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1st International Conference on Smart Mechatronics, ICSMech 2024

作者： Dragon, Klemens Raphael Lie, Zener Sukra Muharam, Aam Ika, Juan Luciano Perdana, Muhamad Arjuna Amin Hapid, Abdul Binus Aso School of Engineering Bina Nusantara University Automotive and Robotics Program Computer Engineering Department Jakarta Indonesia Badan Riset dan Inovasi Nasional Research Center for Transportation Technology Jawa Barat Bandung Indonesia

ISBN: (纸本)9798350368918

While the rise of electric vehicles reflects a push for clean energy as envisioned by international agreements like the Paris Agreement, wireless charging's convenience can overcome limitations and hasten their adoption, making it a strategic step alongside regulations such Permenhub No. 15/2022 and SDG 9 'Industry, Innovation, and Infrastructure' for cleaner air and sustainable transportation. The aim is to convert the high frequency AC voltage from the coupling capacitor into a stable DC voltage. Further increase the system sensitivity to the load variations, by designing a MOSFET based active rectifier. By changing load parameters, the accuracy of PFC adaptation to the battery State of Charge (SOC) obtained is at 98% with 2% tolerance for errors. It is concluded that the research is said to be successful because it has met the parameters of general research hypothesis where the system is able to convert high frequency AC voltage into a stable DC voltage. © 2024 IEEE.

关键词： State of charge

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ELiOT: End-to-end Lidar Odometry using Transformer Framework

arXiv

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arXiv 2023年

作者： Lee, Daegyu Nam, Hyunwoo Hyunchul Shim, D. Daejeon Korea Republic of Robotics Program KAIST Daejeon Korea Republic of

In recent years, deep-learning-based point cloud registration methods have shown significant promise. Furthermore, learning-based 3D detectors have demonstrated their effectiveness in encoding semantic information from LiDAR data. In this paper, we introduce ELiOT, an end-to-end LiDAR odometry framework built on a transformer architecture. Our proposed Self-attention flow embedding network implicitly represents the motion of sequential LiDAR scenes, bypassing the need for 3D-2D projections traditionally used in such tasks. The network pipeline, composed of a 3D transformer encoder-decoder, has shown effectiveness in predicting poses on urban datasets. In terms of translational and rotational errors, our proposed method yields encouraging results, with 7.59% and 2.67% respectively on the KITTI odometry dataset. Our end-to-end approach forgoes conventional geometric concepts and leverages positional embedding networks to identify motion flow and emphasize key features, achieving geometric motion in a seamless manner. © 2023, CC BY-NC-SA.

关键词： Optical radar

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Energy Minimization using Custom-Designed Magnetic-Spring Actuators

Energy Minimization using Custom-Designed Magnetic-Spring Ac...

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

作者： Yue Yang Fu Ali U. Kilic David J. Braun Department of Electrical and Computer Engineering SyBBURE Searle Undergraduate Research Program Vanderbilt University Advanced Robotics and Control Laboratory Advanced Robotics and Control Laboratory Vanderbilt University

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This study introduces an innovative actuator that resembles a motor with a non-uniform permanent magnetic field. We have developed a prototype of the actuator by combining a standard motor, characterized by a uniform magnetic field, with a custom rotary magnetic spring exhibiting a non-uniform magnetic field. We have also presented a systematic computational approach to customize the magnetic field to minimize the energy consumption of the actuator when used for a user-defined oscillatory task. Experiments demonstrate that this optimized actuator significantly lowers energy consumption in a typical oscillatory task, such as pick-and-place or oscillatory limb motion during locomotion, compared to conventional motors. Our findings imply that incorporating task-optimized non-uniform permanent magnetic fields into conventional motors and direct-drive actuators could enhance the energy efficiency of robotic systems.

关键词： Actuators Energy consumption Limbs Systematics Prototypes Motors Minimization Magnetic fields Springs Standards

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Swarm Intelligence Path-Planning Pipeline and Algorithms for UAVs: Simulation, Analysis and Recommendation 26

Swarm Intelligence Path-Planning Pipeline and Algorithms for...

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26th International Conference on Enterprise Information Systems, ICEIS 2024

作者： Harris, Wyatt Tseng, Sean Viso, Tabatha Weissman, Max Ngan, Chun-Kit Department of Robotics Engineering Worcester Polytechnic Institute 100 Institute Rd. WorcesterMA United States Data Science Program Worcester Polytechnic Institute 100 Institute Rd. WorcesterMA United States

ISBN: (纸本)9789897586927

This research work aims to support domain experts in the selection of proper path planning algorithms for UAVs to solve a domain business problem (i.e., the last mile delivery of goods). In-depth analysis, insight, and recommendations of three promising approaches, including reinforcement learning-based, bio-inspired-based, and physics-based are used to address the multi-agent UAV path planning problem. Specifically, the contributions of this work are fourfold: First, we develop a unified pipeline to implement each approach to conduct this analysis. Second, we build a 2D UAV path planning environment to simulate each approach. Third, using this 2D environment, we run the 450 simulations in three different group sizes of swarm UAV agents (i.e., 3, 5, and 10) within three environments of varying complexity (i.e., Easy, Intermediate, and Hard). We aggregate the simulation data and compare their performance in terms of success rate, run-time, and path length while using the classical A* Search as a baseline. Finally, based upon the performance of each approach and our analytical investigations, we provide informed recommendations for the optimal use case of each UAV path planning approach. The recommendations are presented using parameters for environmental complexity and urgency of goods delivery. Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Motion planning

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Mechatronics Design of Wheeled Mobile Robotic System for Gutter Maintenance 30

Mechatronics Design of Wheeled Mobile Robotic System for Gut...

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30th IEEE International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2023

作者： Arbulu, Mario Arias, Geyni Iniguez, Brayan Dussan, Sofia Barreto, Ricardo Cornejo, Jose CORHUILA Mechatronics Engineering Program Colombia CORHUILA Industrial Engineering Program Colombia Robotics 4.0 R and D Department Colombia Universidad Nacional de Ingeniería Center for Space Emerging Technologies Lima Peru Universidad Tecnológica Del Perú Lima Peru

ISBN: (纸本)9798350315578

This paper deals with the challenges involved on roof building gutter maintenance robot design, from the problem statement of secure teleoperated semiautonomous navigation on confined gutter spaces, and the best selection of right mechatronics brush to remove the waste on gutter surface;Two concept design have been tested: Passive Brush with Parallel locomotion (PBP), and Active Brush Axial (ABA). The results on our development are shown, as following: secure robot lifting regulation, simulation environment, HMI to teleoperate, mechatronics design. Finally, some tests on simulations and current prototypes are shown, to validate our proposal, and the conclusion that the ABA design allows to remove until 85% gutter waste, instead of PBP design;furthermore, the ABA allows independent brush rotation velocity, instead of PBP design. © 2023 IEEE.

关键词： Maintenance

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A Novel Adaptive Actor-Critic Reinforcement Learning Controller for Constrained Robots 25

A Novel Adaptive Actor-Critic Reinforcement Learning Control...

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2022 IEEE International Conference on Automation/25th Congress of the Chilean Association of Automatic Control, ICA-ACCA 2022

作者： Pantoja-Garcia, Luis Garcia-Rodriguez, Rodolfo Parra-Vega, Vicente Robotics and Advanced Manufacturing Department Coah Saltillo25900 Mexico Universidad Politecnica Metropolitana de Hidalgo Aeronautical Engineering Program and Postgraduate Program in Aerospacial Engineering Tolcayuca43860 Mexico

ISBN: (纸本)9781665494083

Force-position control for robot manipulators has become a basic control regime for advanced applications such as interaction or cooperative tasks that imply contact to environment or external object. Thus, for many applications environmental information is evaluated continuously to generate adequate control actions to complete the task. There has been proposed techniques such as adaptive or intelligent control to cover some of these requirements, however it seems insufficient given the uncertainty involved when dealing with the environment. The emergence of deep learning has recently renewed interest in these tasks from the perspective of reinforcement learning algorithms. In this paper, an actor-critic scheme is proposed for simultaneous control of contact force and joint positions while moving along an environmental rigid surface. The critic stage evaluates information of the environment through an extended error manifold, trying to minimize the cumulative future rewards by a neural network. The actor stage, which receives environment information from the critic stage, is used to approximate inverse robot dynamics. Together with two continuous orthogonal sliding PID controllers gives rise to simultaneous tracking of position and force, with stability analysis. Numerical simulations show the performance of the proposed approach under several conditions. © 2022 IEEE.

关键词： Reinforcement learning

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Cognitive TransFuser: Semantics-Guided Transformer-Based Sensor Fusion for Improved Waypoint Prediction 11th

Cognitive TransFuser: Semantics-Guided Transformer-Based Sen...

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11th International Conference on Robot Intelligence Technology and Applications, RiTA 2023

作者： Choi, Hwan-Soo Jeong, Jongoh Hoo Cho, Young Yoon, Kuk-Jin Kim, Jong-Hwan Robot Intelligence Technology Lab. School of Engineering KAIST Daejeon Korea Republic of dSPECTER Seoul Korea Republic of Visual Intelligence Lab. Robotics Program and Dept. of Mechanical Engineering KAIST Daejeon Korea Republic of

ISBN: (纸本)9783031706868

Sensor fusion approaches for intelligent self-driving agents remain key to driving scene understanding given visual global contexts acquired from input sensors. Specifically, for the local waypoint prediction task, single-modality networks are still limited by strong dependency on the sensitivity of the input sensor, and thus recent works therefore promote the use of multiple sensors in fusion in feature level in practice. While it is well known that multiple data modalities encourage mutual contextual exchange, it requires global 3D scene understanding in real-time with minimal computation upon deployment to practical driving scenarios, thereby placing greater significance on the training strategy given a limited number of practically usable sensors. In this light, we exploit carefully selected auxiliary tasks that are highly correlated with the target task of interest (e.g., traffic light recognition and semantic segmentation) by fusing auxiliary task features and also using auxiliary heads for waypoint prediction based on imitation learning. Our RGB-LIDAR-based multi-task feature fusion network, coined Cognitive TransFuser, augments and exceeds the baseline network by a significant margin for safer and more complete road navigation in the CARLA simulator. We validate the proposed network on the Town05 Short and Town05 Long Benchmark through extensive experiments, achieving up to 44.2 FPS real-time inference time. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Semantic Segmentation

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Development of Detection Kidney Function Stand Alone System and Design based on Oral Ammonia Levels 7

Development of Detection Kidney Function Stand Alone System ...

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7th International Conference of Computer and Informatics Engineering, IC2IE 2024

作者： William Hansen, Natasha Angeline Mariana, Yosica Sitepu, Elioenai Astuti, Winda Siswanto, Melinda Bina Nusantara University Binus Aso School of Engineering Automotive and Robotics Program Computer Engineering Department Jakarta11480 Indonesia Bina Nusantara University Binus Aso School of Engineering Product Design Program Industrial Engineering Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Industrial Engineering Department Jakarta11480 Indonesia Bina Nusantara University Biotechnology Engineering Department Jakarta11480 Indonesia

ISBN: (纸本)9798331505530

The development of detection kidney function system at the first prototype have problem with the dependence on the internet, which make not practical in the development of the system and the initial design was not ergonomic. In this work the development of the second step is done with the improvement of accuracy compared with other methods. In this work the new design of the embedded system is also applied and in term of algorithm, the system applied for woman patient. Three different types of machine learning techniques, namely K-nearest neighbors (KNN), Decision Tree and Random Forest are applied and compared. The AI-based kidney failure severity identification system with KNN algorithm had an average accuracy of 90% and 91% for training and testing accuracy, respectively. The design of the embedded system also renews with stylist, simple and practical. © 2024 IEEE.

关键词： Support vector machines

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Applying CNN Models with Stereo Camera for Object Detection and Distance Estimation in Automatic Micro Electric Vehicle (MEVi) 1

Applying CNN Models with Stereo Camera for Object Detection ...

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1st International Conference on Smart Mechatronics, ICSMech 2024

作者： Harimanto, Farrell Putra Hasanah, Nurul Pradana, Muhammad Khristamto Aditya Puji, Muhammad Nurul Binus Aso School of Engineering Bina Nusantara University Automotive & Robotics Program Computer Engineering Department Jakarta11480 Indonesia National Research and Innovation Agency Kst Samaun Samadikun Research Center for Smart Mechatronics Bandung40135 Indonesia

ISBN: (纸本)9798350368918

Technological advancements in the automotive industry are currently focused on autonomous driving systems or driver assistance systems. Depth estimation is also an important feature of the autonomous driving system as it can accurately estimate the distance to the detected objects. In current developments technologies such as stereo cameras are more popular for a more accurate depth estimation. The process for object detection consists of a combination of classification, localization, and segmentation. Deep convolutional neural networks are the basis of object detection. The methods for object detection are divided into two categories namely the Region Proposal-based method such as RCNN, and the Classification-based method which provides for methods such as YOLO and MobileNet. As a part of the research conducted by the National Research and Innovation Agency (BRIN) on Automatic Micro Electric Vehicles or MEVi, this paper analyzed and compared the performance of several CNN models for object detection as well as the performance of depth sensing using ZED stereo camera which was used for the autonomous system in MEVi. Among several models that had been compared, YOLO v8 model demonstrates high accuracy and moderate speed as indicated by its high mAP (0.50) and FPS (38.46). Based on depth sensing results, the accuracy of distance measurement also depended on brightness and environment with the average of accuracy achieved indoors was 98.2% and outdoors was 96.3%. © 2024 IEEE.

关键词： Automotive industry

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Computational Analysis of Mechanical Interactions between a Soft Robotic Device and a Skin-Muscle Phantom for Mechanotherapy 46

Computational Analysis of Mechanical Interactions between a ...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Ticllacuri, Victor Mio, Renato Biomedical Engineering Program PUCP-UPCH Lima Peru Pontificia Universidad Catolica del Peru Laboratory of Biomechanics and Applied Robotics Lima Peru Universidad Peruana Cayetano Heredia Laboratory of Biomedical Engineering Lima Peru University of Bayreuth Germany

ISBN: (纸本)9798350371499

Studying the soft robot-tissue mechanical interaction in muscle stimulation devices poses a significant challenge due to the complex behavior of the materials involved. To advance this field, this paper models computationally three types of soft elastomeric actuators designed to perform deep cyclic compression stimuli on human soft tissues for muscle rehabilitation by mechanotherapy. The analysis focuses on the interaction between a phantom representing transversely isotropic muscle and homogeneous skin, with a soft robotic device comprised of a hyperelastic actuator and a rigid support. Results from deformation, stress-strain and surface pressure analysis demonstrate efficient actuation, suggesting deep and focused stimulation on the muscle, while actuators exhibit reliable safety factors and load distribution, implying longer operational life. This lightweight and compact soft robotic device is suitable for integration into a wearable suit for targeted muscle groups stimulation in the lower limbs. Furthermore, this computational approach represents a significant advance in the biomechanical study of soft robot-human tissues interaction, with potential for generalization in similar biomedical device applications. Keywords - Soft robotics, Mechanotherapy, Transversely Isotropic Muscle, Human-Robot interaction. © 2024 IEEE.

关键词： Phantoms

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