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cs.RO機器人相關，共計13篇
【1】 Semantic navigation with domain knowledge
标題：基于領域知識的語義導航
作者：Rafael Gomes Braga,Sina Karimi,Ulrich Dah-Achinanon,Ivanka Iordanova,David St-Onge
機構：Received: date Accepted: date
備注：12 pages, 10 figures. arXiv admin note: substantial text overlap with arXiv:2104.10296
連結：https://arxiv.org/abs/2106.10220
摘要：移動機器人系統的幾個部署位置是人為的（即城市消防員、建築檢查、财産安全），管理者可以通路有關該位置的特定領域的知識，這些知識可以提供語義上下文資訊，以便更好地進行推理和決策。在本文中，我們提出了一個系統，通過利用部署位置的語義資訊并将其內建到機器人定位和導航系統中，使得移動機器人能夠以位置感覺和操作友好的方式進行操作。我們将建築資訊模型（BIM）內建到機器人作業系統（ROS）中，生成拓撲圖和度量圖，并提供給分層路徑規劃器（全局和局部）。地圖合并算法将新發現的障礙物整合到度量地圖中，而基于UWB的定位系統則檢測要注冊回語義資料庫的裝置。在建築物和建築工地的模拟和實際部署中驗證了結果。
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
标題：基于位置的腦變形動力學模拟器在鎖孔神經外科路徑規劃和術中控制中的應用
作者：Alice Segato,Chiara Di Vece,Sara Zucchelli,Marco Di Marzo,Thomas Wendler,Mohammad Farid Azampour,Stefano Galvan,Riccardo Secoli,Elena De Momi
備注：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
連結：https://arxiv.org/abs/2106.10206
摘要：機器人輔助手術中的許多任務需要規劃和控制機械手與高度可變形物體互動的運動。本研究提出了一個基于位置動力學（PBD）模拟的真實的、有時間限制的模拟器，用于模拟因導管插入引起的腦變形，用于鎖孔手術的術前路徑規劃和術中引導。通過考慮變形模型中的不确定性、噪聲傳感和不可預測的驅動，它最大限度地提高了成功的機率。在平行六面體模拟體模上對PBD變形參數進行初始化，得到腦白質的合理起始猜測。通過将獲得的位移與複合水凝膠模型中導管插入的變形資料進行比較，對其進行校準。了解大腦灰質結構的不同行為，對參數進行微調，得到一個廣義人腦模型。将腦結構的平均位移值與文獻值進行比較。該模拟器的數值模型采用了一種與文獻相關的新方法，并通過使用體内動物試驗的變形資料進行驗證，證明其與真實的大腦變形非常接近，平均錯配率為4.73$\pm$2.15%。該模型的穩定性、準确性和實時性使其适合于為KN路徑規劃、術前路徑規劃和術中引導創造一個動态環境。
摘要：Many tasks in robot-assisted surgery require planning and controlling manipulators' motions that interact with highly deformable objects. This study proposes a realistic, time-bounded simulator based on Position-based Dynamics (PBD) simulation that mocks brain deformations due to catheter insertion for pre-operative path planning and intra-operative guidance in keyhole surgical procedures. It maximizes the probability of success by accounting for uncertainty in deformation models, noisy sensing, and unpredictable actuation. The PBD deformation parameters were initialized on a parallelepiped-shaped simulated phantom to obtain a reasonable starting guess for the brain white matter. They were calibrated by comparing the obtained displacements with deformation data for catheter insertion in a composite hydrogel phantom. Knowing the gray matter brain structures' different behaviors, the parameters were fine-tuned to obtain a generalized human brain model. The brain structures' average displacement was compared with values in the literature. The simulator's numerical model uses a novel approach with respect to the literature, and it has proved to be a close match with real brain deformations through validation using recorded deformation data of in-vivo animal trials with a mean mismatch of 4.73$\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
機構： P´eter Galambos, and K´aroly Sz´ell
連結：https://arxiv.org/abs/2106.10129
摘要：提高制藥實驗室和生産設施的自動化水準對向患者提供藥品起着至關重要的作用。然而，這一領域的特殊要求使得采用其他行業的尖端技術具有挑戰性。本文概述了相關的方法，以及如何在制藥行業，特别是在開發實驗室中使用這些方法。最近的進展包括能夠處理複雜任務的柔性移動機械手的應用。然而，由于協定的多樣性，将來自許多不同供應商的裝置內建到端到端自動化系統中是非常複雜的。是以，人們考慮了各種标準化方法，并提出了一個概念，使之更進一步。這個概念使一個帶有視覺系統的移動機械手能夠“學習”每個裝置的姿勢，并利用條形碼從通用雲資料庫中擷取接口資訊。這些資訊包括控制和通信協定定義以及操作裝置所需的機器人動作表示。為了定義與裝置相關的移動，裝置除了條形碼外，還必須有一個基準标記作為标準。這一概念将在後續檔案的适當研究活動之後加以闡述。
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
标題：基于遞歸神經網絡的虛拟時間樣本在農業語義分割中的應用
作者：Alireza Ahmadi,Michael Halstead,Chris McCool
機構：University of Bonn, Nussallee , Bonn , Germany
連結：https://arxiv.org/abs/2106.10118
摘要：本文探讨了在沒有時間标記資料的農業機器人環境下進行時間語義分割的可能性。我們建議從标記的靜止圖像生成虛拟時間樣本來實作這一點。這使得我們無需額外的注釋工作，就可以生成虛拟标記的時間序列。通常，為了訓練遞歸神經網絡（RNN），需要從視訊（時間）序列中提取标記樣本，這是一項費力的工作，并且阻礙了這方面的工作。通過生成虛拟時間樣本，我們證明了訓練一個輕量級RNN對兩個具有挑戰性的農業資料集進行語義分割是可能的。我們的結果表明，通過使用虛拟樣本訓練時間語義切分器，我們可以在甜椒和甜菜資料集上分别提高4.6和4.9的絕對性能。這表明我們的虛拟資料增強技術能夠準确地對農業圖像進行時間分類，而不需要使用複雜的合成資料生成技術，也不需要标記大量的時間序列。
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
标題：朝向分散注意力的魯棒主動視覺跟蹤
作者：Fangwei Zhong,Peng Sun,Wenhan Luo,Tingyun Yan,Yizhou Wang
備注：To appear in ICML2021
連結：https://arxiv.org/abs/2106.10110
摘要：在主動視覺跟蹤中，分心物體的出現是衆所周知的困難，因為分心物常常通過遮擋目标或帶來混亂的外觀來誤導跟蹤器。為了解決這個問題，我們提出了一個混合的合作競争多智能體博弈，其中一個目标和多個幹擾者組成一個協作團隊，與一個跟蹤器對抗，使其無法跟蹤。通過在遊戲中的學習，分心者的各種分心行為自然出現，進而暴露了跟蹤器的弱點，增強了跟蹤器的分心魯棒性。為了有效的學習，我們提出了一系列實用的方法，包括分心者的獎勵函數、跨模式的師生學習政策和跟蹤器的重複注意機制。實驗結果表明，該跟蹤器具有良好的分心魯棒主動視覺跟蹤性能，并能很好地推廣到不可見環境中。我們還證明了多智能體博弈可以用來對抗地測試跟蹤器的魯棒性。
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
标題：沙下：探地合成孔徑雷達探雷小型無人機的導航與定位
作者：Rik Bähnemann,Nicholas Lawrance,Lucas Streichenberg,Jen Jen Chung,Michael Pantic,Alexander Grathwohl,Christian Waldschmidt,Roland Siegwart
機構：Autonomous Systems Lab, ETH Zurich, Switzerland, Microwave Engineering, Ulm University, Germany
備注：Submitted to Field Robotics journal in June 2021
連結：https://arxiv.org/abs/2106.10108
摘要：安裝在小型無人機上的探地雷達是協助人道主義掃雷的一種很有前途的工具。然而，合成孔徑雷達圖像的品質取決于雷達天線的精确運動估計以及無人機的資訊視點生成。介紹了一種全自動機載探地合成孔徑雷達（GPSAR）系統。該系統包括一個空間校準和時間同步的工業級傳感器套件，可實作地面導航、雷達成像和光學成像。一個定制的任務規劃架構允許生成和自動執行帶狀地圖和圓形GPSAR軌迹，這些軌迹由地面控制，也可用于航空成像測量飛行。基于因子圖的狀态估計器融合來自雙接收機實時運動學（RTK）全球導航衛星系統（GNSS）和慣性測量單元（IMU）的測量，以獲得精确、高速的平台位置和方向。地面真值實驗表明，在定位率為1khz的情況下，傳感器定時精度為0.8{\mu}s和0.1{\mu}s。與不确定航向初始化的機關置因子相比，雙位置因子法線上定位精度提高了40%，批量定位精度提高了59%。我們的現場試驗驗證了定位精度和精度，使相幹雷達測量和探測雷達目标埋在沙子。這驗證了作為空中地雷探測系統的潛力。
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
标題：通過多步政策回歸方法學習計劃
作者：Stefan Wagner,Michael Janschek,Tobias Uelwer,Stefan Harmeling
機構：Department of Computer Science, Heinrich Heine University D¨usseldorf, Germany
備注：Accepted at the 30th International Conference on Artificial Neural Networks (ICANN 2021)
連結：https://arxiv.org/abs/2106.10075
摘要：我們提出了一種新的方法來提高推理性能的環境中，需要一個特定的序列的行動，以解決。例如，在迷宮環境中，理想情況下确定了最佳路徑。我們希望學習一個可以提前預測n個動作的政策，而不是一步一步地學習一個政策。我們提出的政策水準回歸（PHR）方法利用A2C采樣的環境知識，在一個政策蒸餾設定中學習一個n維的政策向量，每個觀測值産生n個連續動作。我們在微網格和Pong環境下對我們的方法進行了測試，通過成功地預測單個觀測的動作序列，在推理過程中顯示出極大的加速。
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
标題：利用共享嵌入改進雷射雷達地圖上的雷達定位
作者：Huan Yin,Yue Wang,Rong Xiong
機構： 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
備注：Extended abstract. Spotlight Talk at Radar Perception for All-Weather Autonomy Workshop of ICRA 2021
連結：https://arxiv.org/abs/2106.10000
摘要：我們提出了一個異構定位架構，用于解決預先建構的lidar地圖上的雷達全局定位和姿态跟蹤問題。為了彌補傳感模式之間的差距，建構了深度神經網絡，為雷達掃描和雷射雷達地圖建立共享的嵌入空間。在這裡學習到的特征嵌入支援相似性度量，進而分别提高地圖檢索和資料比對。在RobotCar和MulRan資料集上，通過與Scan-Context和RaLL的比較，證明了該架構的有效性。此外，與原RaLL相比，本文提出的位姿跟蹤流水線具有較少的神經網絡。
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
标題：适用于不同人-機器人環境的人感覺導航規劃器
作者：Phani Singamaneni,Anthony Favier,Rachid Alami
機構： 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
連結：https://arxiv.org/abs/2106.09971
摘要：随着越來越多的機器人被部署到人類環境中，人類感覺的導航規劃器需要處理室内和室外環境中發生的多個上下文。在本文中，我們提出了一個可調的人類感覺機器人導航規劃器，可以處理各種人類機器人環境。本文介紹了規劃器的體系結構，讨論了它的特點和一些實作細節。然後，我們提出了一個詳細的分析各種模拟人類機器人的情況下使用所提出的規劃以及一些定量的結果。最後，我們在一個真實的機器人上部署我們的系統後，在一個真實的場景中展示了結果。
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
标題：一種不同手指結構的抓取小型物品的可變抓取式抓取器
作者：Tetsuyou Watanabe,Kota Morino,Yoshitatsu Asama,Seiji Nishitani,Ryo Toshima
機構：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
備注：None
連結：https://arxiv.org/abs/2106.09957
摘要：這封信提出了一種新型的小型夾具能夠抓住各種類型的小型物品從平面組裝小型裝置。該夾持器采用單執行機構，實作了兩種夾持方式：平行夾持和翻轉夾持。夾持器的模式可以通過接觸平面（如桌子）來切換。處理薄厚度和輕重量是在嘗試抓住小尺寸物品時面臨的關鍵挑戰。雖然平行夾持器在處理小物件時是有效的，但是平行夾持器所能抓取的物件的厚度是有限的。是以，采用翻轉模式來抓取超過此門檻值的項目。在翻轉模式下，一個手指擡起物品，而另一個手指從上方握住物品以防止其彈出。所提出的夾持器能夠從桌子上拾取多種類型的物品，包括薄（0.05 mm）和輕（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
标題：基于強化學習和主動推理的目标導向規劃
作者：Dongqi Han,Kenji Doya,Jun Tani
機構：Cognitive Neurorobotics Research Unit, Okinawa Institute of Science and Technology, Okinawa, Japan, Neural Computation Unit
備注：Work in progress
連結：https://arxiv.org/abs/2106.09938
摘要：目标導向行為和習慣性行為有什麼差別？我們提出了一種新的貝葉斯推理決策計算架構，其中所有的東西都內建為一個完整的神經網絡模型。該模型通過自我探索學習預測環境狀态轉換，并通過随機内部狀态采樣$z$生成運動行為。習慣性行為是通過強化學習獲得的，它是從$z$的先驗分布中獲得的。目标定向行為由$z$的後驗分布決定，通過計劃，使用主動推理，使目标觀察的自由能最小化。我們通過在一個有攝像頭觀察和連續運動動作的感覺運動導航任務中的實驗，證明了該架構的有效性。
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
标題：用于冠狀病毒篩查的反轉體溫自主導航機器人的研制
作者：Ryan Kim
連結：https://arxiv.org/abs/2106.09894
摘要：在整個COVID-19大流行期間，患者表現出的最常見症狀是發燒，這導緻使用溫度掃描作為檢測潛在病毒攜帶者的先發制人措施。手持式溫度計的人類雇員已經被用來完成這項任務，但是這使他們處于危險之中，因為他們無法與外界保持距離，而且這種方法的連續性導緻了極大的不便和低效。提出的解決方案是一種自主導航機器人，能夠轉換和掃描人的體溫，以檢測發燒并幫助篩選COVID-19。為了實作這一目标，機器人必須能夠（1）自主導航，（2）檢測和跟蹤人，（3）擷取個體的體溫讀數，當溫度超過38{\deg}C時與之交談。一個自主導航的移動機器人由一個由人臉跟蹤算法控制的機械手和一個由熱錄影機、智能手機和聊天機器人組成的末端執行器組成。我們的目标是開發一個功能強大的解決方案來執行上述任務。此外，還将介紹遇到的技術挑戰及其工程解決方案，并就在接近商業化時可納入的增強功能提出建議。
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
标題：基于機器人群的施工任務優化
作者：Teshan Liyanage,Subha Fernando
機構：Universiy of Moratuwa, Colombo, Sri Lanka, University of Moratuwa
備注：4 pages, 3 figures, submitted to 2021 7th International Conference on Control, Automation and Robotics (ICCAR) Singapore
連結：https://arxiv.org/abs/2106.09749
摘要：自然界中的群居昆蟲，如螞蟻、白蟻和蜜蜂，在一個非常有效的過程中協同建構它們的群體。在這些昆蟲群落中，每一種昆蟲都參與了各自的建構任務，表現出個體實體的備援和平行行為。但是，由于現有的群機器人建構方法的局限性，這些群機器人行為的機器人适應性還沒有在足夠大的範圍内适應現實世界的普遍應用。本文提出了一種結合現有群構造方法的群機器人系統，該系統能夠以優化的方式構造給定的二維形狀。
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.