Development of a Centralized Conflict Free Greedy Assignment Learning Spiking Neural Network for Solving a Perimeter Defense Problem

In this paper, a Centralized Conflict-free Assignment Learning Spiking neural network (C2ALS) is formulated for solving a Perimeter Defense Problem (PDP). Here, the region between the perimeter and the sensing range of the defender is divided into two layers. The outermost layer, in which the defender can only sense the intruders, is termed the sensing layer. The layer closest to the perimeter in which the defenders can sense and capture the intruders is termed the capture layer. Both layers are further divided into angular segments with respect to the center of the area to be protected. The spatiotemporal movements of both the defenders and intruders in these segments are converted into spikes and given as input to a Spiking Neural Network (SNN). The SNN is trained in a supervised manner to learn the intruder capture assignments of a defender in terms of these segments. Here, the desired assignments required for training a defender are referred to as greedy assignments because while generating them, priority is given to those segments that are relatively closer to the defender’s actual location. Inhibitory connections are used in the SNN architecture to obtain assignments without conflicts. Detailed performance study results show that the proposed C2ALS approach outperforms the other existing centralized optimization-based solutions for PDP by a 17% increase in the success rates of capturing the intruders. Also, C2ALS is the first centralized spike-based learning solution for PDP, capable of generating conflict-free assignments while ensuring robust intruder assignments with minimal defender movement.