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2023 IEEE International Conference on Electrical, Computer and Energy Technologies, ICECET 2023

作者： MacCarini, Marco Shahid, Asad Ali Piga, Dario Roveda, Loris Idsia USI-SUPSI Dti Lugano Switzerland

ISBN: (纸本)9798350327816

In modern industrial applications, the ability of robots to learn and adapt their motion is increasingly necessary. This is particularly true in tasks such as welding and material deposition, where the robot must plan and execute a path for a wide range of target scenarios. The current method of setting up these applications requires expert operators to manually generate a robot path for each part, which is a time-consuming process that can take at least one day to complete for each new part. In the case of deposition tasks, several activities must be carried out, including generating a deposition path in the CAD environment, converting the path into robot instructions, defining collisions-free robot motion, testing for quality assessment, and updating the path for desired deposition quality. To reduce setup time and standardize the task, there is a high demand for automating the path generation to achieve the desired quality. This paper focuses on automatic sealant deposition for the assembly of aerospace parts, which requires adapting the deposition path based on the target part geometry and material properties. The proposed Self-Modulated Adaptive Robotic Deposition (SMART DEPOSITION) tool offers a solution for generating and adapting robot motion in response to the deposition requirements of a target part. To accomplish this, the approach first analyzes the input part geometry to generate a grid of points. Then, an optimal deposition path is computed by exploiting the traveling salesman problem, using multiple approaches such as the nearest neighbor, a two-opt algorithm, and simulated annealing. The generated path can then be used to plan and execute the robot motion, resulting in the desired deposition quality with a standardized approach. The simulation results demonstrate the effectiveness of the proposed approach in optimizing the deposition path for different parts with varying geometries. © 2023 IEEE.

关键词： autonomous robotics Industry5.0 intelligent robotics path generation task optimization

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A name disambiguation module for intelligent robotic consultant in industrial internet of things

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2020年第0期136卷 106413-000页

作者： Ma, Xiao Wang, Ranran Zhang, Yin Jiang, Chi Abbas, Haider Zhongnan Univ Econ & Law Sch Informat & Safety Engn Wuhan Peoples R China Natl Univ Sci & Technol Islamabad Pakistan Univ Elect Sci & Technol China Chengdu Peoples R China

Nowadays, the industrial internet of things (IIoT) is becoming more and more popular and necessary in our daily life. intelligent robotic consultant which is a very important application of IIoT has attracted the attention of researchers. Robotic literature consultant which aims at helping users find related news or articles are becoming more and more important. As far as we know that the name disambiguation problem of robotic literature consultant still remain unsolved. networks (e.g., Utilizing simple topological structure in academic network (e.g., the relationships of co-authors) and feature engineering are the main method. However, it is generally difficult to generate the features considering the information's privacy and availability. In addition, the semantics underlying the real-world academic data cannot be easily captured by the simple relationship data. Therefore, in this paper, a graph embedding based name disambiguation module named Mech-RL is introduced for the robotic literature consultant and a novel meta-path channel based heterogeneous network representation learning method named Mech-RL is proposed. Firstly, given some meaningful meta-paths, the heterogeneous information network and meta-path channels are extracted. Then, by proposing two meta-path based proximity measures, Mech-RL obtains the node embeddings by sampling and jointly updating according to each meta-path channel. Finally, we apply the clustering algorithm on the generated paper embeddings to complete the disambiguation task. The real-world dataset based experimental results demonstrate our approach is effective compared to the related author name disambiguation approaches. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： intelligent robotics Name disambiguation Industrial internet of things Heterogeneous information networks Representation learning

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Real-time precision detection algorithm for jellyfish stings in neural computing, featuring adaptive deep learning enhanced by an advanced YOLOv4 framework

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FRONTIERS IN NEUROrobotics 2024年 18卷 1375886页

作者： Zhu, Chao Feng, Hua Xu, Liang Qinhuangdao First Hosp Emergency Dept Qinhuangdao Hebei Peoples R China

Introduction Sea jellyfish stings pose a threat to human health, and traditional detection methods face challenges in terms of accuracy and real-time capabilities. Methods To address this, we propose a novel algorithm that integrates YOLOv4 object detection, an attention mechanism, and PID control. We enhance YOLOv4 to improve the accuracy and real-time performance of detection. Additionally, we introduce an attention mechanism to automatically focus on critical areas of sea jellyfish stings, enhancing detection precision. Ultimately, utilizing the PID control algorithm, we achieve adaptive adjustments in the robot's movements and posture based on the detection results. Extensive experimental evaluations using a real sea jellyfish sting image dataset demonstrate significant improvements in accuracy and real-time performance using our proposed algorithm. Compared to traditional methods, our algorithm more accurately detects sea jellyfish stings and dynamically adjusts the robot's actions in real-time, maximizing protection for human health. Results and discussion The significance of this research lies in providing an efficient and accurate sea jellyfish sting detection algorithm for intelligent robot systems. The algorithm exhibits notable improvements in real-time capabilities and precision, aiding robot systems in better identifying and addressing sea jellyfish stings, thereby safeguarding human health. Moreover, the algorithm possesses a certain level of generality and can be applied to other applications in target detection and adaptive control, offering broad prospects for diverse applications.

关键词： YOLOv4 attention mechanism PID jellyfish stings intelligent robotics computer vision

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Modern controlling and guiding of combine harvesters

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STROJARSTVO 2004年第4-6期46卷 161-169页

作者： Brkic, D Sumanovac, L Juric, T Knezevic, D Univ JJ Strossmayer Osijek Fac Agr Osijek Dept Agr Engn Vinkovci HR-32100 Vinkovci Croatia

The beginning of the 70-ies in the last century was marked by the implementation of more complex informational systems on universal combine harvesters. Automatic machine guidance was implemented, as well in order to relieve the driver, especially in combine harvesters operating at high working speeds. There is a difference between controlling and measuring instruments and the instruments for the automatic regulation and guidance. Information technology and electronics in measuring instruments as well as the use of computers in combine harvesters make possible the follow: measuring of the harvested area, control over the grain loss, control over the grain yield in the whole harvested area as well as in some particular parts. Further they enable the carting of the area according to the yield, frequency control and the encumbrance of the important axes, also guiding a combine harvester to its most optimal permeable level, connecting to a satellite way of guidance, registering engine troubles in order to fix them on time and getting the right information about the timely servicing of combine harvesters. Except for the above mentioned features big capacity combine harvesters show some other possibilities such as: possibility to establish a connection with other computers in order to get the information needed, automatic guidance of a combine harvester maintaining the direction of moving possibility for the automatic regulation of the mass flow through the harvester as well as some other possibilities since such instruments have been developed and constantly built in new kinds of harvesters. Finally it is the system of controlling a combine harvester by a satellite positioning. Lately some researches have been performed considering the equipment in use GIS- for the automatic (robotized) controlling of a combine harvester. Such kind of combine harvesters have not yet been used in our domestic agriculture and some effort should be made to implement them in the exploitation.

关键词： automatic control combine harvester GPS intelligent robotics mapping positioning precision land management

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intelligent Fusion Imaging Photonics for Real-Time Lighting Obstructions

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SENSORS 2023年第1期23卷 323-323页

作者： Do, Hyeonsu Yoon, Colin Liu, Yunbo Zhao, Xintao Gregg, John Da, Ancheng Park, Younggeun Lee, Somin Eunice Univ Michigan Biomed Engn Appl Phys Biointerfaces Inst Dept Elect & Comp Engn Macromol Sci & Engn Ann Arbor MI 48109 USA Univ Michigan Dept Mech Engn Ann Arbor MI 48109 USA

Dynamic detection in challenging lighting environments is essential for advancing intelligent robots and autonomous vehicles. Traditional vision systems are prone to severe lighting conditions in which rapid increases or decreases in contrast or saturation obscures objects, resulting in a loss of visibility. By incorporating intelligent optimization of polarization into vision systems using the iNC (integrated nanoscopic correction), we introduce an intelligent real-time fusion algorithm to address challenging and changing lighting conditions. Through real-time iterative feedback, we rapidly select polarizations, which is difficult to achieve with traditional methods. Fusion images were also dynamically reconstructed using pixel-based weights calculated in the intelligent polarization selection process. We showed that fused images by intelligent polarization selection reduced the mean-square error by two orders of magnitude to uncover subtle features of occluded objects. Our intelligent real-time fusion algorithm also achieved two orders of magnitude increase in time performance without compromising image quality. We expect intelligent fusion imaging photonics to play increasingly vital roles in the fields of next generation intelligent robots and autonomous vehicles.

关键词： intelligent robotics intelligent autonomous vehicles intelligent drones

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ADAPTIVE FUZZY NEURAL NETWORK FOR INVERSE MODELING OF ROBOT MANIPULATORS

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IFAC Proceedings Volumes 2008年第2期41卷 5308-5313页

作者： D.T. Pham A.A. Fahmy E.E. Eldukhri Manufacturing Engineering Centre Cardiff University Cardiff CF24 3AA U.K.

This paper presents a new systematic adaptive fuzzy neural network for inverse modelling of robot manipulators. An inductive learning algorithm is applied to generate the required inverse modelling rules from the robot's input/output records. A full differentiable fuzzy neural network is developed to construct the inverse models of the robot manipulator, while any adaptation technique, such as back-propagation algorithm, can be applied to tune the network parameters online.

关键词： Fuzzy logic Neural networks Modelling Robots manipulators intelligent robotics Autotuning Nonlinear systems Adaptive system and control Knowledge discovery

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Learning Re-grabbing Policies based on Grabbed Garbage Weight Estimation using In-bucket Images for Waste Cranes

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IFAC-PapersOnLine 2023年第2期56卷 5021-5026页

作者： Hikaru Sasaki Go Watanabe Terushi Hirabayashi Kaoru Kawabata Takamitsu Matsubara Nara Institute of Science and Technology Nara Japan Hitachi Zosen Corporation Osaka Japan

The automation of waste cranes has been demanded to perform garbage incineration work with fewer workers efficiently. In particular, data-driven learning approaches are desirable for waste crane automation. When deciding whether to re-grab garbage by a waste crane to grab more garbage, human operators are efficiently making decisions based on visual information. However, the current automation system has to lift up the bucket to measure grabbed garbage weight to decide re-grab. The lifting motion after grabbing makes the crane motion inefficient. For this limitation, we propose a re-grabbing decision system with feedback from in-bucket camera images for the efficiency of waste crane automation by introducing the vision sensor in the bucket. To simplify the decision process of re-grabbing, we separate the re-grabbing decision system from the in-bucket image into grabbed garbage weight estimation and the re-grabbing decision policy based on estimated garbage weight. Moreover, the weight estimator model and the re-grabbing policy model are designed based on a Bayesian manner for data efficiency. The effectiveness is verified in a robotic waste crane system with an in-bucket camera. We confirmed the proposed method could learn re-grabbing decisions from the autonomously collected data. It achieved more efficient re-grabbing by waste cranes than the conventional automated system.

关键词： intelligent robotics Perception and sensing Industrial application Bayesian inference Waste crane

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A Radial Basis Functions approach to collision avoidance in collaborative tasks

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IFAC-PapersOnLine 2022年第2期55卷 307-312页

作者： G. Cipriani M. Bottin G. Rosati M. Faccio University of Padova Department of Industrial Engineering Padova Italy University of Padova Department of Management and Engineering Vicenza Italy

In the last years, collaborative human-robot applications have become more and more appealing thanks to the robot’s easiness of programming and the promise of increasing precision and safety. However, by combining two resources (the cobot and the human operator) there is a problem of safety since cobot and human operator have to work in the same workspace. To ensure human safety, the distance between robot and operator must be assessed and the robot must adapt accordingly either by reducing its velocity or by modifying its trajectory. In this paper, we propose a new online method to adapt the trajectory of the robot to the human movements using a single depth camera. This algorithm eliminates the robot from the scene using a simple calibration process. Then, it interpolates the shared workspace, captured by the depth camera, using Radial Basis Functions (RBFs). The result is a continuous function that is representative of the risk of collision with obstacles on the plane. Its gradient is used as a repulsive potential in the Artificial Potential Field (APF) method to generate the path. This method eliminates the need to calculate the distance between operator and robot since it is intrinsically considered in the potentials. Results shows the validity of the method.

关键词： intelligent robotics cobots collision avoidance human-robot collaboration safety

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Multi-modal communication system for mobile robot

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IFAC-PapersOnLine 2022年第4期55卷 133-138页

作者： Jan Švec Petr Neduchal Marek Hrúz Faculty of Applied Sciences New Technologies for the Information Society University of West Bohemia Univerzitní 8 Plzeň 306 14

This paper presents a cloud-based multi-modal human-robot interaction architecture. The architecture consists of parts running on the edge (mobile) platform and cluster services. Software on the mobile platform is represented primarily by a Robot Operating System, an MQTT broker and the client side of the SpeechCloud architecture, which provides the communication with the computing cluster. Its detailed description is a part of the paper. The main contribution of the paper is a design of an architecture based on standard frameworks, protocols, and validated JSON messages. The paper also focuses on the description of a tested mobile platform equipped with an NVIDIA Jetson family computer and information about tasks that the robot will be able to perform using the proposed architecture.

关键词： Multi-modal interaction Networked robotic systems Mobile robots Perception sensing intelligent robotics

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Stabilization of vertical motion of a vehicle on bumpy terrain using deep reinforcement learning*

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IFAC-PapersOnLine 2022年第37期55卷 276-281页

作者： Ameya Salvi John Coleman Jake Buzhardt Venkat Krovi Phanindra Tallapragada Department of Automotive Engineering Clemson University SC 29634 USA Department of Mechanical Engineering Clemson University SC 29634 USA

Stabilizing vertical dynamics for on-road and off-road vehicles is an important research area that has been looked at mostly from the point of view of ride comfort. The advent of autonomous vehicles now shifts the focus more towards developing stabilizing techniques from the point of view of onboard proprioceptive and exteroceptive sensors whose real-time measurements influence the performance of an autonomous vehicle. The current solutions to this problem of managing the vertical oscillations usually limit themselves to the realm of active suspension systems without much consideration to modulating the vehicle velocity, which plays an important role by the virtue of the fact that vertical and longitudinal dynamics of a ground vehicle are coupled. The task of stabilizing vertical oscillations for military ground vehicles becomes even more challenging due lack of structured environments, like city roads or highways, in offroad scenarios. Moreover, changes in structural parameters of the vehicle, such as mass (due to changes in vehicle loading), suspension stiffness and damping values can have significant effect on the controller's performance. This demands the need for developing deep learning based control policies, that can take into account an extremely large number of input features and approximate a near optimal control action. In this work, these problems are addressed by training a deep reinforcement learning agent to minimize the vertical acceleration of a scaled vehicle travelling over bumps by controlling its velocity.

关键词： Autonomous robotic systems intelligent robotics Reinforcement Learning Vehicle Dynamics Cyber-Physical Systems

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