Game of drones: UAV pursuit-evasion game with type-2 fuzzy logic controllers tuned by reinforcement learning

Abstract

As being one of the most bankable flying objects, quadcopters have already proved their usefulness in both civilian and military applications. On the other hand, their control is still challenging as, unlike from ground robots, they do not have enough friction forces to stabilize their motion. Since they have under-actuated, highly nonlinear and coupled dynamics, and have to operate under noisy conditions, model-free control algorithms are more than welcome. In this paper, type-2 Takagi-Sugeno-Kang fuzzy logic controllers (TSK-FLCs) are tuned by reinforcement learning (RL), and implemented on quadcopters. The controllers are successfully tested on a variety of pursuit-evasion scenarios which provide a suitable basis for the utilization of RL since they consist of conflicting aims. A number of comparative results are presented for several case studies with different quadcopters, different initial points and under noisy conditions.