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Mediterranean Conference on Control and Automation (MED)

作者： S. Domí nguez-Durante C. J. Pé rez-Del-Pulgar G. J. Paz-Delgado M. Azkarate Department of Systems Engineering and Automation Space Robotics Laboratory Universidad de Ma&#x00ED laga Andalucia Tech Ma&#x00ED laga Spain Robotics and Automation Section European Space Agency Noordwijk The Netherlands

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

Planetary exploration has been affected by the ability of rovers to avoid getting trapped on sandy terrains. Currently, the European Space Agency in collaboration with the Russian Federal Space Agency are preparing the ExoMars mission, with the objective of placing a rover on the Martian surface to seek for signs of life. This rover is capable of a novel locomotion mode, commonly called wheel walking. It consists on moving the wheel forward and backward in order to improve traction and escape from terrains with a high slip ratio. The main drawback of this locomotion mode is related to the efficiency in power consumption. Therefore, it should be used only in extreme conditions. The objective of this paper is twofold. First, the definition of an experimental setup, based on a rover prototype, an experimental flat terrain and a mechanism to induce a given slip to the rover. Second, the use of this experimental scenario to validate a method, previously proposed by authors, to estimate the rover slip during the wheel walking locomotion mode.

关键词： Legged locomotion Adaptation models Power demand Space missions European Space Agency Force Wheels

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LoRa technology application within the Internet of Flying Things concept for data collection from IoT devices

LoRa technology application within the Internet of Flying Th...

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Information, Control, and Communication Technologies (ICCT), International Conference on

作者： Alexander Dubelschikov Timur Gamberov Tatyana Tsoy Kuo-Hsien Hsia Evgeni Magid Tikhonov Moscow Institute of Electronics and Mathematics HSE University Moscow Russia Laboratory of Intelligent Robotics Systems (LIRS) Institute of Information Technology and Intelligent Systems Kazan Federal University Kazan Russia Graduate School Engineering Science and Technology National Yunlin University of Science & Technology Taiwan The Republic of China

ISBN: (数字)9798331517564

ISBN: (纸本)9798331517571

The Internet of Flying Things is one of the priority areas for robotics and automation research and applications. A promising case is an application of UAVs in a search and rescue scenario for data collection from pre-positioned IoT devices, which allows assessing and monitoring a current situation in an operation area. This paper presents a concept of using a low-power LoRa protocol to transmit data from smart sensors to UAVs that deliver the data to a dedicated cloud for further processing and analysis. A mock-up of a structure, which is described in the paper, is to be realized using microcontrollers, LoRa modules and smart temperature sensors.

关键词： Temperature sensors Protocols Microcontrollers LoRa Data collection Communications technology Internet of Things robotics and automation Monitoring Intelligent sensors

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Robust Dynamic Object Removal in 3D Point Clouds Based on Multi-View Projections

Robust Dynamic Object Removal in 3D Point Clouds Based on Mu...

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International Conference on Advanced robotics and Mechatronics (ICARM)

作者： Chen Yao Jingyi Liu Xing Yao Dingyuan Zhang Zheng Zhu Zhenzhong Jia Department of Mechanical and Energy Engineering Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Southern University of Science and Technology (SUSTech) Shenzhen China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities SUSTech Shenzhen China Shenzhen Zhicheng Software Technology Service corporation Futian Shenzhen Guangdong China Shenzhen ETop Information corporation Futian Shenzhen Guangdong China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

SLAM techniques are always used to effectively explore surroundings. To further enhance navigation capabilities, it is essential to filter out dynamic and noisy objects. In this paper, we present an online method that employs a multi-view image-parameterization-based representation, specifically a Bird’s Eye View-Polar View-Range View (BEV-PV-RV) fusion module, for efficient removal of dynamic objects in 3D point clouds. By employing different sizes of projected image windows and varying resolution levels, we can generate confidence levels that determine the disparity between the query scans and the imprecise noisy map derived from a collection of point clouds. Importantly, these points are identified at a granular level. Our iterative process combines the strengths of different projected views, compensating for motion distortions and comparison errors. To evaluate the effectiveness of our approach, we conducted extensive experiments on different sequences of the KITTI dataset. This allows us to successfully eliminate dynamic points, recover static points, and construct a subset that consists mainly of static points with the aid of proposed projected image views. Both quantitative and qualitative analyses demonstrate the robustness, reliability, and high performance of such proposed method.

关键词： Point cloud compression Laser radar Three-dimensional displays Simultaneous localization and mapping Navigation Statistical analysis Heuristic algorithms Robustness Sensors Noise measurement

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Multimodal Detection and Identification of Robot Manipulation Failures

arXiv

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arXiv 2023年

作者： Inceoglu, Arda Aksoy, Eren Erdal Sariel, Sanem Artificial Intelligence and Robotics Laboratory Faculty of Computer and Informatics Engineering Istanbul Technical University Maslak Turkey School of Information Technology Center for Applied Intelligent Systems Research Halmstad University Halmstad Sweden

An autonomous service robot should be able to interact with its environment safely and robustly without requiring human assistance. Unstructured environments are challenging for robots since the exact prediction of outcomes is not always possible. Even when the robot behaviors are well-designed, the unpredictable nature of physical robot-object interaction may prevent success in object manipulation. Therefore, execution of a manipulation action may result in an undesirable outcome involving accidents or damages to the objects or environment. Situation awareness becomes important in such cases to enable the robot to (i) maintain the integrity of both itself and the environment, (ii) recover from failed tasks in the short term, and (iii) learn to avoid failures in the long term. For this purpose, robot executions should be continuously monitored, and failures should be detected and classified appropriately. In this work, we focus on detecting and classifying both manipulation and post-manipulation phase failures using the same exteroception setup. We cover a diverse set of failure types for primary tabletop manipulation actions. In order to detect these failures, we propose FINO-Net [1], a deep multimodal sensor fusion based classifier network. Proposed network accurately detects and classifies failures from raw sensory data without any prior knowledge. In this work, we use our extended FAILURE dataset [1] with 99 new multimodal manipulation recordings and annotate them with their corresponding failure types. FINO-Net achieves 0.87 failure detection and 0.80 failure classification F1 scores. Experimental results show that proposed architecture is also appropriate for real-time use. Copyright © 2023, The Authors. All rights reserved.

关键词： Robots

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Roller-Quadrotor: A Novel Hybrid Terrestrial/Aerial Quadrotor with Unicycle-Driven and Rotor-Assisted Turning

Roller-Quadrotor: A Novel Hybrid Terrestrial/Aerial Quadroto...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Zhi Zheng Jin Wang Yuze Wu Qifeng Cai Huan Yu Ruibin Zhang Jie Tu Jun Meng Guodong Lu Fei Gao The State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou China Robotics Institute of Zhejiang University Hangzhou China Robotics Research Center of Yuyao City Ningbo China The State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China Huzhou Institute of Zhejiang University Huzhou China Center for Data Mining and Systems Biology College of Electrical Engineering Zhejiang University Hangzhou China

The Roller-Quadrotor is a novel quadrotor that combines the maneuverability of aerial drones with the endurance of ground vehicles. This work focuses on the design, modeling, and experimental validation of the Roller-Quadrotor. Flight capabilities are achieved through a quadrotor config-uration, with four thrust-providing actuators. Additionally, rolling motion is facilitated by a unicycle-driven and rotor-assisted turning structure. By utilizing terrestrial locomotion, the vehicle can overcome rolling and turning resistance, thereby conserving energy compared to its flight mode. This innovative approach not only tackles the inherent challenges of traditional rotorcraft but also enables the vehicle to roll through narrow gaps and overcome obstacles by taking advantage of its aerial mobility. We develop comprehensive models and controllers for the Roller-Quadrotor and validate their performance through experiments. The results demonstrate its seamless transition between aerial and terrestrial locomotion, as well as its ability to safely roll through gaps half the size of its diameter. Moreover, the terrestrial range of the vehicle is approximately 2.8 times greater, while the operating time is about 41.2 times longer compared to its aerial capabilities. These findings underscore the feasibility and effectiveness of the proposed structure and control mechanisms for efficient rolling through challenging terrains while conserving energy.

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Designing a digitalized cell for remanufacturing of automotive frames

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Procedia CIRP 2022年 109卷 513-519页

作者： Panos Stavropoulos Alexios Papacharalampopoulos Lydia Athanasopoulou Konstantinos Kampouris Panagiotis Lagios Laboratory for Manufacturing Systems and Automation (LMS) Department of Mechanical Engineering and Aeronautics University of Patras Rio Patras 26504 Greece Gizelis Robotics Schimatari Viotias 32009 Kormatzini Area Greece

Circular economy introduction in industry is globally recognized as one of the most effective green strategies towards sustainability and resources reduction. As the automotive sector has been historically one of the biggest energy consumers, significant efforts are being constantly made to reduce the environmental impact and to increase energy efficiency at the manufacturing stage, both at production and process level. This study focuses on proposing a framework for automotive defective parts identification and remanufacturing in the main production line. In particular, the concept of an automotive microfactory is taken into consideration. To achieve this, a vision-based monitoring system performing the defect identification drives the whole procedure that is considered to be realized through a remanufacturing cell. The software tool is considered to be utilized right before the scheduling process of orders and tasks performed in the remanufacturing cell, taking into account specific performance parameters. The architecture of the software is presented along with some discussion regarding the types of control that can be enforced at both processing and line level, while the use of synthetic data is regarded to test the efficiency of the monitoring system.

关键词： Remanufacturing Automotive Vision-based monitoring Machine Learning Circular Economy

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SubPipe: A Submarine Pipeline Inspection Dataset for Segmentation and Visual-inertial Localization

SubPipe: A Submarine Pipeline Inspection Dataset for Segment...

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OCEANS

作者： Olaya Álvarez-Tuñón Luiza Ribeiro Marnet Martin Aubard László Antal Maria Costa Yury Brodskiy Department of Electrical and Computer Engineering Artificial Intelligence in Robotics Laboratory (AiR-Lab) Aarhus University Aarhus C Denmark EIVA a/s Skanderborg Denmark OceanScan Marine Systems & Technology Matosinhos Portugal RWTH Aachen University Aachen Germany

ISBN: (数字)9798350362077

ISBN: (纸本)9798350362084

This paper presents SubPipe, an underwater dataset for SLAM, object detection, and image segmentation. SubPipe has been recorded using a lightweight autonomous underwater vehicle (LAUV), operated by OceanScan MST, and carrying a sensor suite including two cameras, a side-scan sonar, and an inertial navigation system, among other sensors. The AUV has been deployed in a pipeline inspection environment with a submarine pipe partially covered by sand. The AUV's pose ground truth is estimated from the navigation sensors. The side-scan sonar and RGB images include object detection and segmentation annotations, respectively. State-of-the-art segmentation, object detection, and SLAM methods are benchmarked on SubPipe to demonstrate the dataset's challenges and opportunities for leveraging computer vision algorithms. To the authors' knowledge, this is the first anno-tated underwater dataset providing a real pipeline inspection scenario. The dataset and experiments are publicly avail-able online at https://***/remaro-network/SubPipe-dataset.

关键词： Location awareness Image segmentation Simultaneous localization and mapping Oceans Pipelines Object detection Inertial navigation

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AXI-REALM: A Lightweight and Modular Interconnect Extension for Traffic Regulation and Monitoring of Heterogeneous Real-Time SoCs

AXI-REALM: A Lightweight and Modular Interconnect Extension ...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Thomas Benz Alessandro Ottaviano Robert Balas Angelo Garofalo Francesco Restuccia Alessandro Biondi Luca Benini Integrated Systems Laboratory ETH Zurich Switzerland Department of Electrical Electronic and Information Engineering University of Bologna Italy Department of Computer Science and Engineering UC San Diego San Diego CA USA Department of Excellence in Robotics & AI Scuola Superiore Sant'Anna Pisa Italy

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

The increasing demand for heterogeneous functionality in the automotive industry and the evolution of chip manufac-turing processes have led to the transition from federated to integrated critical real-time embedded systems (CRTESs). This leads to higher integration challenges of conventional timing predictability techniques due to access contention on shared resources, which can be resolved by providing system-level observability and controllability in hardware. We focus on the interconnect as a shared resource and propose AXI-REALM, a lightweight, modular, and technology - independent real-time extension to industry-standard AXI4 interconnects, available open-source. AXI-REALM uses a credit-based mechanism to distribute and control the bandwidth in a multi-subordinate system on periodic time windows, proactively prevents denial of service from malicious actors in the system, and tracks each manager's access and interference statistics for optimal budget and period selection. We provide detailed performance and implementation cost assessment in a 12nm node and an end-to-end functional case study implementing AXI-REALM into an open-source Linux-capable RISC-V SoC. In a system with a general-purpose core and a hardware accelerator's DMA engine causing interference on the interconnect, AXI-REALM achieves fair bandwidth distribution among managers, allowing the core to recover 68.2 % of its performance compared to the case without contention. Moreover, near-ideal performance (above 95 %) can be achieved by distributing the available bandwidth in favor of the core, improving the worst-case memory access latency from 264 to below eight cycles. Our approach minimizes buffering compared to other solutions and introduces only 2.45 % area overhead compared to the original SoC.

关键词： Bandwidth Interference Real-time systems Hardware Regulation Timing System-on-chip

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Combine Neural Radiance Fields in Different Reference Coordinates

Combine Neural Radiance Fields in Different Reference Coordi...

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Chinese Automation Congress (CAC)

作者： Weili Zhang Zhongheng Li Yu Guo Fei Wang Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications Beijing Institute of Spacecraft System Engineering Beijing China

Neural Radiance Fields (NeRF) leverage deep learning networks to restore high-quality implicit representations of 3D scenes from 2D images, surpassing traditional texture mapping in rendering quality. Typically, reconstructed images by NeRF exist in a reference coordinate system, necessitating consideration of each surface's information in a single shot. Overlapping parts in adjacent images are addressed by obtaining the camera pose through sparse COLMAP reconstruction. However, occluded regions, caused by contact with support surfaces, pose challenges for complete reconstruction. To address this, we propose a scene fusion algorithm based on NeRF, facilitating the amalgamation of neural radiance fields under different reference coordinate systems. This reduces data requirements and shooting conditions, enhancing the implicit 3D reconstruction of real objects. By estimating the translation and rotation relationship between image capture angles and the NeRF model, we fuse neural radiance fields in distinct reference coordinate systems, completing object surface information. Evaluation is conducted on both NeRF synthetic dataset and real objects.

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Comparison of Monocular ROS-Based Visual SLAM Methods 1

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7th International Conference on Interactive Collaborative robotics, ICR 2022

作者： Safarova, Liaisan Abbyasov, Bulat Tsoy, Tatyana Li, Hongbing Magid, Evgeni Laboratory of Intelligent Robotic Systems Intelligent Robotics Department Institute of Information Technology and Intelligent Systems Kazan Federal University Kremlyovskaya Str. 35 Kazan420111 Russia Department of Instrument Science and Engineering Shanghai Jiao Tong University Shanghai China Tikhonov Moscow Institute of Electronics and Mathematics HSE University Tallinn Str 34 Moscow123592 Russia

ISBN: (数字)9783031236099

ISBN: (纸本)9783031236082

Simultaneous Localization and Mapping (SLAM) is a robot navigation approach used to estimate a movement of a sensor in an unknown environment. SLAM application examples include urban search and rescue operations in high-risk environments, visual surveillance and service robotics. Compared to laser-range finders, visual sensors are more light weight, have less power consumption and provide more vast amount of environmental information. Visual SLAM methods are based on visual information only and employ monocular, stereo or RGD-D cameras as input sensors. Monocular SLAM methods are often preferred for a robotic platform when cost, energy and system weight requirements are limited. Calculation of errors and testing on ready-made datasets allow to check applicability of an algorithm in real-world scenarios with less efforts than experiments with a real robot. Verification of a sensor position and robot trajectory estimation accuracy is achieved by measuring Absolute Trajectory Error (ATE), Relative Pose Error (RPE) and Root Mean Square Error (RMSE). This article presents analysis and comparison of the most widely used Visual SLAM monocular algorithms built for Robot Operating System (ROS): ORB-SLAM2, ORB-SLAM3, DSO and LDSO. The KITTI and Euroc MAV datasets were selected to evaluate the algorithms’ performance. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Robot Operating System

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