The rapid increase in drone threats has created an urgent need for practical counter-drone systems for safety and defence purposes. Interceptor drones represent a promising and cost-effective solution for removing unwanted aerial vehicles. However, classical guidance laws such as Proportional Navigation (PN) are not well adapted to highly agile targets under realistic perception constraints. In parallel, deep reinforcement learning (DRL) has demonstrated exceptional performance in high-speed quadcopter control tasks, motivating its consideration for interception applications. A major challenge for such learning-based controllers lies in sim-to-real transfer. This work addresses this challenge through extensive domain randomization and explicit modelling of realistic perception constraints, including sensor noise, limited field of view, sensing ranges, and sensor update rates. A framework is proposed that trains Single Rotor Thrust (SRT) DRL policies for quadcopters and evaluates them against a classical PN implementation within a physics-based simulator. The resulting controllers generalise across platform variations and outperform classical guidance under modelled perception limitations, demonstrating improved robustness and transferability.

Github Repository of the code used for the thesis: https://github.com/maximecapelle/quadintercept_drl