IMMUNE-INSPIRED COOPERATIVE MECHANISM WITH REFINED LOW-LEVEL BEHAVIORS FOR MULTI-ROBOT SHEPHERDING

Abstract

In this paper, immune systems and its relationships with multi-robot shepherding problems are discussed. The proposed algorithm is based on immune network theories that have many similarities with the multi-robot systems domain. The underlying immune-inspired cooperative mechanism of the algorithm is simulated and evaluated. The paper also describes a refinement of the memory-based immune network that enhances a robot's action-selection process. A refined model, which is based on the Immune Network T-cell-regulated — with Memory (INT-M) model, is applied to the dog–sheep scenario. The refinements involves the low-level behaviors of the robot dogs, namely shepherds' formation and shepherds' approach. These behaviors would make the shepherds form a line behind the group of sheep and also obey a safety zone of each flock, thus achieving better control of the flock and minimize flock separation occurrences. Simulation experiments are conducted on the Player/Stage robotics platform.