Resumen
Unmanned autonomous vehicles for various civilian and military applications have become a particularly interesting research area. Despite their many potential applications, a related technological challenge is realizing realistic coordinated autonomous control and decision making in complex and multi-agent environments. Machine learning approaches have been largely employed in simplified simulations to acquire intelligent control systems in multi-agent settings. However, the complexity of the physical environment, unrealistic assumptions, and lack of abstract physical environments derail the process of transition from simulation to real systems. This work presents a modular framework for automated data acquisition, training, and the evaluation of multiple unmanned surface vehicles controllers that facilitate prior knowledge integration and human-guided learning in a closed-loop. To realize this, we first present a digital maritime environment of multiple unmanned surface vehicles that abstracts the real-world dynamics in our application domain. Then, a behavior-driven artificial immune-inspired fuzzy classifier systems approach that is capable of optimizing agents? behaviors and action selection in a multi-agent environment is presented. Evaluation scenarios of different combat missions are presented to demonstrate the performance of the system. Simulation results show that the resulting controllers can achieved an average wining rate between 52%" role="presentation" style="position: relative;">52%52%
52
%
and 98%" role="presentation" style="position: relative;">98%98%
98
%
in all test cases, indicating the effectiveness of the proposed approach and its feasibility in realizing adaptive controllers for efficient multiple unmanned systems? cooperative decision making. We believe that this system can facilitate the simulation, data acquisition, training, and evaluation of practical cooperative unmanned vehicles? controllers in a closed-loop.