学习预测环境状态转换，并通过随机内部状态采样$z$生成运动行为。习惯性行为是通过强化学习获得的，它是从$z$的先验分布中获得的。目标定向行为由$z$的后验分布决定，通过计划，使用主动推理，使目标观察的自由能最小化。我们通过在一个有摄像头观察和连续运动动作的感觉运动导航任务中的实验，证明了该框架的有效性。 摘要：What is the difference between goal-directed and habitual behavior? We propose a novel computational framework of decision making with Bayesian inference, in which everything is integrated as an entire neural network model. The model learns to predict environmental state transitions by self-exploration and generating motor actions by sampling stochastic internal states $z$. Habitual behavior, which is obtained from the prior distribution of $z$, is acquired by reinforcement learning. Goal-directed behavior is determined from the posterior distribution of $z$ by planning, using active inference, to minimize the free energy for goal observation. We demonstrate the effectiveness of the proposed framework by experiments in a sensorimotor navigation task with camera observations and continuous motor actions.

【12】 Development of a conversing and body temperature scanning autonomously navigating robot to help screen for COVID-19 标题：用于冠状病毒筛查的反转体温自主导航机器人的研制

作者：Ryan Kim 链接：https://arxiv.org/abs/2106.09894 摘要：在整个COVID-19大流行期间，患者表现出的最常见症状是发烧，这导致使用温度扫描作为检测潜在病毒携带者的先发制人措施。手持式温度计的人类雇员已经被用来完成这项任务，但是这使他们处于危险之中，因为他们无法与外界保持距离，而且这种方法的连续性导致了极大的不便和低效。提出的解决方案是一种自主导航机器人，能够转换和扫描人的体温，以检测发烧并帮助筛选COVID-19。为了实现这一目标，机器人必须能够（1）自主导航，（2）检测和跟踪人，（3）获取个体的体温读数，当温度超过38{\deg}C时与之交谈。一个自主导航的移动机器人由一个由人脸跟踪算法控制的机械手和一个由热摄像机、智能手机和聊天机器人组成的末端执行器组成。我们的目标是开发一个功能强大的解决方案来执行上述任务。此外，还将介绍遇到的技术挑战及其工程解决方案，并就在接近商业化时可纳入的增强功能提出建议。 摘要：Throughout the COVID-19 pandemic, the most common symptom displayed by patients has been a fever, leading to the use of temperature scanning as a preemptive measure to detect potential carriers of the virus. Human employees with handheld thermometers have been used to fulfill this task, however this puts them at risk as they cannot be physically distanced and the sequential nature of this method leads to great inconveniences and inefficiency. The proposed solution is an autonomously navigating robot capable of conversing and scanning people's temperature to detect fevers and help screen for COVID-19. To satisfy this objective, the robot must be able to (1) navigate autonomously, (2) detect and track people, and (3) get individuals' temperature reading and converse with them if it exceeds 38{\deg}C. An autonomously navigating mobile robot is used with a manipulator controlled using a face tracking algorithm, and an end effector consisting of a thermal camera, smartphone, and chatbot. The goal is to develop a functioning solution that performs the above tasks. In addition, technical challenges encountered and their engineering solutions will be presented, and recommendations will be made for enhancements that could be incorporated when approaching commercialization.

【13】 Optimizing robotic swarm based construction tasks 标题：基于机器人群的施工任务优化

作者：Teshan Liyanage,Subha Fernando 机构：Universiy of Moratuwa, Colombo, Sri Lanka, University of Moratuwa 备注：4 pages, 3 figures, submitted to 2021 7th International Conference on Control, Automation and Robotics (ICCAR) Singapore 链接：https://arxiv.org/abs/2106.09749 摘要：自然界中的群居昆虫，如蚂蚁、白蚁和蜜蜂，在一个非常有效的过程中协同构建它们的群体。在这些昆虫群落中，每一种昆虫都参与了各自的构建任务，表现出个体实体的冗余和平行行为。但是，由于现有的群机器人构建方法的局限性，这些群机器人行为的机器人适应性还没有在足够大的范围内适应现实世界的普遍应用。本文提出了一种结合现有群构造方法的群机器人系统，该系统能够以优化的方式构造给定的二维形状。 摘要：Social insects in nature such as ants, termites and bees construct their colonies collaboratively in a very efficient process. In these swarms, each insect contributes to the construction task individually showing redundant and parallel behavior of individual entities. But the robotics adaptations of these swarm's behaviors haven't yet made it to the real world at a large enough scale of commonly being used due to the limitations in the existing approaches to the swarm robotics construction. This paper presents an approach that combines the existing swarm construction approaches which results in a swarm robotic system, capable of constructing a given 2 dimensional shape in an optimized manner.