Autonomous Aerial Vehicles Projects for M.E, M.Tech, Masters, MS abroad, and PhD students. These Autonomous Aerial Vehicles ieee projects are implemented with future work and extension for final year project submission with research paper publishing. These research projects guide final year students to learn, practice, and complete their academic submissions successfully. Each project includes complete source code, project report, PPT, a tutorial, documentation, and a research paper.
Latest Autonomous Aerial Vehicles Projects
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Application of Artificial Potential Field Method in Three-Dimensional Path Planning for UAV Considering 5G Communication
This project improves how drones fly while staying connected to computers using 5G networks. It plans drone paths in three dimensions by considering areas with the best 5G signal. The method also helps drones avoid getting stuck in tricky spots. Tests show it increases signal strength along the path, even if the route becomes slightly longer. -
Distributed User Association and Computation Offloading in UAV-Assisted Mobile Edge Computing Systems
This project focuses on using drones to help mobile devices process data faster. It finds the best way for devices to send tasks to drones while using the least energy. The study designs algorithms that let drones and devices work together efficiently. Tests show the approach saves energy compared to traditional methods. -
A Multi-Layer Information Dissemination Model and Interference Optimization Strategy for Communication Networks in Disaster Areas
This project studies how information spreads in communication networks used during disasters. It creates a model to understand how messages travel between nodes and how network interference affects this process. The work also finds the best network setup to reduce interference and deployment cost. Simulations show the model accurately predicts information flow and helps improve disaster communication. -
Capacity Analysis of UAV-to-Ground Channels With Shadowing: Power Adaptation Schemes and Effective Capacity
This project studies how a drone can send data to a receiver on the ground more efficiently. It looks at different ways the drone can adjust its power to improve the communication channel. The researchers derived formulas to measure how much data can be sent under these strategies. They also checked their results using computer simulations. -
Cell-Free UAV Networks With Wireless Fronthaul: Analysis and Optimization
This project studies how drones can be used to improve wireless networks without relying on traditional cell towers. Users send data to drones, which then forward it to a central processing point using wireless links. The work looks at different ways to share these links and optimizes drone positions and power settings to get the best network performance. The main benefit comes from placing the drones in the right 3D locations. -
Deep Reinforcement Learning Assisted UAV Path Planning Relying on Cumulative Reward Mode and Region Segmentation
This project focuses on making drones navigate on their own without human control. It uses a type of artificial intelligence called deep reinforcement learning to plan safe and efficient paths. The method splits the area into regions and gives rewards based on distance and obstacles to guide learning. Tests show it makes drones learn faster and avoid getting stuck in poor paths. -
Dynamic Topology Organization and Maintenance Algorithms for Autonomous UAV Swarms
This project focuses on making groups of drones work together even when GPS or location data is missing. The team developed methods for drones to organize themselves, join, or split from a group automatically. Their approach keeps the drones connected and communicating in challenging places like forests or indoors. Tests show the system works reliably and quickly in realistic conditions. -
Enabling Flexible Arial Backhaul Links for Post Disasters A Design Using UAV Swarms and Distributed Charging Stations
This project focuses on using drones and charging stations to provide reliable data links to areas affected by disasters. The goal is to plan the positions and number of drones and stations to either reduce costs or improve service quality. The team developed smart methods to find near-optimal solutions that work almost as well as the best possible design. Simulations show that these methods are effective and practical for real-world use. -
Fairness Enhancement of UAV Systems With Hybrid Active-Passive RIS
This project studies wireless communication using drones to connect with multiple users. It uses smart surfaces that can actively or passively reflect signals to improve coverage. The goal is to make the connection fair for all users by optimizing the drone’s path, signal direction, and surface settings. The results show that even a few active elements on the surface can greatly improve communication speed compared to fully passive surfaces. -
LOS Analysis for Localization in High Frequency Systems
This project studies how terahertz wireless signals travel and how obstacles affect them. It focuses on keeping a clear line-of-sight between transmitters and receivers to improve connection quality and accurate location tracking. The research analyzes different setups of base stations and environments to see how signal reliability can be maximized. It shows that the number and height of base stations play an important role, especially in dense areas. -
Optimum UAV Trajectory Design for Data Harvesting From Distributed Nodes
This project focuses on planning efficient flight paths for drones that collect data from multiple ground locations. It finds the best places for the drone to stop and the order to visit each spot to save energy. The method works for different communication conditions and can also reduce flying time. A simpler version of the method gives almost the same results with much less computation. -
Sensing and Communication in UAV Cellular Networks Design and Optimization
This project studies how drones can collect and send data in cellular networks efficiently. It focuses on planning the drone’s path, controlling its power, and scheduling its sensing tasks. The goal is to gather all necessary data while reducing interference and saving energy. The proposed method significantly lowers the drone’s energy use compared to other approaches.
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At UniPhD, we provide complete guidance and support for Autonomous Aerial Vehicles ieee projects for MTech, ME, Master’s, and PhD students. Our team assists you at every stage from topic selection to coding, report writing, and result analysis.
We also help you choose a suitable IEEE base paper and guide you in developing your project using Python-based tools and frameworks such as TensorFlow, Keras, PyTorch, Scikit-learn, OpenCV, Flask, and Streamlit. In addition, we support implementation and simulation through platforms like MATLAB, Simulink, and NS2, depending on project requirements.
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Our M.E., M.Tech, Masters, MS abroad, and PhD theses are developed according to individual university guidelines and checked with plagiarism detection tools to ensure originality and quality.
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UniPhD provides complete support for research paper writing, editing, and proofreading to help you publish your work in reputed journals or conferences. We accept documents in Microsoft Word, RTF, or LaTeX formats and ensure your paper meets publication standards.
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Autonomous Aerial Vehicles Research Support for PhD Scholars
UniPhD offers advanced Autonomous Aerial Vehicles research projects designed specifically for PhD scholars. We provide end-to-end support for your research design, implementation, experimentation, and publication process.
Each project package includes comprehensive documentation, including the research proposal, complete source code, research guidance, documentation, research paper, and thesis writing support, helping you successfully complete your doctoral research and academic publications.