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2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference

作者： Sumarudin, A. Sutisna, Nana Syafalni, Infall Trilaksono, Bambang Riyanto Adiono, Trio Inst Teknol Bandung Sch Elect Engn & Informat Bandung Indonesia

ISBN: (纸本)9798350367331;9798350367348

This paper studies the application of Deep Q-Networks (DQN) for shortest-path planning on mobile robots. Implementing DQN on mobile robots poses challenges due to the limited computational resources of embedded systems. The process consists of two mode: the training mode, where an inference model of DQN with weight and bias parameters is generated, and the operational mode, where the DQN model is loaded onto the robot to perform actions in a simulation maze environment with gazebo. This study investigates optimal computational techniques for matrix operations, which are the primary operations in DQN. Computational methods performed on embedded system platforms utilizing GPUs (Jetson Xavier NX) involve studying matrix computation techniques using arrays, 2D arrays with GPUs, and tensors. Based on the experiment, the results show that for the DQN model used in shortest-path planning, computation based on an array matrix significantly speeds up the process.

关键词： robot programming

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Codeless, Inclusive, and End-To-End robotized Manipulations by Leveraging Extended Reality and Digital Twin Technologies 16

Codeless, Inclusive, and End-To-End Robotized Manipulations ...

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16th International Conference on Human System Interaction (HSI)

作者： Huebel, Niklas Kaigom, Eric Guiffo Frankfurt Univ Appl Sci Dept Comp Sci & Engn Frankfurt Germany

ISBN: (纸本)9798350362923;9798350362916

Although the importance of robots for efficient production in smart factories and assistance in home settings is well recognized, their usage in industry and society still faces challenges. A cause lies in the difficulty that comes with programming robots by writing several lines of code to complete basic tasks. Unfortunately, workers in particular as well as citizens in general rarely have the necessary technical background and experience to achieve this complex goal. Recruiting professional robot programmers creates a dependency and increases costs associated with production and assistance. This issue gets more complicated and urgent when robots are deployed to accommodate small batch sizes and heterogeneous preferences from Industry 4.0 and Industry 5.0 applications. In fact, the duration allocated to the changeover in robotized applications might be brief to meet concurrent economic objectives. Furthermore, generic motion primitives provided by robot vendors might not match with irregular and domain-specific Cartesian trajectories in industry, society, and in-between. This work embraces these issues by integrating extended reality and digital twins to propose an intuitive, inclusive, fast, and effective framework to program (i.e., plan and execute) robot motions. Novices can thereby move a manipulator to reach freely specified Cartesian frames and complete grasping maneuvers. This is done without writing any line of code. How our approach empowers citizens is shown by completing manipulations to grasp and relocate workpieces in practice.

关键词： robot programming

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LF-3PM: a LiDAR-based Framework for Perception-aware Planning with Perturbation-induced Metric

LF-3PM: a LiDAR-based Framework for Perception-aware Plannin...

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2024 International Conference on Intelligent robots and Systems

作者： Chai, Kaixin Xu, Long Wang, Qianhao Xu, Chao Yin, Peng Gao, Fei Zhejiang Univ State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China City Univ Hong Kong Hong Kong Peoples R China Zhejiang Univ Huzhou Inst Huzhou 313000 Peoples R China

ISBN: (纸本)9798350377712;9798350377705

Just as humans can become disoriented in featureless deserts or thick fogs, not all environments are conducive to the Localization Accuracy and Stability (LAS) of autonomous robots. This paper introduces an efficient framework designed to enhance LiDAR-based LAS through strategic trajectory generation, known as Perception-aware Planning. Unlike vision-based frameworks, the LiDAR-based requires different considerations due to unique sensor attributes. Our approach focuses on two main aspects: firstly, assessing the impact of LiDAR observations on LAS. We introduce a perturbation-induced metric to provide a comprehensive and reliable evaluation of LiDAR observations. Secondly, we aim to improve motion planning efficiency. By creating a Static Observation Loss Map (SOLM) as an intermediary, we logically separate the time-intensive evaluation and motion planning phases, significantly boosting the planning process. In the experimental section, we demonstrate the effectiveness of the proposed metrics across various scenes and the feature of trajectories guided by different metrics. Ultimately, our framework is tested in a real-world scenario, enabling the robot to actively choose topologies and orientations preferable for localization. The source code is accessible at https://***/ZJU-FAST-Lab/LF-3PM.

关键词： robot programming

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Human-robot interaction for robotic manipulator programming in Mixed Reality

Human-robot interaction for robotic manipulator programming ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Ostanin, Mikhail Mikhel, Stanislav Evlampiev, Alexey Skvortsova, Valeria Klimchik, Alexandr Innopolis Univ Ctr Technol Robot & Mechatron Components Innopolis Russia

ISBN: (纸本)9781728173955

The paper presents an approach for interactive programming of the robotic manipulator using mixed reality. The developed system is based on the HoloLens glasses connected through robotic Operation System to Unity engine and robotic manipulators. The system gives a possibility to recognize the real robot location by the point cloud analysis, to use virtual markers and menus for the task creation, to generate a trajectory for execution in the simulator or on the real manipulator. It also provides the possibility of scaling virtual and real worlds for more accurate planning. The proposed framework has been tested on pick-and-place and contact operations execution by UR10e and KUKA iiwa robots.

关键词： robot programming

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A Novel Multivariate Skew-Normal Mixture Model and Its Application in Path-Planning for Very-Large-Scale robotic Systems

A Novel Multivariate Skew-Normal Mixture Model and Its Appli...

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American Control Conference (ACC)

作者： Zhu, Pingping Liu, Chang Estephan, Peter Marshall Univ Fac Comp Sci & Elect Engn Huntington WV 25755 USA Peking Univ Dept Adv Mfg & Robot Coll Engn Beijing 100871 Peoples R China Marshall Univ Dept Comp Sci & Elect Engn Huntington WV USA

ISBN: (纸本)9798350382662;9798350382655

This paper addresses the path-planning challenge for very large-scale robotic systems (VLSR) operating in complex and cluttered environments. VLSR systems consist of numerous cooperative agents or robots working together autonomously. Traditionally, many approaches for VLSR systems are developed based on Gaussian mixture models (GMMs), where the GMMs represent agents' evolving spatial distribution, serving as a macroscopic view of the system's state. However, our recent research into VLSR systems has unveiled limitations in using GMMs to represent agent distributions, especially in cluttered environments. To overcome these limitations, we propose a novel model called the skew-normal mixture model (SNMM) for representing agent distributions. Additionally, we present a parameter learning algorithm designed to estimate the SNMM's parameters using sample data. Furthermore, we develop two SNMM-based path-planning algorithms to guide VLSR systems through complex and cluttered environments. Our simulation results demonstrate the effectiveness and superiority of these algorithms compared to GMM-based path-planning methods.

关键词： robot programming

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An Approach for Precise robotic Sanding Applications of Uneven Surfaces Based on Improved Point Cloud Registration Methods 20

An Approach for Precise Robotic Sanding Applications of Unev...

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IEEE 20th International Conference on Automation Science and Engineering (CASE)

作者： Xu, Xiaomei Schneider, Marco Bashir, Attique Mueller, Rainer Saarland Univ ZeMA Ctr Mechatron & Automat gGmbH Saarbrucken Germany

ISBN: (纸本)9798350358513;9798350358520

In a robotic sanding operation, three critical aspects require attention: precise identification of the sanding locations, accurate calculation of the necessary sanding amount, and strategic planning for the robot's sanding trajectory. We introduce a purely mathematical approach to achieve exceptionally precise detection of uneven surfaces under the millimeter level. Our approach entails capturing point cloud images from both sanded and unsanded objects. A best-fit method is employed to enhance the accuracy of Iterative Closest Point (ICP) method matching. The ICP method is then applied iteratively until a high level of accuracy is achieved. By implementing the Angle Criterion method the edge noise in the point cloud, which can arise due to misalignment, are filtered out. The point cloud data representing uneven surfaces is transformed into the robot's base coordinate system to facilitate the preparation of the robotic sanding path planning. The concept was validated on a ceramic wash basin using a 3D camera, a laser line sensor, and a sensitive robot with the ability to perform force-controlled applications like sanding.

关键词： robot programming

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CorNav: Autonomous Agent with Self-Corrected Planning for Zero-Shot Vision-and-Language Navigation 62

CorNav: Autonomous Agent with Self-Corrected Planning for Ze...

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62nd Annual Meeting of the Association-for-Computational-Linguistics (ACL) / Student Research Workshop (SRW)

作者： Liang, Xiwen Ma, Liang Guo, Shanshan Han, Jianhua Xu, Hang Mao, Shikui Liang, Xiaodan Sun Yat Sen Univ Shenzhen Campus Shenzhen Guangdong Peoples R China Northeastern Univ Shenyang Liaoning Peoples R China Huawei Noahs Ark Lab Montreal PQ Canada Dataa Robot Beijing Peoples R China Peng Cheng Lab Shenzhen Guangdong Peoples R China

ISBN: (纸本)9798891760998

Understanding and following natural language instructions while navigating through complex, real-world environments poses a significant challenge for general-purpose robots. These environments often include obstacles and pedestrians, making it essential for autonomous agents to possess the capability of self-corrected planning to adjust their actions based on feedback from the surroundings. However, the majority of existing vision-andlanguage navigation (VLN) methods primarily operate in less realistic simulator settings and do not incorporate environmental feedback into their decision-making processes. To address this gap, we introduce a novel zero-shot framework called CorNav, utilizing a large language model for decision-making and comprising two key components: 1) incorporating environmental feedback for refining future plans and adjusting its actions, and 2) multiple domain experts for parsing instructions, scene understanding, and refining predicted actions. In addition to the framework, we develop a 3D simulator that renders realistic scenarios using Unreal Engine 5. To evaluate the effectiveness and generalization of navigation agents in a zero-shot multi-task setting, we create a benchmark called NavBench. Our empirical study involves deploying 7 baselines across four tasks, i.e., goal-conditioned navigation given a specific object category, goal-conditioned navigation given simple instructions, finding abstract objects based on high-level instructions, and step-by-step instruction following. Extensive experiments demonstrate that CorNav consistently outperforms all baselines by a significant margin across all tasks.

关键词： robot programming

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Perceptual Factors for Environmental Modeling in robotic Active Perception

Perceptual Factors for Environmental Modeling in Robotic Act...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Morilla-Cabello, David Westheider, Jonas Popovic, Mari A. Montijano, Eduardo Univ Zaragoza Inst Invest Ingn Aragon Zaragoza Spain Univ Bonn Inst Geodesy & Geoinformat Bonn Germany

ISBN: (纸本)9798350384581;9798350384574

Accurately assessing the potential value of new sensor observations is a critical aspect of planning for active perception. This task is particularly challenging when reasoning about high-level scene understanding using measurements from vision-based neural networks. Due to appearance-based reasoning, the measurements are susceptible to several environmental effects such as the presence of occluders, variations in lighting conditions, and redundancy of information due to similarity in appearance between nearby viewpoints. To address this, we propose a new active perception framework incorporating an arbitrary number of perceptual effects in planning and fusion. Our method models the correlation with the environment by a set of general functions termed perceptual factors to construct a perceptual map, which quantifies the aggregated influence of the environment on candidate viewpoints. This information is seamlessly incorporated into the planning and fusion processes by adjusting the uncertainty associated with measurements to weigh their contributions. We evaluate our perceptual maps in a simulated environment that reproduces environmental conditions common in robotics applications. Our results show that, by accounting for environmental effects within our perceptual maps, we improve the state estimation by correctly selecting the viewpoints and considering the measurement noise correctly when affected by environmental factors. We furthermore deploy our approach on a ground robot to showcase its applicability for real-world active perception missions.

关键词： robot programming

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Distilling and Retrieving Generalizable Knowledge for robot Manipulation via Language Corrections

Distilling and Retrieving Generalizable Knowledge for Robot ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Zha, Lihan Cui, Yuchen Lin, Li-Heng Kwon, Minae Arenas, Montserrat Gonzalez Zeng, Andy Xia, Fei Sadigh, Dorsa Stanford Univ Comp Sci Dept Stanford CA 94305 USA Google Deepmind Mountain View CA 94043 USA

ISBN: (纸本)9798350384581;9798350384574

Today's robot policies exhibit subpar performance when faced with the challenge of generalizing to novel environments. Human corrective feedback is a crucial form of guidance to enable such generalization. However, adapting to and learning from online human corrections is a non-trivial endeavor: not only do robots need to remember human feedback over time to retrieve the right information in new settings and reduce the intervention rate, but also they would need to be able to respond to feedback that can be arbitrary corrections about high-level human preferences to low-level adjustments to skill parameters. In this work, we present Distillation and Retrieval of Online Corrections (DROC), a large language model (LLM)-based system that can respond to arbitrary forms of language feedback, distill generalizable knowledge from corrections, and retrieve relevant past experiences based on textual and visual similarity for improving performance in novel settings. DROC is able to respond to a sequence of online language corrections that address failures in both high-level task plans and low-level skill primitives. We demonstrate that DROC effectively distills the relevant information from the sequence of online corrections in a knowledge base and retrieves that knowledge in settings with new task or object instances. DROC outperforms other techniques that directly generate robot code via LLMs [1] by using only half of the total number of corrections needed in the first round and requires little to no corrections after two iterations. We show further results and videos on our project website: https://***/***/droc.

关键词： robot programming

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Fruit Picking robot Arm Training Solution Based on Reinforcement Learning in Digital Twin

Journal of ICT Standardization

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Journal of ICT Standardization 2023年第3期11卷 261-282页

作者： Tian, Xinyuan Pan, Bingqin Bai, Liping Wang, Guangbin Mo, Deyun Macau Institute of Systems Engineering Macau University of Science and Technology Taipa Macau999078 China Sichuan Digital Transportation Tech Co. Ltd. Sichuan Chengdu610095 China School of Mechanical and Electrical Engineering Lingnan Normal University Guangdong Zhanjiang524000 China

In the era of Industry 4.0, digital agriculture is developing very rapidly and has achieved considerable results. Nowadays, digital agriculture-based research is more focused on the use of robotic fruit picking technology, and the main research direction of such topics is algorithms for computer vision. However, when computer vision algorithms successfully locate the target object, it is still necessary to use robotic arm movement to reach the object at the physical level, but such path planning has received minimal attention. Based on this research deficiency, we propose to use Unity software as a digital twin platform to plan the robotic arm path and use ML-Agent plug-in as a reinforcement learning means to train the robotic arm path, to improve the accuracy of the robotic arm to reach the fruit, and happily the effect of this method is much improved than the traditional method. © 2023 River Publishers.

关键词： robot programming

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