2021 7th International Conference on Automation, Robotics and Applications (ICARA)最新文献

Accurate Gridless Indoor Localization Based on Multiple Bluetooth Beacons and Machine Learning 基于多蓝牙信标和机器学习的精确无网格室内定位

2021 7th International Conference on Automation, Robotics and Applications (ICARA) Pub Date : 2021-02-04 DOI: 10.1109/ICARA51699.2021.9376476

Konstantinos Kotrotsios, T. Orphanoudakis

引用次数: 8

A Novel Approach to Computationally Lighter GNSS-Denied UAV Navigation Using Monocular Camera 基于单目摄像机的轻量化gnss拒绝无人机导航新方法

2021 7th International Conference on Automation, Robotics and Applications (ICARA) Pub Date : 2021-02-04 DOI: 10.1109/ICARA51699.2021.9376502

Joyraj Bhowmick, Anurag Singh, Harshit Gupta, R. Nallanthighal

引用次数: 2

Dynamic Cognitive-Social Particle Swarm Optimization 动态认知-社会粒子群优化

2021 7th International Conference on Automation, Robotics and Applications (ICARA) Pub Date : 2021-02-04 DOI: 10.1109/ICARA51699.2021.9376550

Khelil Kassoul, S. Belhaouari, N. Cheikhrouhou

引用次数: 2

A Deep Learning Short Commands Recognition for MCU in Robotics Applications 基于深度学习的单片机短指令识别在机器人中的应用

2021 7th International Conference on Automation, Robotics and Applications (ICARA) Pub Date : 2021-02-04 DOI: 10.1109/ICARA51699.2021.9376366

I. Guarneri, G. Messina, A. Bruna, Davide Giacalone

引用次数: 2

Towards a Highly Integrated 3D Printed Soft Continuum Manipulator 迈向高度集成的3D打印软连续机械臂

2021 7th International Conference on Automation, Robotics and Applications (ICARA) Pub Date : 2021-02-04 DOI: 10.1109/ICARA51699.2021.9376438

J. M. Salgueiro, J. Reis

引用次数: 1

Deep Reinforcement Learning Based Online Area Covering Autonomous Robot 基于深度强化学习的自主机器人在线区域

2021 7th International Conference on Automation, Robotics and Applications (ICARA) Pub Date : 2021-02-04 DOI: 10.1109/ICARA51699.2021.9376477

Olimpiya Saha, Guohua Ren, Javad Heydari, Viswanath Ganapathy, Mohak Shah

{"title":"Deep Reinforcement Learning Based Online Area Covering Autonomous Robot","authors":"Olimpiya Saha, Guohua Ren, Javad Heydari, Viswanath Ganapathy, Mohak Shah","doi":"10.1109/ICARA51699.2021.9376477","DOIUrl":"https://doi.org/10.1109/ICARA51699.2021.9376477","url":null,"abstract":"Autonomous area covering robots have been increasingly adopted in for diverse applications. In this paper, we investigate the effectiveness of deep reinforcement learning (RL) algorithms for online area coverage while minimizing the overlap. Through simulation experiments in grid based environments and in the Gazebo simulator, we show that Deep Q-Network (DQN) based algorithms efficiently cover unknown indoor environments. Furthermore, through empirical evaluations and theoretical analysis, we demonstrate that DQN with prioritized experience replay (DQN-PER) significantly minimizes the sample complexity while achieving reduced overlap when compared with other DQN variants. In addition, through simulations we demonstrate the performance advantage of DQN-PER over the state-of-the-art area coverage algorithms, BA* and BSA. Our experiments also indicate that a pre-trained RL agent can efficiently cover new unseen environments with minimal additional sample complexity. Finally, we propose a novel way of formulating the state representation to arrive at an area-agnostic RL agent for efficiently covering unknown environments.","PeriodicalId":183788,"journal":{"name":"2021 7th International Conference on Automation, Robotics and Applications (ICARA)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126246210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

An RRT-Based Path Planning Strategy in a Dynamic Environment 动态环境下基于rrt的路径规划策略

2021 7th International Conference on Automation, Robotics and Applications (ICARA) Pub Date : 2021-02-04 DOI: 10.1109/ICARA51699.2021.9376472

Yijing Li

引用次数: 6

ECascade-RCNN: Enhanced Cascade RCNN for Multi-scale Object Detection in UAV Images ECascade-RCNN:用于无人机图像多尺度目标检测的增强级联RCNN

2021 7th International Conference on Automation, Robotics and Applications (ICARA) Pub Date : 2021-02-04 DOI: 10.1109/ICARA51699.2021.9376456

Qizhang Lin, Yan Ding, Hong Xu, Wenxiang Lin, Jiaxin Li, Xiaoxiao Xie

引用次数: 8

Self-Repairing Line of Metamorphic Robots 变形机器人的自修复线

2021 7th International Conference on Automation, Robotics and Applications (ICARA) Pub Date : 2021-02-04 DOI: 10.1109/ICARA51699.2021.9376447

Nooshin Nokhanji, N. Santoro

{"title":"Self-Repairing Line of Metamorphic Robots","authors":"Nooshin Nokhanji, N. Santoro","doi":"10.1109/ICARA51699.2021.9376447","DOIUrl":"https://doi.org/10.1109/ICARA51699.2021.9376447","url":null,"abstract":"A Metamorphic Robots System is a modular self-reconfigurable robotic system composed of autonomous mobile modules in a 2D (or 3D) regular grid. The modules have limited computational capabilities, interact only with neighboring modules, and can move around adjacent modules from a cell to an empty neighboring cell under specific conditions. An important well-studied problem for these robotic systems is Motion Planning, also known as Shape Formationor Self-reconfiguration, requiring the modules to organize themselves into a pre-determined final configuration (i.e., shape); basic shapes such as the line (or chain) are especially important as they are utilized as a foundation for constructing more complicated shapes and are an initial measure for handling complicated tasks. A metamorphic robots system could offer a higher degree of reliability and robustness compared to fixed-architecture robots due to its capacity to self-repair: should some modules fail and no longer move, the shape could be reconstructed by the non-faulty modules. To do this correctly, efficiently, and without restricting the autonomy of the modules is a non-trivial task. In this paper, we study the Line Recoveryproblem, requiring the non-faulty modules to reconstruct the line without violation of connectivity requirements at any time during the recovery procedure. A thorough feasibility characterization of the problem, the necessary conditions for its solvability, and an algorithm that solves the problem, regardless of the number and distribution of faults, are provided.","PeriodicalId":183788,"journal":{"name":"2021 7th International Conference on Automation, Robotics and Applications (ICARA)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127806991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Hardware in the Loop Simulation of Aircraft Inspection by an Unmanned Aerial System 无人机系统飞机检测的硬件在环仿真

2021 7th International Conference on Automation, Robotics and Applications (ICARA) Pub Date : 2021-02-04 DOI: 10.1109/ICARA51699.2021.9376532

Daniel Dose, M. Tappe, M. Alpen, J. Horn

{"title":"Hardware in the Loop Simulation of Aircraft Inspection by an Unmanned Aerial System","authors":"Daniel Dose, M. Tappe, M. Alpen, J. Horn","doi":"10.1109/ICARA51699.2021.9376532","DOIUrl":"https://doi.org/10.1109/ICARA51699.2021.9376532","url":null,"abstract":"Inspection of commercial aircrafts, wind turbines, bridges and other infrastructure elements is done manually in many cases. Therefore, today's maintenance mostly is time-consuming and cost-intensive. The goal of the joint project AI inspection drone is the holistic system design of an unmanned aerial system (UAS) for the damage detection and assessment of airliners. The technical design should be based on current maintenance requirements and the overall system should be able to anticipate its own flight, evaluate the inspection data AI-based, and draw automatic conclusions. In this paper we describe the structure of the announced and partly already realized process chain. The focus is on the required interfaces between the individual components, the control engineering challenges to the UAS and the mapping of the entire process chain in a simulation environment, which also enables a hardware in the loop test of the different sensors and the carrier system itself. Based on this simulation which also includes the mapping of the technical and operational environment, different movement strategies with regard to energy requirements and flight time as well as an efficient sensor data fusion should be investigated. The results obtained are, as far as possible, validated by simulation and real experiments.","PeriodicalId":183788,"journal":{"name":"2021 7th International Conference on Automation, Robotics and Applications (ICARA)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114966252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1