This thesis presents the design and implementation of a decentralized collision avoidance system for Unmanned Aerial Vehicles (UAVs). The approach relies solely on local information and is optimized for real-time execution. A priority-based Model Predictive Control (MPC) strategy is adopted, where UAVs are assigned priorities: lower-priority drones predict the trajectory of higher-priority ones and adapt accordingly, while higher-priority drones ignore others. This reduces communication overhead and computational cost. Drones navigate toward a target through velocity commands computed by an optimizer, which selects the best intermediate goal and velocity scaling factor by minimizing a cost function. The cost accounts for proximity to obstacles, distance to the final target, and deviation from nominal speed.