Bio-inspired multi-robot communication through behavior recognition

This paper focuses on enabling multi-robot teams to cooperatively perform tasks without the use of radio or acoustic communication. One key to more effective cooperative interaction in a multi-robot team is the ability to understand the behavior and intent of other robots. This is similar to the honey bee “waggle dance” in which a bee can communicate the orientation and distance of a food source. In this similar manner, our heterogenous multi-robot team uses a specific behavior to indicate the location of mine-like objects (MLOs). Observed teammate action sequences can be learned to perform behavior recognition and task-assignment in the absence of communication. We apply Conditional Random Fields (CRFs) to perform behavior recognition as an approach to task monitoring in the absence of communication in a challenging underwater environment. In order to demonstrate the use of behavior recognition of an Autonomous Underwater Vehicle (AUV) in a cooperative task, we use trajectory based techniques for model generation and behavior discrimination in experiments using simulated scenario data. Results are presented demonstrating heterogenous teammate cooperation between an AUV and an Autonomous Surface Vehicle (ASV) using behavior recognition rather than radio or acoustic communication in a mine clearing task.