Zhang Qirui, Wei Ruixuan, Huang Songlin
Aviation Engineering School, Air Force Engineering University, Xi'an 710038, China.
Unit 93535 of PLA, Rikaze 857060, China.
Sensors (Basel). 2024 Apr 27;24(9):2790. doi: 10.3390/s24092790.
A highly intelligent system often draws lessons from the unique abilities of humans. Current humanlike models, however, mainly focus on biological behavior, and the brain functions of humans are often overlooked. By drawing inspiration from brain science, this article shows how aspects of brain processing such as sensing, preprocessing, cognition, obstacle learning, behavior, strategy learning, pre-action, and action can be melded together in a coherent manner with cognitive control architecture. This work is based on the notion that the anti-collision response is activated in sequence, which starts from obstacle sensing to action. In the process of collision avoidance, cognition and learning modules continuously control the UAV's repertoire. Furthermore, simulated and experimental results show that the proposed architecture is effective and feasible.
一个高度智能的系统常常从人类的独特能力中汲取经验。然而，当前的类人模型主要关注生物行为，人类的大脑功能常常被忽视。通过从脑科学中获取灵感，本文展示了大脑处理的各个方面，如感知、预处理、认知、障碍学习、行为、策略学习、预动作和动作，如何能够以一种连贯的方式与认知控制架构融合在一起。这项工作基于这样一种观念，即防撞反应是按顺序激活的，从障碍物感知开始到动作。在避撞过程中，认知和学习模块不断控制无人机的全部技能。此外，仿真和实验结果表明，所提出的架构是有效且可行的。
