检索结果-内蒙古大学图书馆

Corrigendum to "Hyperledger sawtooth based supplychain traceability system for counterfeit drugs" [Comput. Ind. Eng. 190 (2024) 110021] (Computers & Industrial Engineering (2024) 190, (S0360835224001426), (10.1016/***.2024.110021))

引用

Computers and Industrial Engineering 2025年 204卷

作者： Nawaz, Anum Wang, Liguan Irfan, Muhammad Westerlund, Tomi Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Turku Intelligent and Embedded Robotic Systems Lab Faculty of Technology University of Turku Finland

关键词：

来源：评论

学校读者我要写书评

暂无评论

Towards Closing the Sim-to-Real Gap in Collaborative Multi-Robot Deep Reinforcement Learning 5

Towards Closing the Sim-to-Real Gap in Collaborative Multi-R...

引用

5th International Conference on robotics and Automation Engineering, ICRAE 2020

作者： Zhao, Wenshuai Queralta, Jorge Pena Qingqing, Li Westerlund, Tomi University of Turku Turku Intelligent Embedded and Robotic Systems Lab Finland

ISBN: (纸本)9781728189819

Current research directions in deep reinforcement learning include bridging the simulation-reality gap, improving sample efficiency of experiences in distributed multi-agent reinforcement learning, together with the development of robust methods against adversarial agents in distributed learning, among many others. In this work, we are particularly interested in analyzing how multi-agent reinforcement learning can bridge the gap to reality in distributed multi-robot systems where the operation of the different robots is not necessarily homogeneous. These variations can happen due to sensing mismatches, inherent errors in terms of calibration of the mechanical joints, or simple differences in accuracy. While our results are simulation-based, we introduce the effect of sensing, calibration, and accuracy mismatches in distributed reinforcement learning with proximal policy optimization (PPO). We discuss on how both the different types of perturbances and how the number of agents experiencing those perturbances affect the collaborative learning effort. The simulations are carried out using a Kuka arm model in the Bullet physics engine. This is, to the best of our knowledge, the first work exploring the limitations of PPO in multi-robot systems when considering that different robots might be exposed to different environments where their sensors or actuators have induced errors. With the conclusions of this work, we set the initial point for future work on designing and developing methods to achieve robust reinforcement learning on the presence of real-world perturbances that might differ within a multi-robot system. © 2020 IEEE.

关键词： Multipurpose robots

来源：评论

学校读者我要写书评

暂无评论

Ubiquitous distributed deep reinforcement learning at the edge: Analyzing Byzantine agents in discrete action spaces 11

Ubiquitous distributed deep reinforcement learning at the ed...

引用

11th International Conference on Emerging Ubiquitous systems and Pervasive Networks, EUSPN 2020

作者： Zhao, Wenshuai Queralta, Jorge Peña Qingqing, Li Westerlund, Tomi Turku Intelligent Embedded and Robotic Systems Lab University of Turku Finland

The integration of edge computing in next-generation mobile networks is bringing low-latency and high-bandwidth ubiquitous connectivity to a myriad of cyber-physical systems. This will further boost the increasing intelligence that is being embedded at the edge in various types of autonomous systems, where collaborative machine learning has the potential to play a significant role. This paper discusses some of the challenges in multi-agent distributed deep reinforcement learning that can occur in the presence of byzantine or malfunctioning agents. As the simulation-to-reality gap gets bridged, the probability of malfunctions or errors must be taken into account. We show how wrong discrete actions can significantly affect the collaborative learning effort. In particular, we analyze the effect of having a fraction of agents that might perform the wrong action with a given probability. We study the ability of the system to converge towards a common working policy through the collaborative learning process based on the number of experiences from each of the agents to be aggregated for each policy update, together with the fraction of wrong actions from agents experiencing malfunctions. Our experiments are carried out in a simulation environment using the Atari testbed for the discrete action spaces, and advantage actor-critic (A2C) for the distributed multi-agent training. © 2020 The Authors. Published by Elsevier B.V.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Federated Learning in robotic and Autonomous systems 18

Federated Learning in Robotic and Autonomous Systems

引用

18th International Conference on Mobile systems and Pervasive Computing, MobiSPC 2021, The 16th International Conference on Future Networks and Communications, FNC 2021 and the 11th International Conference on Sustainable Energy Information Technology, SEIT 2021

作者： Xianjia, Yu Queralta, Jorge Peña Heikkonen, Jukka Westerlund, Tomi Turku Intelligent Embedded and Robotic Systems Lab University of Turku Turku Finland

Autonomous systems are becoming inherently ubiquitous with the advancements of computing and communication solutions enabling low-latency offloading and real-time collaboration of distributed devices. Decentralized technologies with blockchain and distributed ledger technologies (DLTs) are playing a key role. At the same time, advances in deep learning (DL) have significantly raised the degree of autonomy and level of intelligence of robotic and autonomous systems. While these technological revolutions were taking place, raising concerns in terms of data security and end-user privacy has become an inescapable research consideration. Federated learning (FL) is a promising solution to privacy-preserving DL at the edge, with an inherently distributed nature by learning on isolated data islands and communicating only model updates. However, FL by itself does not provide the levels of security and robustness required by today's standards in distributed autonomous systems. This survey covers applications of FL to autonomous robots, analyzes the role of DLT and FL for these systems, and introduces the key background concepts and considerations in current research. © 2021 Elsevier B.V.. All rights reserved.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Autocalibration of a mobile UWB localization system for Ad-Hoc multi-robot deployments in GNSS-denied environments 10

Autocalibration of a mobile UWB localization system for Ad-H...

引用

10th International Conference on Localization and GNSS, ICL-GNSS 2020

作者： Almansa, Carmen Martínez Shule, Wang Queralta, Jorge Peña Westerlund, Tomi Turku Intelligent Embedded and Robotic Systems University of Turku Finland

Ultra-wideband (UWB) wireless technology has seen an increased penetration in the robotics field as a robust localization method in recent years. UWB enables high accuracy distance estimation from time-of-flight measurements of wireless signals, even in non-line-of-sight measurements. UWB-based localization systems have been utilized in various types of GNSS-denied environments for ground or aerial autonomous robots. However, most of the existing solutions rely on a fixed and well-calibrated set of UWB nodes, or anchors, to estimate accurately the position of other mobile nodes, or tags, through multilateration. This limits the applicability of such systems for dynamic and ad-hoc deployments, such as post-disaster scenarios where the UWB anchors could be mounted on mobile robots to aid the navigation of UAVs or other robots. We introduce a collaborative algorithm for online autocalibration of anchor positions, enabling not only ad-hoc deployments but also movable anchors, based on Decawave's DWM1001 UWB module. Compared to the built-in autocalibration process from Decawave, we drastically reduce the amount of calibration time and increase the accuracy at the same time. We provide both experimental measurements and simulation results to demonstrate the usability of this algorithm. © 2020 for this paper by its authors.

关键词： Multipurpose robots

来源：评论

学校读者我要写书评

暂无评论

UWB-based localization for multi-UAV systems and collaborative heterogeneous multi-robot systems 17

UWB-based localization for multi-UAV systems and collaborati...

引用

17th International Conference on Mobile systems and Pervasive Computing, MobiSPC 2020

作者： Shule, Wang Almansa, Carmen Martínez Queralta, Jorge Peña Zou, Zhuo Westerlund, Tomi School of Information Science and Technology Fudan University Shanghai China Turku Intelligent Embedded and Robotic Systems Lab University of Turku Turku Finland

Ultra-wideband technology has emerged in recent years as a robust solution for localization in GNSS denied environments. In particular, its high accuracy when compared to other wireless localization solutions is enabling a wider range of collaborative and multi-robot application scenarios, being able to replace more complex and expensive motion-capture areas for use cases where accuracy in the order of tens of centimeters is sufficient. We present the first survey of UWB-based localization focused on multi-UAV systems and heterogeneous multi-robot systems. We have found that previous literature reviews do not consider in-depth the challenges in both aerial navigation and navigation with multiple robots, but also in terms of heterogeneous multi-robot systems. In particular, this is, to the best of our knowledge, the first survey to review recent advances in UWB-based (i) methods that enable ad-hoc and dynamic deployments;(ii) collaborative localization techniques;and (iii) cooperative sensing and cooperative maneuvers such as UAV docking on mobile platforms. Finally, we also review existing datasets and discuss the potential of this technology for both localization in GNSS-denied environments and collaboration in multi-robot systems. © 2020 The Authors.

关键词： Multipurpose robots

来源：评论

学校读者我要写书评

暂无评论

Ubiquitous Distributed Deep Reinforcement Learning at the Edge: Analyzing Byzantine Agents in Discrete Action Spaces

引用

Procedia Computer Science 2020年 177卷 324-329页

作者： Wenshuai Zhao Jorge Peña Queralta Li Qingqing Tomi Westerlund Turku Intelligent Embedded and Robotic Systems Lab University of Turku Finland

关键词： Reinforcement Learning Edge Computing Multi-Agent systems Collaborative Learning RL Deep RL Adversarial RL

来源：评论

学校读者我要写书评

暂无评论

Edge Based Skin Cancer Decision Support System Using Machine Learning Algorithms

Edge Based Skin Cancer Decision Support System Using Machine...

引用

2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Nawaz, Anum Irfan, Muhammad Sadiqa, Hafza Ayesha Westerlund, Tomi School of Information Science and Technology Fudan University Shanghai China University of Turku Turku Intelligent and Embedded Robotic Systems Lab Faculty of Technology Turku Finland

ISBN: (纸本)9798350304602

Skin lesions has grown rapidly in recent years. Detecting and treating skin lesions in its early stages can be extremely beneficial to its recovery. Using Digital image processing (DIP) to extract skin diseases from lesions emerged rapidly due to in accessibility of dermatologist and laboratory facilities in remote areas specifically in under developed countries. Out of many deep learning based feature extraction algorithms, convolutional neural networks (CNN) outperform along with different classifiers. In this study, we focused on edge based classifiers for robust skin lesions classification. To choose the best combination of classifier with existing CNN based feature extraction methods we used HAM-10000 dataset, supervised by ISIC. Different mobile based classifiers such as QDA, KNN and SVM, along with their different hybrid versions were tested in same conditions. Experimental results shows that the cubic kernel function used in the cubic SVM approach allows for more complex decision boundaries to be created for melanoma detection, as the characteristics of melanoma lesions can vary greatly. The cubic kernel function enables the SVM model to capture these complex patterns and make accurate predictions using less computational resources as compared to other mobile based classifiers. Cubic SVM shows the accuracy of 98% while using only 20 seconds of time. © 2023 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Federated Learning in robotic and Autonomous systems

引用

Procedia Computer Science 2021年 191卷 135-142页

作者： Yu Xianjia Jorge Peña Queralta Jukka Heikkonen Tomi Westerlund Turku Intelligent Embedded and Robotic Systems Lab University of Turku Turku Finland

Autonomous systems are becoming inherently ubiquitous with the advancements of computing and communication solutions enabling low-latency offloading and real-time collaboration of distributed devices. Decentralized technologies with blockchain and distributed ledger technologies (DLTs) are playing a key role. At the same time, advances in deep learning (DL) have significantly raised the degree of autonomy and level of intelligence of robotic and autonomous systems. While these technological revolutions were taking place, raising concerns in terms of data security and end-user privacy has become an inescapable research consideration. Federated learning (FL) is a promising solution to privacy-preserving DL at the edge, with an inherently distributed nature by learning on isolated data islands and communicating only model updates. However, FL by itself does not provide the levels of security and robustness required by today’s standards in distributed autonomous systems. This survey covers applications of FL to autonomous robots, analyzes the role of DLT and FL for these systems, and introduces the key background concepts and considerations in current research.

关键词： robotics Cloud robotics Fog robotics Federated Learning Federated Reinforcement Learning Federated Edge Learning Distributed Learning Distributed Ledger Technologies Edge AI

来源：评论

学校读者我要写书评

暂无评论

Towards Lifelong Federated Learning in Autonomous Mobile Robots with Continuous Sim-to-Real Transfer

引用

Procedia Computer Science 2022年 210卷 86-93页

作者： Xianjia Yu Jorge Peña Queralta Tomi Westerlund Turku Intelligent Embedded and Robotic Systems (TIERS) Lab University of Turku Finland

The role of deep learning (DL) in robotics has significantly deepened over the last decade. intelligent robotic systems today are highly connected systems that rely on DL for a variety of perception, control and other tasks. At the same time, autonomous robots are being increasingly deployed as part of fleets, with collaboration among robots becoming a more relevant factor. From the perspective of collaborative learning, federated learning (FL) enables continuous training of models in a distributed, privacy-preserving way. This paper focuses on vision-based obstacle avoidance for mobile robot navigation. On this basis, we explore the potential of FL for distributed systems of mobile robots enabling continuous learning via the engagement of robots in both simulated and real-world scenarios. We extend previous works by studying the performance of different image classifiers for FL, compared to centralized, cloud-based learning with a priori aggregated data. We also introduce an approach to continuous learning from mobile robots with extended sensor suites able to provide automatically labelled data while they are completing other tasks. We show that higher accuracies can be achieved by training the models in both simulation and reality, enabling continuous updates to deployed models.

关键词： Sim-to-real Federated learning Visual obstacle avoidance Robot navigation Continuous learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：