Cooperative Prey Hunting for Multi Agent System Designed using Bio-Inspired adaptation Technique

Abstract

In this paper, a biologically inspired cooperative prey hunting strategy is implemented for a swarm of UAVs. The cooperative hunting strategy is based on diffusion and adaptation algorithms. Diffusion and adaptation algorithms exhibit features of self-organization to create a mobile adaptive networks. Nodes in mobile adaptive networks are equipped with both learning and mobility capabilities, as they interact with one another on a local level to find solutions to distributed processing and distributed inference issues. The findings contribute to an understanding of the dynamic network structures that emerge during interactions between swarm of fish and the predators. The Swarm of fish and the predators are modelled using the unicycle model moving in a 2D plane. The swarm of fish forage in search of food which are attacked by the predators which follow hunting strategy defined by the state transition model. An optimal PSO tuned Fractional Order PID controller is designed to ensure the trajectory tracking of the UAVs. Simulation results exhibit the effectiveness of the work.