Preliminary Experimental Results of Context-Aware Teams of Multiple Autonomous Agents Operating under Constrained Communications

Author:

Martinez-Lorenzo Jose,Hudack Jeff,Jing YutaoORCID,Shaham Michael,Liang Zixuan,Al Bashit Abdullah,Wu Yushu,Zhang WeiteORCID,Skopin Matthew,Heredia-Juesas Juan,Ma Yuntao,Sweeney Tristan,Ares Nicolas,Fox Ari

Abstract

This work presents and experimentally tests the framework used by our context-aware, distributed team of small Unmanned Aerial Systems (SUAS) capable of operating in real time, in an autonomous fashion, and under constrained communications. Our framework relies on a three-layered approach: (1) an operational layer, where fast temporal and narrow spatial decisions are made; (2) a tactical layer, where temporal and spatial decisions are made for a team of agents; and (3) a strategical layer, where slow temporal and wide spatial decisions are made for the team of agents. These three layers are coordinated by an ad hoc, software-defined communications network, which ensures sparse but timely delivery of messages amongst groups and teams of agents at each layer, even under constrained communications. Experimental results are presented for a team of 10 small unmanned aerial systems tasked with searching for and monitoring a person in an open area. At the operational layer, our use case presents an agent autonomously performing searching, detection, localization, classification, identification, tracking, and following of the person, while avoiding malicious collisions. At the tactical layer, our experimental use case presents the cooperative interaction of a group of multiple agents that enables the monitoring of the targeted person over wider spatial and temporal regions. At the strategic layer, our use case involves the detection of complex behaviors, i.e., the person being followed enters a car and runs away, or the person being followed exits the car and runs away, which require strategic responses to successfully accomplish the mission.

Funder

United States Air Force Research Laboratory

Publisher

MDPI AG

Subject

Artificial Intelligence,Control and Optimization,Mechanical Engineering

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