Affiliation:
1. Aviation Engineering School, Air Force Engineering University, Xi’an 710038, China
2. Unit 93535 of PLA, Rikaze 857060, China
Abstract
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.
Funder
Science and Technology Innovation 2030 Key Project of “New Generation Artificial Intelligence”, China
Reference49 articles.
1. Amin, J.N., Boskovic, J.D., and Mehra, K. (2006, January 21–24). A Fast and Efficient Approach to Path Planning for Unmanned Vehicles. Proceedings of the AIAA Guidance, Navigation, and Control Conference, Keystone, Colorado.
2. Autonomous Driver Based on an Intelligent System of Decision-Making;Czubenko;Cogn. Comput.,2015
3. Peers’ Experience Learning for Developmental Robots;Wei;Int. J. Soc. Robot.,2019
4. Unmanned aerial vehicle perception system following visual cognition invariance mechanism;Zhang;IEEE Access,2019
5. Beard, R.W., and McLain, T.W. (2000, January 14–17). Trajectory Planning for Coordinated Rendezvous of Unmanned Air Vehicles. Proceedings of the AIAA Guidance, Navigation, and Control Conference and Exhibit, Denver, CO, USA.