The invention relates to the technical field of unmanned aerial vehicles, in particular to a
reinforcement learning dynamic test
system for multi-aircraft cooperative take-off and landing of unmanned aerial vehicles. The
system is composed of an environment
perception and modeling unit, a multi-
machine collaborative decision-making unit, a high-fidelity dynamics
simulation unit, a distributed communication and synchronization unit, a
reinforcement learning strategy training unit, a real-time test and evaluation unit, a man-
machine interaction and
visualization unit and an
energy management and optimization unit. A data storage and playback unit; and an environment
simulation and disturbance generation unit. Three technologies of
LiDAR,
millimeter wave and vision are creatively fused, multi-
modal target detection is realized, the integrity rate exceeds 99%, the positioning precision is improved by 80%, errors are greatly reduced, a layered architecture design is adopted, global and local
layers are included, a
decision process is optimized, decision
delay is enabled to be less than 20 milliseconds, cooperative operation of multiple unmanned aerial vehicles is supported, and the method is suitable for large-scale popularization and application. In addition, a six-degree-of-freedom platform is combined to simulate a real motion state, and a pneumatic
database is introduced to provide accurate parameters, so that a
simulation result is closer to reality.