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DRONEKIT PYTHON INTEGRATED WITH MISSION PLANNER SIMULATION
Unmanned Aerial Vehicles (UAVs) have gained significant importance in recent years due to their wide range of applications in areas such as surveillance, mapping, agriculture, disaster management, and logistics. Autonomous operation of UAVs requires reliable control mechanisms, efficient communication protocols, and safe testing environments. This project presents the design and implementation of an autonomous drone control system using DroneKit Python integrated with Mission Planner simulation. The proposed system utilizes DroneKit, a Python-based application programming interface, to implement autonomous flight logic including vehicle connection, arming, takeoff, waypoint navigation, telemetry monitoring, and safe landing. Communication between the control program and the simulated drone is achieved using the MAVLink protocol, which enables real-time bidirectional data exchange. The Software-In-The-Loop (SITL) simulation environment is employed to emulate the behavior of a real flight controller, while Mission Planner serves as the ground control station for mission planning, visualization, and telemetry analysis.The system was tested extensively in a simulation environment, and the results demonstrate successful autonomous mission execution with stable communication, accurate waypoint navigation, and reliable telemetry feedback. The use of simulation eliminates the risks and costs associated with physical drone testing, making the approach safe and suitable for academic and research applications. The project also provides a scalable framework that can be extended to realworld UAV deployment with minimal modifications. Overall, this project highlights the effectiveness of combining DroneKit Python and Mission Planner simulation for developing and validating autonomous UAV systems. The proposed solution offers a practical, costeffective, and industry-relevant platform for learning, experimentation, and future advancements in drone automation
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