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检索条件"任意字段=5th International Conference on Informatics in Control, Automation and Robotics"

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Semantic Map Construction Method Based on Brain-Inspired SLAM

Semantic Map Construction Method Based on Brain-Inspired SLA...

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IEEE international conference on Cyber Technology in automation, control, and Intelligent Systems

作者： Hangpiao Zhao Fengzhen Tang The State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang China The University of Chinese Academy of Sciences Shenyang Institute of Automation Beijing China

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

Simultaneous localization and mapping (SLAM) is one of the core techniques for robots to move autonomously in unknown environments. Rat-SLAM, inspired by the navigation mechanism of the animal brain, provided nice performance in many environments. However, the experience map constructed by RatSLAM excluded semantic information, which is considered to play an important role in human environmental cognition and spatial navigation. In this paper, we propose a method to construct semantic experience map, combining the state-of-the-art deep neural network and RatSLAM. In our method, YOLOv5 is utilized to extract semantic information from the environmental images. the information of static objects, such as road signs, is selected to be incorporated into the experience map, being more closely to the brain navigation. To avoid the same object being repeatedly marked, we propose a semantic dynamic island mechanism, which only marks the center of the island in the experience map. the proposed method is verified on KITTI datasets, showing that it can successfully realize the construction of semantic experience map. the semantic experience maps are more intuitive and understandable by human, facilitating human-robot interaction, i. e. helping robots to fulfil more complex tasks for human.

关键词： YOLO Simultaneous localization and mapping Navigation Roads Semantics Human-robot interaction Detectors Data mining Intelligent systems Robots

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UAV control System with Time to Collision (TTC) Prediction Capability 11th

UAV Control System with Time to Collision (TTC) Prediction C...

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11th international conference on robotics, Vision, Signal Processing and Power Applications, RoViSP 2021

作者： Sabikan, Sulaiman Nawawi, Sophan Wahyudi Aziz, Nor Azlina Ab. Fakulti Teknologi Kejuruteraan Elektrik dan Elektronik Centre for Robotics and Industrial Automation Universiti Teknikal Malaysia Melaka Durian Tunggal Malaysia Faculty of Engineering School of Electrical Engineering Universiti Teknologi Malaysia Skudai Malaysia Faculty of Engineering and Technology Multimedia University Melaka Malaysia

ISBN: (纸本)9789811681288

this paper presents the development of an Unmanned Aerial Vehicle (UAV) control system simulation with collision avoidance prediction capability using the Time-to-Collision (TTC) model. TTC is the time required for a UAV either to collide with any static obstacle or completely stop without applying any braking control system when the throttle is fully released. Flight mission data collected from the quadcopter testbed platform experiments in the real environment in order to develop TTC model. the horizontal ground speed, throttle magnitudes, and flight time stamp are downloaded from the onboard quadcopter, filtered, analyzed, and optimize using Particles Swarm Optimization (PSO) algorithm to find the optimal TTC model. this model provides predictions of time before UAV will collide with the obstacle in the same path based on their current parameters, for instance, current speed and payload. this development of UAV’s control system implemented in Matlab/Simulik. the PID-based controller is utilized to stabilize the quadcopter and collision avoidance control systems with the TTC model to assist the system in order to avoid a collision from happening. Simulation tests performed proved the capability of UAV to stop at a safe distance and avoid collisions with the obstacles that existed based on TTC model prediction during flight successfully. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Collision avoidance

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Non-prehensile Planar Manipulation via Trajectory Optimization with Complementarity Constraints

Non-prehensile Planar Manipulation via Trajectory Optimizati...

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IEEE international conference on robotics and automation (ICRA)

作者： Joã o Moura theodoros Stouraitis Sethu Vijayakumar School of Informatics The University of Edinburgh Edinburgh U.K. The Alan Turing Institute London U.K.

Contact adaptation is an essential capability when manipulating objects. Two key contact modes of non-prehensile manipulation are sticking and sliding. this paper presents a Trajectory Optimization (TO) method formulated as a Mathematical Program with Complementarity Constraints (MPCC), which is able to switch between these two modes. We show that this formulation can be applicable to both planning and Model Predictive control (MPC) for planar manipulation tasks. We numerically compare: (i) our planner against a mixed integer alternative, showing that the MPCC planner converges faster, scales better with respect to the time horizon (th), and can handle environments with obstacles; (ii) our controller against a state-of-the-art mixed integer approach, showing that the MPCC controller achieves improved tracking and more consistent computation times. Additionally, we experimentally validate both our planner and controller with the KUKA LWR robot on a range of planar manipulation tasks. See our accompanying video here: https://***/EkU6YHMhjto.

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controlling the Rolling Position of a Spherical Robot Using Fuzzy-Based Technique with IoT Application

Controlling the Rolling Position of a Spherical Robot Using ...

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Artificial Intelligence, robotics and control (AIRC), international conference on

作者： Nurul Nafisah Kamis Salmiah Ahmad Abd Halim Embong Mechatronics Engineering Department International Islamic University Malaysia Selangor Malaysia College of Engineering and Technology University of Doha for Science and Technology Doha Qatar

ISBN: (数字)9798350385076

ISBN: (纸本)9798350385083

this paper presents the organized design and analysis in steps towards achieving the best fuzzy-based controller for Spherical Robot (SR) rolling mechanism and tested with monitoring applications using IOT. the SR was tested with different closed loop performance analysis using fuzzy-based controller where different types of Proportional-Integral-Derivatives (PID)-based Fuzzy Logic controller (FLC) were designed for the position controller of the rolling mechanism. the design was started with an uncompensated Proportional (P)-type, followed with the Proportional-Derivative (PD)-type, Proportional-Integrator (PI)-type, and Proportional-Integrator-Derivative (PID)-type of FLC. Although it is common to conclude PID-based is superior in many applications but this research has proven others, since this type of robot is known to be nonlinear in nature. All controllers were designed using triangular membership function and Gaussian membership function with Mamdani method applied for its Fuzzy Inference System (FIS). PD-type FLC with triangular MF has outperformed other types of PID-based FLC for rolling position control and therefore was chosen for the hardware implementation. the Particle Swarm Optimization (PSO) algorithm was developed to systematically tune the input and output gains of the PD-type FLC for further performance enhancement which later shown that rise time and settling time of the rolling position have significantly reduced by 81.4 % and 82.6 % respectively. the optimized controller has been integrated with the SR equipped with many sensors for surveillance and monitoring using IOT application.

关键词： Fuzzy logic Surveillance Position control System improvement Robot sensing systems Performance analysis Sensors

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the Sequence-to-Sequence Architecture with An Embedded Module for Long-Term Traffic Speed Forecasting with Missing Data 26

The Sequence-to-Sequence Architecture with An Embedded Modul...

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26th international conference on automation and Computing, ICAC 2021

作者： Zheng, Ge Chai, Wei Koong Katos, Vasilis Bournemouth University Department of Computing and Informatics DorsetBH12 5BB United Kingdom

ISBN: (纸本)9781860435577

Traffic forecasting plays a crucial role in Intelligent Transportation Systems (ITSs), which is proposed to provide traffic status in advance for road users to avoid traffic congestion or other traffic incidents and for authorities to optimise the strategies of traffic management. In this paper, we develop a novel deep learning framework, based on the Sequence-to-Sequence architecture with an embedded module, for long-term traffic speed forecasting with missing data and providing high forecasting accuracy. the embedded module uses Graph Convolution Neural Network for the local spatial dependency analysis by conducting convolutional operation on the k-hop neighbourhood matrix, while utilises Transformer for the global spatial dependency analysis by implementing the attention mechanism that assigns individual weights to neighbour detectors for contributing to the targeted detector. the sequence-to-sequence architecture is built to analyse temporal dependencies of the spatially-fused time series from the embedded module. To evaluate the proposed model against existing well-known ones, the real traffic speed dataset with missing data and frequent traffic incidents is used to train and test the models. the experimental results indicate that our proposed framework achieves the most accuracy forecasting, even obtaining more than 80% accuracy for forecasting two hours in advance. © 2021 Chinese automation and Computing Society in the UK-CACSUK.

关键词： Traffic congestion

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Adoption of Collaborative robotics in Industry 5.0. An Irish industry case study 20th

Adoption of Collaborative Robotics in Industry 5.0. An Irish...

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20th IFAC conference on Technology, Culture, and international Stability (TECIS)

作者： Doyle-Kent, Mary Kopacek, Peter Waterford Inst Technol Dept Engn Technol Waterford X91 KOEK Ireland TU Wien Inst Mech & Mechatron IHRT Favoritenstr 9-11-E325 A6 A-1040 Vienna Austria

It is widely accepted that every country needs to be forward thinking to remain competitive in manufacturing in the twenty first century. Some European countries have the advantage of a rich history in automation and robotics in their manufacturing industries. Looking forward, Industry 5.0 will be the next industrial revolution, and will emanate from Industry 4.0. Collaborative robotics will play a significant role by enabling humans and robots to work together in harmony. In 2021, as part of a doctoral thesis titled "Collaborative robotics in Industry 5.0" data were gathered for a case study in the south east of Ireland by the author of this paper. the results serve to provide an interesting benchmark for collaborative robotics in Irish industry. A brief summary of some of these results is shared here as well as to novel conceptual framework and definition of Industry 5.0. Copyright (C) 2021 the Authors.

关键词： Industry 4.0 Industry 5.0 Society 5.0 Collaborative robotics Manufacturing Irish Industry

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Hybrid Data Collection System for IoT Based Solar Powered Drip Irrigation system using LoRa WAN 5

Hybrid Data Collection System for IoT Based Solar Powered Dr...

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5th international conference on Advancement in Data Science, E-Learning and Information Systems, ICADEIS 2023

作者： Wedashwara, Wirarama Jaya, I Komang Damar Yadnya, Made Sutha Ahmadi, Candra Mulyana, Tatang University of Mataram Dept of Informatics Engineering Mataram Indonesia University of Mataram Dept of Agriculture Mataram Indonesia University of Mataram Dept of Electrical Engineering Mataram Indonesia National Taiwan University of Science and Technology Dept of Electrical Engineering Taipei Taiwan Telkom University Department of Information System Bandung Indonesia

ISBN: (纸本)9798350303414

Outdoor data communication using the internet in Internet of things (IoT) applications has a risk of interference caused by environmental conditions. Providing an access point for outdoor applications requires more electricity. the proposed research aimed to optimize data communication between local storage, internet and wireless sensor network (WSN). A hybrid data collection system for an IoT-based solar drip irrigation system using LoRa WAN. the Hybrid system in this study consists of a WAN for local control and data collection using LoRa and uploading to the internet. Apart from storing data in indoor and internet local storage, the system allows local users such as farmers to access a web-based user interface without internet. the WSN system in research uses nRF from previous studies. the blackbox testing shows valid results for nRF, LoRa and HTTP GET data communication series to local and online web server interfaces. the results of a data communication loop programming produces a total average time of 2469ms and a delay of 3000ms on outdoor devices. the indoor device uses 24272ms and a delay of 5000ms for one loop. the Quality of Service (QoS) evaluation showed that the lowest PDR archived by nRF with 97.285%. In other side the longest delay is archived by LoRa with 936ms. through solar power testing, the overall QoS decreases when the light intensity is below 101 lux, and the solar panel voltage is below 0.121v. But the decrease doesn't seem significant, especially in LoRa with the lowest value of 98.378% and 934ms delay. Meanwhile, the lowest delay was achieved by nRF with 247.6ms. the lowest PDR was archieved by HTTP GET with 96.7638%. Meanwhile, the longest delay was archieved by LoRa 876.6ms. © 2023 IEEE.

关键词： Quality of service

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Hybridizing Long Short-Term Memory Network and Inverse Kinematics for Human Manipulation Prediction in Smart Manufacturing

Hybridizing Long Short-Term Memory Network and Inverse Kinem...

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international conference on control, automation, robotics and Vision (ICARCV)

作者： Jiarong Yao Chongshan He Kaixu Li Rong Su Keck-Voon Ling School of Electrical and Electronic Engineering Nanyang Technological University Singapore Centre for Advanced Robotics Technology Innovation (CARTIN) School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

Human-Robot Collaboration (HRC) is essential for enhancing productivity and flexibility in smart manufacturing, which poses requirements on accurately predicting the future movements of human operators, especially the trajectories of their upper limbs. However, existing model-based studies on human manipulation prediction lacks consideration of stochasticity and variability while the emerging deep learning-based methods are demanding on data size, which yet makes real-time deployment challenging. therefore, combining the advantages of both model-based and deep learning-based methods, a method for predicting human arm motion, specifically, the position of a worker's wrist in less than 0.5 second, is proposed by hybridizing a Long Short-Term Memory (LSTM) network with an Inverse Kinematics (IK) model. Using historical coordinate sequences of the wrist joint in three-dimensional space in the past multiple frames as input, a neural network is trained to output the predicted coordinates of the wrist joint for the next frame. then IK (Inverse Kinematics) is used to calculate the arm's motion trajectory based on the predicted wrist coordinates. As the predicted wrist coordinates are sequentially used as the input for the next prediction cycle, the prediction is realized over a sliding time window. Evaluation was conducted using both proprietary and open datasets, results demonstrated that our LSTM-IK method achieved high prediction accuracy, with an average distance error of approximately 5 cm, and can adapt to various task scenarios and individual differences. Additionally, comparison with ground truth illustrated the model's ability to handle complex motion patterns, even with partial occlusions or rapid movements.

关键词： Wrist Learning systems Robot kinematics Kinematics Predictive models Manipulators Real-time systems Trajectory Long short term memory Smart manufacturing

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AR Intelligent Real-time Method for Cultural Heritage Object Recognition

AR Intelligent Real-time Method for Cultural Heritage Object...

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international conference on Advanced Information and Communication Technologies (AICT)

作者： Oleh Pisnyi Ivan Kit Khrystyna Lipianina-Honcharenko Jürgen Sieck Anatoliy Sachenko Maciej Dobrowolski Grygoriy Sapozhnyk Master's degree in Computer Science West Ukrainian National University Ternopil Ukraine Department for Information-Computing Systems and Control West Ukrainian National University Ternopil Ukraine Hochschule für Technik und Wirtschaft Berlin Berlin Germany Research Institute for Intelligent Computer Systems West Ukrainian National University Ternopil Ukraine Department of Informatics and Teleinformatics Kazimierz Pulaski University of Technology and Humanities in Radom Radom Poland

In a world of rapid technological changes and growing interest in cultural heritage, the development of innovative tools for travelers and history enthusiasts becomes incredibly important. Even in the digital age, interactive ways of exploring historical landmarks remain relevant and attractive. In this research, an AR Intelligent Real-time Method for Cultural Heritage Object Recognition has been developed. the method is implemented through a chatbot, which, when using a smartphone's camera, recognizes objects of cultural heritage and provides descriptions of these objects. the chatbot opens up new possibilities for travelers, allowing them to enjoy history and cultural heritage using intelligent tools and augmented reality.

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MARLMUI: Multi-Agent Reinforcement Learning Approach in Mobile Adaptive User Interface

MARLMUI: Multi-Agent Reinforcement Learning Approach in Mobi...

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international Youth conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Dmitry A. Vidmanov Alexander N. Alfimtsev Department of Informatics and Control Systems Bauman Moscow State Technical University Moscow Russia

the article is devoted to the study of machine learning approaches in the processing of the user interface of mobile ecosystems in order to test the approach of adaptive interfaces, which formalizes them as a stochastic sequential decision problem, as well as the use of multi-agent model-based reinforcement learning for adaptation planning. this article introduces for the first time the use of reinforcement learning in a mobile adaptive user interface. the article presents adaptation options based on changing the representation of elements, as well as a transition function for the model of Markov decision processes. this article proposes a novel method called MARLMUI, a Multi-Agent Reinforcement Learning Mobile User Interface. In conclusion, Dec-POMDP, a decentralized partially observable MDP model, is considered as a proposed interface processing algorithm based on multi-agent reinforcement learning. this study is first attempt to systematize knowledge and practically implement an adaptive interface in the mobile ecosystem.

关键词： Adaptation models Processor scheduling Computational modeling Ecosystems Reinforcement learning User interfaces Markov processes

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