In this paper, intelligent path planning and coordination control of Networked Unmanned Autonomous Systems (NUAS) in dynamic environments is presented. A biologically-inspired approach based on a computational model of emotional learning in mammalian limbic system is employed. The methodology, known as Brain Emotional Learning (BEL), is implemented in this application for the first time. The multi-objective properties and learning capabilities added by BEL to the path planning and coordination co-design of Networked Unmanned Autonomous Systems (NUAS) are very useful, especially while dealing with challenges caused by dynamic environments with moving obstacles. Furthermore, the proposed method is very promising for implementation in real-time applications due to its low computational complexity. Numerical results of the BEL-based path planner and intelligent controller for NUAS demonstrate the effectiveness of the proposed approach. The main contribution of this paper is to utilize the computational model of emotional learning in mammal’s brain, i.e., BEL, for developing a novel path planning and intelligent control method for practical real-time NUAS. To the best of the authors knowledge, this is the first time that BEL is implemented for accomplishing intelligent path planning and coordination control of NUAS. The learning capabilities added by the proposed approach to the path planning and coordination of MAS enhances the overall path planning strategy, which is very useful especially while dealing with challenges caused by dynamic and uncertain environments with unpredictable and unknown moving obstacles.

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Hao Xu, "Brain emotional learning-based intelligent path planning and coordination control of networked unmanned autonomous systems (Conference Presentation)," Proc. SPIE 10640, Unmanned Systems Technology XX, 106400I (Presented at SPIE Defense + Security: April 18, 2018; Published: 14 May 2018); https://doi.org/10.1117/12.2304715.5783298191001.