qiuyuchuliang

qiuyuchuliang's Stars

• kaixindelele/ChatPaper

  Use ChatGPT to summarize the arXiv papers. 全流程加速科研，利用chatgpt进行论文全文总结+专业翻译+润色+审稿+审稿回复

  Language:Python18.2k932171.9k

• LeiWang1999/FPGA

  帮助大家进行FPGA的入门，分享FPGA相关的优秀文章，优秀项目

  3.8k624655

• ucb-bar/chipyard

  An Agile RISC-V SoC Design Framework with in-order cores, out-of-order cores, accelerators, and more

  Language:Scala1.6k85650620

• yuanhao-cui/Must-Reading-on-ISAC

  Must Reading Papers, Research Library, Open-Source Code on Integrated Sensing and Communications (aka. Joint Radar and Communications, Joint Sensing and Communications, Dual-Functional Radar Communications)

  Language:MATLAB582253141

• young-how/DQN-based-UAV-3D_path_planer

  RLGF is a general training framework suitable for UAV deep reinforcement learning tasks. And integrates multiple mainstream deep reinforcement learning algorithms(SAC, DQN, DDQN, PPO, Dueling DQN, DDPG).

  Language:Python29421928

• sutdcv/UAV-Human

  [CVPR2021] UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicles

  Language:Python19262111

• AlirezaShamsoshoara/Fire-Detection-UAV-Aerial-Image-Classification-Segmentation-UnmannedAerialVehicle

  Aerial Imagery dataset for fire detection: classification and segmentation (Unmanned Aerial Vehicle (UAV))

  Language:Python16431248

• mlech26l/keras-ncp

  Language:Python1587123

• LenaShengzhen/AerialRobotics

  Simulate the path planning and trajectory planning of quadrotors/UAVs.

  Language:MATLAB14721124

• fdcl-gwu/uav_simulator

  Python - Gazebo Simulation Environment for a UAV with Geometric Control

  Language:Python1054729

• Logan-Shi/UAV-motion-control

  MATLAB implementation of UAV (unmanned aerial vehicle) control simulation, with RRT (rapidly exploring random tree) for path planning, B-Spline for trajectory generation and LP (linear programming) for trajectory optimization.

  Language:MATLAB100219

• bilibili30/UAV-task-allocation-PSO

  基于粒子群算法多无人机任务分配

  Language:Python982210

• BIT-MCS/DRL-EC3

  [JSAC 2018] Energy-Efficient UAV Control for Effective and Fair Communication Coverage: A Deep Reinforcement Learning Approach

  Language:Python811319

• KostadinovShalon/UAVDetectionTrackingBenchmark

  Language:Python7861317

• yjwong1999/Twin-TD3

  IEEE WCNC 2023: Deep Reinforcement Learning for Secrecy Energy-Efficient UAV Communication with Reconfigurable Intelligent Surfaces

  Language:Python743115

• JohannesAutenrieb/mission_planning

  Mission Planning & Task Allocation - Team A for UAV Swarm Project for BAE Systems UAV Swarm Challenge

  Language:Python684020

• AlirezaShamsoshoara/Reinforcement_Learning_Team_Q_learnig_MARL_Multi_Agent_UAV_Spectrum_task

  A solution for Dynamic Spectrum Management in Mission-Critical UAV Networks using Team Q learning as a Multi-Agent Reinforcement Learning Approach

  Language:Python612118

• avionicscode/Robust-Attitude-Controller-for-UAV-Using-Dynamic-Inversion-and-Extended-State-Observer-controller

  A robust feedback linearization controller is presented for attitude control of an unmanned aerial vehicle (UAV). The objective of this controller is to make the roll angle, pitch angle, and yaw angle track the given trajectories(commands) respectively. This design is developed using dynamic inversion and extended state observer (ESO). Firstly, dynamic inversion is used to linearize and decouple UAV attitude system into three single-input-single-output (SISO) systems, then three proportional-derivative (PD) controllers are designed for these linearized systems. Extended state observers are used to estimate and compensate unmodeled dynamics and extent disturbances. Simulation results show that the proposed controller is effective and robust.

  Language:MATLAB582016

• whalewang410/Energy-Efficient_UAV_Communication_With_Trajectory_Optimization

  Language:MATLAB50117

• michaelkapteyn/UAV-Digital-Twin

  ROS 2 packages that implement dynamic structural health monitoring for a UAV via structural digital twin, formulated using a probabilistic graphical model

  Language:Python491025

• brunapearson/mtrl-auto-uav

  Multi-Task Regression-based Learning for Autonomous Unmanned Aerial Vehicle Flight Control within Unstructured Outdoor Environments

  Language:Python473218

• sinamics/uavcast

  ✈️ uav companion software ✈️

  Language:JavaScript426525

• idawatibustan/uav_pathplanning

  Implementation of path planning and trajectory algorithm for Unmanned Aerial Vehicle

  Language:MATLAB373015

• jifazhang/UAV-optimization

  UAV trajectory optimization in wireless communication

  Language:MATLAB35

• ncsu-geoforall-lab/uav-lidar-analytics-course

  NCSU GIS/MEA 584: Mapping and Analysis Using UAS

  Language:HTML349810

• jediofgever/UAV

  this repository contains some useful matlab simulınk files for uav sensors simulation , kalman filter propogation and autopilot implementation. equations and all mathematical model considered here is referred to this http://uavbook.byu.edu/doku.php book.

  Language:MATLAB314111

• dmar-bonn/ipp-al

  Informative Path Planning for Active Learning in Aerial Semantic Mapping

  Language:Python293

• AKAGIwyf/UAV-Tracking

  In recent years, UAV began to appear in all aspects of production and life of human society, and has been widely used in aerial photography, monitoring, security, disaster relief and other fields. For example, UAV tracking can be used for urban security, automatic cruise to find suspects and assist in intelligent urban security management.However, the practical application of UAV in various early scenes was mostly based on human remote control or intervention, and the degree of automation was not high. The degree to which UAVs can be automated is one of the decisive factors in whether they can play a bigger role in the future. With the increasing demand of UAV automation, target tracking based on computer vision has become one of the current research hotspots. Some companies in China and abroad, such as DJI, have successfully equipped target tracking on UAVs, but these technologies only exist in papers and descriptions, and the specific implementation has not been sorted out and opened source. Therefore, we plan to try to complete this project by ourselves and open source it on Github. Traditional visual tracking has many advantages, such as strong autonomy, wide measurement range and access to a large amount of environmental information, it also has many disadvantages.It requires a powerful hardware system. In order to obtain accurate navigation information, it needs to be equipped with a high-resolution camera and a powerful processor. From image data acquisition to processing, huge data operations are involved, which undoubtedly increases the cost of UAV tracking. Moreover, the reliability of traditional visual navigation and tracking is poor, and it is difficult for UAV to work in complex lighting and obstacle scenes. Therefore, we plan to use deep learning for target tracking in this project. We can train our own model through deep learning algorithm (we have not decided what network structure to use), then move the trained model to the embedded development board for operation, fix it on the UAV, read the image through the camera and process the data, so that it can recognize the objects to be recognized and tracked. In this project, we will use NVIDIA Jetson TX2 development board, install ROS in Linux system, establish communication with pixhawk, and conduct UAV flight control through PID algorithm.

  Language:Python18304

• zhoulongyu/JCAS_UAV

  JCAS

  Language:Python14104

• Apurvasriram/UAV-assisted-Wireless-Powered-Communication-channel.

  Contains derivations, code, plots and inferences derived after performing performance analysis of an UAV assisted Wireless Powered Communication channel.

  9100