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检索条件"机构=Faculty of Robot Science and Engineering"

共 527 条记录，以下是381-390 订阅

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Investigation of an Optimal Charging/Discharging Policy for Electric Vehicles Parking Station in a Smart Grid Environment

Investigation of an Optimal Charging/Discharging Policy for ...

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International Conference on Control, Mechatronics and Automation (ICCMA)

作者： Sarah B. Fahmy Silvana E. Guirguis Omar M. Shehata Elsayed I. Morgan Multi-Robot Systems (MRS) Research Group Cairo Egypt Faculty of Engineering and Materials Science German University in Cairo Egypt Faculty of Engineering Ain Shams University Cairo Egypt

ISBN: (数字)9781728192109

ISBN: (纸本)9781728192116

The world is shifting from conventional vehicles to Electric Vehicles (EVs) since their operation has a low cost and is environmentally friendly. However, there are various obstacles affecting EVs development, such as the vehicle range, charging process and infrastructure, etc. This paper focuses on the charging infrastructure of EVs investigating their optimal charging cycles in a Smart-Grid environment in order to improve the profitability of charging stations and decrease the high energy demand from the grid. Accordingly, a charging station was studied equipped with Photo-voltaic rooftop allowing vehicles to sell energy to the grid forming a Vehicle-to-Grid strategy. A priority-based algorithm was implemented to schedule EVs charging/discharging. Two optimization techniques; Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), were used to optimize the installation and operation of the station to maximize the station's profit. Accordingly, several cases were studied to analyze the influence of the mentioned capabilities. The results revealed that the maximum parking profit was obtained by utilizing the photo-voltaic rooftop with vehicle-to-Grid capability. Moreover, the performance of the two optimization techniques is compared showing similar results regarding the optimization effectiveness. However, GA takes longer computational time.

关键词： State of charge Vehicle-to-grid Optimization Batteries Particle swarm optimization Genetic algorithms Electric vehicle charging

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Decentralized Intersection Management of Autonomous Vehicles Using Nonlinear MPC

Decentralized Intersection Management of Autonomous Vehicles...

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Novel Intelligent and Leading Emerging sciences Conference (NILES)

作者： Hussein Ali Samaa Khaled Omar M. Shehata Elsayed I. Morgan Multi-Robot Systems (MRS) Research Group Cairo Egypt Faculty of Engineering and Materials Science German University in Cairo Egypt Faculty of Engineering Ain Shams University Cairo Egypt

ISBN: (数字)9781728182261

ISBN: (纸本)9781728182278

The rapid population growth and increase in vehicle numbers over the last few decades have caused traffic congestion worldwide, as intersections significantly impact the efficiency of traffic networks in urban areas, this paper focuses on intelligent traffic management at intersections by integrating the technologies of Intelligent Transportation Systems (ITS) and Autonomous Vehicles (AV). In this framework, a physical model represents each vehicle taking into account the dynamic limits. A decentralized Nonlinear Model Predictive Control (NMPC) is proposed to help coordinate the traffic flow of the AV at the intersection. The controller solves a quadratic cost function for the vehicle to ensure a smooth trajectory and minimum energy consumption, while avoiding collisions that is guaranteed using linear constraints and two developed priority assignment methods, Predicted Arrival Time (PAT) and First-Come First-Served (FCFS). The two methods are tested using a developed simulation environment on MATLAB/Simulink and the results are compared, the predicted arrival time method outperformed the FCFS method.

关键词： Vehicle dynamics Engines Mathematical model Force Aerodynamics Wheels Torque

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Adoption of Hybrid StateChart Principle in a Hierarchical Formation Architecture for Configurable Vehicles

Adoption of Hybrid StateChart Principle in a Hierarchical Fo...

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Novel Intelligent and Leading Emerging sciences Conference (NILES)

作者： Marawan Azmy Hebaish Mohamed A. Olwan Mostafa Ayman AbdELRahman A. Genidy William Habib Nouran Adel Hassan Omar Tarek Seada Elsayed I. Morgan Multi-Robot Systems (MRS) Research Group Cairo Egypt Faculty of Engineering and Materials Science German University in Cairo Egypt

ISBN: (数字)9781728182261

ISBN: (纸本)9781728182278

Multi-Vehicle Formation is an important step towards an efficient transportation system. In this paper, a 3-tier hybrid formation control architecture for connected vehicles is introduced. This architecture adopts the principle of a Hybrid StateChart in deliberative decision making layer for trajectories assignment, followed by an intermediate executional control layer for vehicles' trajectory tracking. Finally, a functional control layer for low-level feedback control. The architecture is simulated using a set of miniature vehicles for parallel straight-line vehicle formation trajectory separated with an offset in the lateral direction. Furthermore, an experimental point to point control test was conducted on a single miniature vehicle for verifying the intermediate control layer. Results from the performed simulated experiments illustrate the effectiveness and the high precision of the proposed control architecture in terms of the minimum error between the vehicle's longitudinal positions for straight-line formation trajectories tracking. While the experimental test illustrate the effectiveness of the used intermediate control layer.

关键词： Trajectory Mobile robots Cameras Actuators Protocols Matlab Hardware

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Behavior Evaluation of Vehicle Platoon via Different Fuzzy-X Tuned Controllers

Behavior Evaluation of Vehicle Platoon via Different Fuzzy-X...

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International Conference on Control, Mechatronics and Automation (ICCMA)

作者： Mohamed A. Olwan AbdELRahman A. Mostafa Youssef M. AbdELAty Dalia M. Mahfouz Omar M. Shehata Elsayed I. Morgan Multi-Robot Systems (MRS) Research Group Cairo Egypt Faculty of Engineering and Materials Science German University in Cairo Egypt Faculty of Engineering Ain Shams University Cairo Egypt

ISBN: (数字)9781728192109

ISBN: (纸本)9781728192116

Over the past few decades, Intelligent Transportation System (ITS) has become a topic of considerable interest for transportation development. One of the significant applications of ITS is vehicle platooning, which is described as a string of fully or partly automated vehicles traveling together at closely controlled inter-vehicular distances while preserving a set speed without violating safety restrictions. Control studies of a platoon of vehicles moving in a single dimension has recently attracted extensive research interest. The platoon control challenge relies on the adaptation of the predefined vehicle's speed while keeping a safe gap between each two successive vehicles. This study investigates intelligent unidirectional decentralized control approach based on Fuzzy Logic Control (FLC). The performance of the controller is tuned using three different techniques through the hybridization of FLC with two approaches; Genetic Algorithm (GA) and Proportional-Integral-Derivative (PID) and the adaptation of FLC with Neural Networks (NN) forming Fuzzy-X tuned controllers to control the follower vehicles to achieve their objective. In order to accomplish the goal of the study, each vehicle is represented through a longitudinal vehicle dynamic model. A reference velocity trajectory is built for the vehicles to follow. Simulations are conducted to evaluate the performance of each controller in terms of spacing error convergence and desired velocity tracking. Results show that the desired tracking performance and gap control are achieved by all the controllers with certain limitations that are discussed through out the study.

关键词： Torque Genetic algorithms Mathematical model Drag Vehicle dynamics Tuning Roads

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Intermittent Insertion Control Method with Fine Needle for Adapting Lung Deformation due to Breathing Motion

Intermittent Insertion Control Method with Fine Needle for A...

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2020 IEEE/RSJ International Conference on Intelligent robots and Systems (IROS)

作者： Ryosuke Tsumura Kaoru Kakima Hiroyasu Iwata Global Robot Academia Laboratory Waseda University Tokyo Japan School of Creative Science and Engineering Waseda University Tokyo Japan Faculty of Science and Engineering Waseda University Tokyo Japan

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

Fine needle insertions into a lung are challenging in terms of the needle deflection due to the breathing motion. Although previous related works neglected the effect for the needle deflection due to the breathing motion by patients stopping the breath during the insertion, they have to suffer from the discomfort. This paper proposes the intermittent insertion control method to decrease needle deflection adapting the lung deformation due to the breathing motion. The novelty of this method is to allow for accurate needle insertion without stopping the breath, which will contribute to decreasing the discomfort and the amount of radiation exposure for patients. The intermittent insertion is to move forward the fine needle during a certain time frame that the needle deflection barely occurs since the lung is not deformed by the diaphragm motion. The feasibility of the proposed method was validated through a polyvinyl chloride (PVC) phantom and ex vivo experiments. The results showed that the deflection can be suppressed up to 1.3 mm and 3.9 mm in the PVC phantom and ex vivo experiments, respectively.

关键词： Animals Lung Phantoms Needles Intelligent robots Strain

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Monocular adaptive inverse depth filtering algorithm based on Gaussian model

Monocular adaptive inverse depth filtering algorithm based o...

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第32届中国控制与决策会议

作者： Chenglong Xu Chengdong Wu Daokui Qu Haibo Sun Jilai Song Xiaofeng Wang Faculty of Robot Science and Engineering Northeastern University Shenyang SIASUN Robot & Automation Co. LTD. State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of Sciences

This paper presents an adaptive filtering algorithm for monocular depth *** is a geometric calculation method under the assumption that the inverse depth conforms to the Gaussian *** the inverse depth update,the average value of the historical stable points is introduced to smooth the output,and the depth uncertainty of the unit pixel offset is calculated to eliminate the estimation *** the similarity search along the epipolar line,the normalized cross-correlation and gradient are used as joint ***,the effectiveness of the algorithm is verified through experiments,and it can get a better trade-off between performance and time.

关键词： Monocular depth estimation Inverse depth filtering Gaussian model Normalized cross-correlation

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Neural Coding Strategies for Event-Based Vision Data

Neural Coding Strategies for Event-Based Vision Data

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Shane Harrigan Sonya Coleman Dermot Kerr Pratheepan Yogarajah Zheng Fang Chengdong Wu Intelligent Systems Research Centre Ulster University Northern Ireland United Kingdom Faculty of Robot Science and Engineering Northeastern University China

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

Neural coding schemes are powerful tools used within neuroscience. This paper introduces three different neural coding scheme formations for event-based vision data which are designed to emulate the neural behaviour exhibited by neurons under stimuli. Presented are phase-of-firing and two sparse neural coding schemes. It is determined that machine learning approaches, i.e. Convolutional Neural Network combined with a Stacked Autoencoder network, produce powerful descriptors of the patterns within events. These coding schemes are deployed in an existing action recognition template and evaluated using two popular event-based data sets.

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A Multi-Object Grasping Detection Based on the Improvement of YOLOv3 Algorithm

A Multi-Object Grasping Detection Based on the Improvement o...

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第32届中国控制与决策会议

作者： Kun Du Jilai Song Xiaofeng Wang Xiang Li Jie Lin Faculty of Robot Science and Engineering Northeastern University Shenyang SIASUN Robot & Automation Co. LTD. State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of Sciences

YOLOv3 has achieved good results in the field of object *** order to achieve multi-object grasping detection,the network structure has been *** improved YOLOv3 algorithm is applied to the object position and pose detection in robotic grasping,and a deep learning model is proposed to predict the robot’s grasping position,which can detect the occurrence of multiple objects in real time and grasp them in order according to the semantic *** the specific application scenario,the corresponding dataset is made,and a corner detection method based on YOLOv3 is proposed to grasping position and pose *** with the traditional corner detection method,this method has semantic information in its detected *** the scene,we first classify and locate the object,then detect the corner of the object,and filter the corner of the false detection through the positioning of the object,and design the corresponding algorithm to complete the corner of the missed detection,so that the accuracy of the corner detection is greatly improved,reaching 99% in the self-made ***,the position information of the corner is used to calculate the centroid position of the object,that is,the grasping point of the *** point cloud information is obtained by depth camera,and the grasping pose of the object is *** method can greatly improve the accuracy of grasping detection in specific scenes.

关键词： YOLOv3 robotic grasping Deep learning Corner detection Grasping position and pose detection

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3D Head Pointer: A manipulation method that enables the spatial localization for a wearable robot arm by head bobbing

3D Head Pointer: A manipulation method that enables the spat...

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IEEE International Symposium on Measurement and Control in robotics (ISMCR)

作者： Joy Oh Kozo Ando Shuhei Iizuka Lena Guinot Fumihiro Kato Hiroyasu Iwata Graduate School of Creative Science and Engineering Waseda University Tokyo Japan Waseda University Global Robot Academia Institute Tokyo Japan Faculty of Science and Engineering Waseda University Tokyo Japan

ISBN: (数字)9781665404792

ISBN: (纸本)9781665404808

This paper introduces a new type of interface “3D Head Pointer” for operation of a wearable robot arm in 3D space. The developed system is aimed at assisting the user in execution of daily chores (e.g. opening an umbrella, stirring a pot) while concurrently operating wearable robot arm. Previous researches have demonstrated the difficulty in simultaneously controlling a robot arm while uninterruptedly performing another task with one's own hands. The proposed method uses a combination of the orientation of the face and head bobbing (a forward and backward movement of the head) to manipulate spatial localization in a polar coordinate system. In a Virtual Reality (VR) environment, the proposed system and its efficiency at adapting its operating magnification (IDF) by relying on the incremental changes in head bobbing were evaluated through measurement of the instructional accuracy and time demand. Experiments were conducted with 12 participants. Results displayed an accuracy of 1.3 cm with a 4 seconds time span necessary to communicate an instruction. These results, along with the confirmed ability of an individual to multi-task while operating, suggested the effectiveness of the operation method and IDF.

关键词： Xenon 6G mobile communication

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Research on Person Recognition Model of Domain Adaptive Learning

Research on Person Recognition Model of Domain Adaptive Lear...

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Chinese Control and Decision Conference, CCDC

作者： Ming-hai Jiao Wei-ming Duan Jue Wang Ben-dong Luo Chi Zhang Xiang-yu Sun School of Computer Science and Engineering Northeastern University Shenyang China Faculty of Robot Science and Engineering Northeastern University Shenyang China College of Science Northeastern University Shenyang China School of Mechanical Engineering and Automation Northeastern University Shenyang China

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

The person recognition problem in different domain is a hard challenge on lower accuracy of object detection with massive image data, that is the gap between person target and background is small and hard to distinguish different posture of person target. The novel domain adaptive person detection model based on transfer data training is proposed for detecting data on new target domain. And domain adaptive loss function is designed to control the difference between the two models with self training, so as to avoid catastrophic forgetting in the source domain. And then the experiment results show that the improved deep self training domain adaptive learning model and loss function algorithm are effective and efficient for person recognition problem.

关键词： Adaptation models Training Data models Object detection Feature extraction Detectors Adaptive systems

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