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检索条件"任意字段=18th International Conference on Control Automation Robotics and Vision"

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An Energy-Aware Decision-Making Scheme for Mobile Robots on a Graph Map Based on Deep Reinforcement Learning

An Energy-Aware Decision-Making Scheme for Mobile Robots on ...

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

作者： Gabriele Gemignani Margherita Bongiorni Lorenzo Pollini Dept. of Electrical and Information Engineering DAuSy National Programme for Polytechnic of Bari Bari Italy Department of Information Engineering University of Pisa Pisa Italy University of Pisa Automatic Control University of Pisa

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

Autonomous decision-making has always been one of the primary goals to pursue as concerns mobile robots. Researchers of this field have recently turned their attention to Deep Reinforcement Learning (DRL). this paper presents a Double Deep Q Network architecture for managing the high level decisions of a mobile robot involved in a site servicing task. We imagined a scenario where an autonomous service robot must react to alarms due to failures in its area of interest; the robot must have onboard the necessary servicing tool by resorting to a tool change station if needed, reach the area of the failure and fix it, while at the same time handling its battery status. One of the key properties, yet rarely examined, when it comes to robots' long-term independence is the energy-awareness, namely the ability of autonomously managing the charge state as a function of current and future needs. the proposed Deep Q Network training reward scheme is defined specifically to obtain an energy-aware high-level controller, by penalizing both extremely low levels of battery charge as well as unnecessary recharges. the model is numerically simulated on a graph scenario constituted of several failure and charging nodes. Results show that the trained agent always succeeds in reaching the destination without ever incurring in a complete discharge, as it promptly performs temporary stops at charging locations whenever needed.

关键词： Training Wireless communication Service robots Decision making Fault location Deep reinforcement learning Discharges (electric) Batteries Planning Mobile robots

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Deep vs. Deep Bayesian: Faster Reinforcement Learning on a Multi-robot Competitive Experiment 18

Deep vs. Deep Bayesian: Faster Reinforcement Learning on a M...

引用

18th international conference on Informatics in control, automation and robotics (ICINCO)

作者： Huang, Jingyi Giardina, Fabio Rosendo, Andre ShanghaiTech Univ Sch Informat Sci & Technol Shanghai Peoples R China Harvard John A Paulson Sch Engn & Appl Sci Cambridge MA USA

ISBN: (纸本)9789897585227

Deep Learning experiments commonly require hundreds of trials to properly train neural networks, often labeled as Big Data, while Bayesian learning leverages scarce data points to infer next iterations, also known as Micro Data. Deep Bayesian Learning combines the complexity from multi-layered neural networks to probabilistic inferences, and it allows a robot to learn good policies within few trials in the real world. In here we propose, for the first time, an application of Deep Bayesian Reinforcement Learning (RL) on a realworld multi-robot confrontation game, and compare the algorithm with a model-free Deep RL algorithm, Deep Q-Learning. Our experiments show that DBRL significantly outperforms DRL in learning efficiency and scalability. the results of this work point to the advantages of Deep Bayesian approaches in bypassing the Reality Gap and sim-to-real implementations, as the time taken for real-world learning can quickly outperform data-intensive Deep alternatives.

关键词： Reinforcement Learning Policy Search robotics

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A Machine vision Based In-Line Quality Assessment Method for the Fabrication of Structured Surfaces 28

A Machine Vision Based In-Line Quality Assessment Method for...

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28th international conference on automation and Computing, ICAC 2023

作者： Wang, Maomao Zhong, Wenbin Yu, Guoyu Jiang, Xiangqian Zeng, Wenhan Epsrc Future Metrology Hub Centre for Precision Technologies University of Huddersfield HuddersfieldHD1 3DH United Kingdom

ISBN: (纸本)9798350335859

the engineering of structured surfaces has gained significant attention due to their unique functionalities such as optical diffusion and scattering. However, the fabrication of these surfaces at the microscale with low cost and high accuracy remains challenging, primarily due to the complexity of the quality assessment processes. Traditional methods for assessing structured surfaces with surface topography measurement instruments are inefficient and costly and are not suitable for inline detection of surface defects. In light of these challenges, this paper presents a novel in-line quality assessment method that integrates the Hough transform and graph cut algorithms to enable fast on-machine quality assessment for structured surfaces. the proposed method enables the evaluation of key quality factors, such as the number, dimensions, and potential defects of the machined microstructures in seconds, without requiring the removal of the part from the machine. this method has been successfully integrated into an ultra-precision diamond machine. the effectiveness of the proposed method has been demonstrated through the in-line assessment of typical structured surfaces. the proposed method provides a potential solution to the fast and cost-effective in-line quality control of high-performance structured surfaces. © 2023 IEEE.

关键词： Fabrication

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Reward Design for Training Robotic Insertion Tasks Using Low-Dimensional Observation 24

Reward Design for Training Robotic Insertion Tasks Using Low...

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4th international conference on robotics and control Engineering, RobCE 2024

作者： Li, Xinyi Song, Aiguo Li, Huijun Wang, Xinke Yang, Ming School of Instrument Science and Engineering Southeast University Nanjing210096 China Automation Research Institute China South Industries Group Corporation Mianyang621000 China

ISBN: (纸本)9798400716782

this paper presents a reward design method for peg-in-hole insertion in uncertain environments using low-dimensional observation of robot end-effector forces. Recent reinforcement learning (RL) studies often avoid reward design by incorporating prior knowledge, which increases data requirements for RL training. Other approaches use sparse rewards to reduce human-engineering effort but fail to provide non-zero values frequently enough to avoid wandering traps. We propose a reward design method that offers improved performance by utilizing raw 3-dimensional force observations and action elements defined through Markov Decision Process (MDP), instead of the commonly used vision-based observations. the training framework employs a policy-based RL algorithm. We compare our proposed reward with various penalty and encouragement settings, trained with different scale parameters. Additionally, we compare our method with multi-modal observation approaches, demonstrating advantages in stability and reduction of wandering steps, achieving an average success rate of approximately 80% in uncertain position insertion tasks, trained using Proximal Policy Optimization (PPO). Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： End effectors

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Deep Learning Methods for Detecting and Classifying Brain Hemorrhages with Different Preprocessing Approach 12

Deep Learning Methods for Detecting and Classifying Brain He...

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12th RSI international conference on robotics and Mechatronics, ICRoM 2024

作者： Moazedi, Mohammadmahdi Feizi Nigjeh, Mohammadhossein Jalouli, Alireza Nikoofard, Amirhossein K.N Toosi University of Technology Department of Control Tehran Iran

ISBN: (纸本)9798331529734

Intracranial hemorrhage (ICH), resulting from trau-matic brain injuries, poses significant risks if not promptly diagnosed. the current standard involves manual CT scan examinations by radiologists, which can be time-consuming and prone to errors. To improve this, deep learning techniques are being explored for ICH detection. Recent AI advancements show promise in enhancing the accuracy and efficiency of diagnosing ICH and its subtypes. By utilizing large datasets and advanced algorithms, AI-based systems can aid in the timely identifi-cation and localization of hemorrhages, potentially improving patient outcomes through quicker interventions. In this work, we implemented multiple models such as ResNet18 (2D and 3D), ResNet50, VGG16 and the vision Transformer (ViT) architecture for classifying CT scans into Normal and Hemorrhage categories in a patient-wise manner. We utilized the MosMedData dataset, which includes 800 brain CT scans consisting of multiple series of DICOM images with and without signs of ICH. A significant challenge is that each patient has a different number of CT scan images, whereas our models require consistent depth across images. However, an advantage is that the CT scan images are sequentially ordered axially, allowing us to average pixel values and merge them into 3 and 10 channels. ResNet50 achieved the highest performance among our models using the 3-channel method, correctly identifying 278 out of 365 patients with brain hemorrhages and 270 out of 420 patients without hemorrhages. © 2024 IEEE.

关键词： Computerized tomography

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Agent-based Intelligent KPIs Optimization of Public Transit control System 18

Agent-based Intelligent KPIs Optimization of Public Transit ...

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18th international conference on Informatics in control, automation and robotics (ICINCO)

作者： Morri, Nabil Hadouaj, Sameh Ben Said, Lamjed Emirates Coll Technol IT Dept Abu Dhabi U Arab Emirates Higher Coll Technol Comp Informat Syst Dept Abu Dhabi U Arab Emirates Univ Tunis Inst Super Gest Tunis SMART Lab Tunis Tunisia

ISBN: (纸本)9789897585227

Public transit has a wide variety of resources. there is an infrastructure including stations and routes with multiple trips provided by different modes of transportation (metro, subway, bus). these resources must be well exploited to ensure good quality of service to passengers and especially against perturbations that may occur during the day. the contribution of this work is to model and implement a transit control system that detects perturbations and finds, through optimization, the best regulation action while respecting the constraints of the traffic situation. this system combines various measures of Key Performance Indicators (KPIs) into a single performance value, covering several dimensions depending on the type of service quality to be guaranteed. To take into account the complex and dynamic nature of transportation systems, a multi-agent approach is adopted in the modelling of our system. the validation is based on real traffic data. the results show better performance of our system compared to the current resolution.

关键词： Public Transit Intelligent control System Optimization Multi-agent System Key Performance Indicator

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Graph-Based Local Planning with Spatiotemporal Risk Assessment for Risk-Bounded and Prediction-Aware Autonomous Driving

Graph-Based Local Planning with Spatiotemporal Risk Assessme...

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

作者： Abdulrahman Ahmad Majid Khonji Ameena Al-Sumaiti Jorge Dias Khaled Elbassioni Department of Computer Science Khalifa University Abu Dhabi UAE Khalifa University Center for Autonomous Robotic Systems (KUCARS) Abu Dhabi UAE Department of Electrical Engineering Smart OR Lab Advanced Power Energy Center (APEC) Khalifa University Abu Dhabi UAE Department of Computer and Information Engineering Khalifa University Abu Dhabi UAE

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

Risk-bounded motion planning for autonomous driving in dynamic environments presents significant research challenges. Ensuring continuous navigation towards a destination while making real-time decisions is a nonconvex problem. this paper presents a graph-based local planning method constrained by user-specific driving preference, represented as a risk-bound criterion for motion planning. First, we propose a lattice graph construction method that adheres to the vehicle's curvature constraints. then, we formulate the trajectory planning problem as an integer-linear programming task, addressed by our novel risk-bounded and prediction-aware constrained shortest path. Our solution accounts for both static and dynamic obstacles in urban settings, adhering to traffic regulations. At the core of our approach is a conservative spatiotemporal risk assessment mechanism, which evaluates collisions considering the uncertain delay from speed control of the ego vehicle and predicted trajectories of dynamic obstacles. We implemented our solution using the CARLA simulator and the ROS2 platform, within a comprehensive framework encompassing global planning, local planning, and vehicle control. the effectiveness of our approach is demonstrated through notable collision avoidance, improved path-tracking, and enhanced risk-bounded planning capabilities.

关键词： Trajectory planning Dynamics Velocity control Planning Spatiotemporal phenomena Trajectory Risk management Vehicle dynamics Collision avoidance Autonomous vehicles

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Identification of Gait Phases with Neural Networks for Smooth Transparent control of a Lower Limb Exoskeleton 18

Identification of Gait Phases with Neural Networks for Smoot...

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18th international conference on Informatics in control, automation and robotics (ICINCO)

作者： Lippi, Vittorio Camardella, Cristian Filippeschi, Alessandro Porcini, Francesco Univ Hosp Freiburg Neurol Freiburg Germany Scuola Super Sant Anna PERCRO Lab TeCIP Inst Pisa Italy Scuola Super Sant Anna Dept Excellence Robot & AI Pisa Italy

ISBN: (纸本)9789897585227

Lower limbs exoskeletons provide assistance during standing, squatting, and walking. Gait dynamics, in particular, implies a change in the configuration of the device in terms of contact points, actuation, and system dynamics in general. In order to provide a comfortable experience and maximize performance, the exoskeleton should be controlled smoothly and in a transparent way, which means respectively, minimizing the interaction forces with the user and jerky behavior due to transitions between different configurations. A previous study showed that a smooth control of the exoskeleton can be achieved using a gait phase segmentation based on joint kinematics. Such a segmentation system can be implemented as linear regression and should be personalized for the user after a calibration procedure. In this work, a nonlinear segmentation function based on neural networks is implemented and compared with linear regression. An on-line implementation is then proposed and tested with a subject.

关键词： Wearable Robots Neural Networks Exoskeleton Gait Phases

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Critical Path Identification for Network Signal Coordination control Using Connected Vehicle Data Based on Analytic Hierarchy Process Method

Critical Path Identification for Network Signal Coordination...

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

作者： Jiarong Yao Chaopeng Tan Hao Wu Yumin Cao Keshuang Tang School of Electrical and Electronic Engineering Nanyang Technological University Singapore Department of Tranport and Planning Delft University of Technology CN Netherlands College of Transportation Engineering Tongji University Shanghai China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

Network signal coordination control is a crucial means to improve the traffic operation efficiency of the overall roadway network. Accurate identification of critical paths does play an important role in determining the scope of network coordination control. therefore, this paper proposed the definition of critical path from the perspective of traffic control and management. Under the detection environment of connected vehicle (CV), a comprehensive quantitative indicator system for path criticality evaluation from three aspects, supply side, demand side and operation side, which are arranged in the form of a tower structure. A critical path identification method (CPIM) was then proposed based on the analytic hierarchy process (AHP) theory, which was hereinafter referred to as AHP-CPIM. In order to evaluate the feasibility and effectiveness of the proposed method, a case study set in an urban network in Tongxiang, Zhejiang Province in China, is conducted through simulation models built through VISSIM and Synchro. Two scenarios were set, one is coordination control based on the coordination subarea obtained from Synchro (namely without critical path identification), and another one is coordination control with critical paths obtained from AHP-CPIM. Results showed that, compared with the control of Synchro and Multiband method under the scenario of coordination control without critical path identification, network signal coordination control optimization based on AHP-CPIM improved about 37.9% and 35.9% in average delay, respectively, justifying the effectiveness of CV-driven critical path identification for network signal coordination control.

关键词： Connected vehicles Soft sensors Robot kinematics Poles and towers Process control Analytic hierarchy process Traffic control Stability analysis Delays Optimization

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A Method of Cell Counting Based on Computer vision 12

A Method of Cell Counting Based on Computer Vision

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12th international conference on CYBER Technology in automation, control, and Intelligent Systems, CYBER 2022

作者： Ding, Shihong Zhao, Huanyu Han, Zhi Tang, Yandong Shenyang Ligong University CO Shenyang110158 China The First Affiliated Hospital of China Medical University CO Shenyang110002 China Shenyang Institute of Automation Chinese Academy of Sciences The State Key Laboratory of Robotics CO Shenyang110016 China Institutcs for Robotics and Intelligent Manufacturing Chinese Academy of Sciences CO Shenyang110168 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

For a long time, making machines have the ability of pattern recognition is the direction of scientists' continuous research. the study of pattern recognition is the fundamental way to understand the essence of artificial intelligence. At present, computer vision has been used to identify and count cells. In view of the problems existing in the field of cell counting, such as difficulties in identifying cells, dense cells, viscosity, difficult cell counting, inaccurate judgment and so on, this paper realizes the identification and counting through the technology of computer vision, determines the types of cells, and explores the characteristics of dense and sticky cells, At the same time, based on the characteristics of dense and sticky cells, a color based threshold segmentation algorithm is introduced to optimize the performance of cell classification. At the same time, the recognition and classification of different regions of adhesive cells are carried out, and the adhesive cells in different regions are recognized and judged by the technology of computer vision. through gray-scale transformation, threshold segmentation, morphological processing and LOG algorithm filtering of medical cell image, the cell region is extracted and counted. the cell counting is realized by calculating the connected domain. the experimental results preliminarily verify the effectiveness of this method, but it still needs to be improved. © 2022 IEEE.

关键词： Cells

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