2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS)最新文献_第3页

Optimal autonomous quadrotor navigation in an obstructed space 障碍物空间中最优自主四旋翼导航

2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS) Pub Date : 2017-10-01 DOI: 10.1109/RED-UAS.2017.8101637

F. Giulietti, G. Pipeleers, Gianluca Rossetti, Ruben Van Parys

{"title":"Optimal autonomous quadrotor navigation in an obstructed space","authors":"F. Giulietti, G. Pipeleers, Gianluca Rossetti, Ruben Van Parys","doi":"10.1109/RED-UAS.2017.8101637","DOIUrl":"https://doi.org/10.1109/RED-UAS.2017.8101637","url":null,"abstract":"This paper presents an ambitious methodology of autonomous navigation for multirotor UAVs in obstructed environments. The strategy was formulated to provide the multirotor vehicles the capability to produce autonomously quasi-optimal and safe trajectories, although generally they have at their disposal limited computational resources on board. The problem is formulated in a model predictive control (MPC) architecture in which motion planning and trajectory tracking processes are solved separately as if they were stored in two different devices. The first process uses a spline-based motion planning approach to generate smooth and safe trajectories. At this step also a multirotor's simpified dynamic model and environment information are taken into account. The second process uses trajectory inputs, which are total thrust and attitude angle rates, to steer the multirotor during the flight. Both adequate time horizon and update frequency are chosen in order to account for disturbances and dynamics model mismatch. The methodology is validated by simulations and future work will include experimental tests in outdoor environment.","PeriodicalId":299104,"journal":{"name":"2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131521781","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

Optimal path of a UAV engaged in wind-influenced circular towing 无人机进行风影响圆形拖曳的最优路径

2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS) Pub Date : 2017-10-01 DOI: 10.1109/RED-UAS.2017.8101638

Mariann Merz, T. Johansen

引用次数: 2

Two imaging systems for positioning and navigation 两个定位和导航成像系统

2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS) Pub Date : 2017-10-01 DOI: 10.1109/RED-UAS.2017.8101654

C. Grönwall, J. Rydell, M. Tulldahl, E. Zhang, Fredrik Bissmarck, E. Bilock

引用次数: 4

Autonomous landing and go-around of airliners under severe weather conditions using Artificial Neural Networks 基于人工神经网络的飞机在恶劣天气条件下的自主着陆和复飞

2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS) Pub Date : 2017-10-01 DOI: 10.1109/RED-UAS.2017.8101661

Haitham Baomar, P. Bentley

{"title":"Autonomous landing and go-around of airliners under severe weather conditions using Artificial Neural Networks","authors":"Haitham Baomar, P. Bentley","doi":"10.1109/RED-UAS.2017.8101661","DOIUrl":"https://doi.org/10.1109/RED-UAS.2017.8101661","url":null,"abstract":"We introduce the Intelligent Autopilot System (IAS) which is capable of autonomous landing, and go-around of large jets such as airliners under severe weather conditions. The IAS is a potential solution to the current problem of Automatic Flight Control Systems of being unable to autonomously handle flight uncertainties such as severe weather conditions, autonomous complete flights, and go-around. A robust approach to control the aircraft's bearing using Artificial Neural Networks is proposed. An Artificial Neural Network predicts the appropriate bearing to be followed given the drift from the path line to be intercepted. In addition, the capabilities of the Flight Manager of the IAS are extended to detect unsafe landing attempts, and generate a go-around flight course. Experiments show that the IAS can handle such flight skills and tasks effectively, and can even land aircraft under severe weather conditions that are beyond the maximum demonstrated landing of the aircraft model used in this work as reported by the manufacturer's operations limitations. The proposed IAS is a novel approach towards achieving full control autonomy of large jets using ANN models that match the skills and abilities of experienced human pilots.","PeriodicalId":299104,"journal":{"name":"2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129136099","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: 12

Hand gesture recognition with convolutional neural networks for the multimodal UAV control 基于卷积神经网络的多模态无人机手势识别

2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS) Pub Date : 2017-10-01 DOI: 10.1109/RED-UAS.2017.8101666

Yuntao Ma, Yuxuan Liu, Ruiyang Jin, Xingyang Yuan, Raza Sekha, Samuel Wilson, R. Vaidyanathan

{"title":"Hand gesture recognition with convolutional neural networks for the multimodal UAV control","authors":"Yuntao Ma, Yuxuan Liu, Ruiyang Jin, Xingyang Yuan, Raza Sekha, Samuel Wilson, R. Vaidyanathan","doi":"10.1109/RED-UAS.2017.8101666","DOIUrl":"https://doi.org/10.1109/RED-UAS.2017.8101666","url":null,"abstract":"We introduce a robust wearable sensor suite fusing arm motion and hand gesture recognition for operator control of UAVs. The sensor suite fuses mechanomyography (MMG) and an inertial measurement unit (IMU) to capture multi-modal (arm movement and hand gesture) command signals simultaneously. The IMU produces world referenced orientation and acceleration data while concomitant MMG tracks muscle activation through surface vibration. The use of surface muscle vibration for gesture recognition removes the need for electrical contact with the skin, which has impeded other forms of muscle measurement for gesture recognition in the field. This investigation presents hardware design, inertial recognition of arm movement, and the detailed structure of a convolutional neural network (CNN) system used for real-time hand gesture recognition based on MMG signals. The system achieved 94% accuracy for five gestures with simple calibration for each user, thereby providing an intuitive gesture based UAV control system. To our knowledge this is the first wearable system enabling multimodal control of UAVs through intuitive gestures that does not require electrical skin contact. Future work involves testing the system with larger UAV swarms.","PeriodicalId":299104,"journal":{"name":"2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129779422","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: 36

A GLR algorithm for multiple consecutive measurement bias estimation 多连续测量偏差估计的GLR算法

2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS) Pub Date : 2017-10-01 DOI: 10.1109/RED-UAS.2017.8101675

G. Lundin, P. Mouyon, A. Manecy

引用次数: 3

Attitude control of a fixed-wing UAV using thrust vectoring system 推力矢量固定翼无人机姿态控制研究

2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS) Pub Date : 2017-10-01 DOI: 10.1109/RED-UAS.2017.8101677

Hirotaka Kikkawa, K. Uchiyama

引用次数: 2

X-Morf: A crash-separable quadrotor that morfs its X-geometry in flight X-Morf:一种可在飞行中改变其x几何形状的坠毁可分离四旋翼飞行器

2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS) Pub Date : 2017-10-01 DOI: 10.1109/RED-UAS.2017.8101670

A. Desbiez, F. Expert, M. Boyron, J. Diperi, S. Viollet, F. Ruffier

引用次数: 36

Real-time prediction of altered states in Drone pilots using physiological signals 利用生理信号实时预测无人机驾驶员的状态变化

2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS) Pub Date : 2017-10-01 DOI: 10.1109/RED-UAS.2017.8101674

Eduardo Zecua Corichi, J. M. Carranza, Carlos Alberto Reyes Garca, Luis Villaseor Pineda

{"title":"Real-time prediction of altered states in Drone pilots using physiological signals","authors":"Eduardo Zecua Corichi, J. M. Carranza, Carlos Alberto Reyes Garca, Luis Villaseor Pineda","doi":"10.1109/RED-UAS.2017.8101674","DOIUrl":"https://doi.org/10.1109/RED-UAS.2017.8101674","url":null,"abstract":"This work describes a novel framework for the automatic monitoring of drone pilots' emotional state in order to recognize situations that can not be controlled at the moment of piloting a drone. The latter is achieved by real-time analysis of heart, breathing, temperature and sweating signals. The first step in our approach was to create a database of the signals mentioned before by following a customized acquisition protocol. Filtered methods are used to eliminate signal disturbances as well as feature extraction methods to reduce wave information and to generate the patterns that will provide a digital signature of the signals. Subsequently, neuro-fuzzy methods are trained with the database to fit a model representing a precondition of the pilot signals under normal circumstances, this is, with the pilot is not under stress or any other concern. Thus, for the recognition stage, the pilot signals are continuously sent to the trained model in order to produce a prediction of what the signal should look like, and this output is compared against the current pilot signals, the aim is to detect alterations in the pilot signal induced by situation of alert. These alterations are recognised and then translated into a stop command sent to the drone in order to avoid a possible collision due to erroneous controlling from the side of the pilot who is under stress or state of alert and may not react in time.","PeriodicalId":299104,"journal":{"name":"2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128569371","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

Issues of safety and risk management for unmanned aircraft operations in urban airspace 城市空域无人驾驶飞机运行的安全和风险管理问题

2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS) Pub Date : 2017-10-01 DOI: 10.1109/RED-UAS.2017.8101671

Lakshmi Pathiyil, V. C. Yeo, K. Low

引用次数: 5