Distributed Knowledge Leader Selection for Multi-Robot Environmental Sampling - Robotics Institute Carnegie Mellon University

Distributed Knowledge Leader Selection for Multi-Robot Environmental Sampling

Master's Thesis, Tech. Report, CMU-RI-TR-16-05, Robotics Institute, Carnegie Mellon University, May, 2016

Abstract

In many multi-robot applications such as target search, environmental monitoring and reconnaissance, the multi-robot system operates semi-autonomously, but under the supervision of a remote human who monitors task progress. In these applications, each robot collects a large amount of task-specific data that must be sent to the human periodically to keep the human aware of task progress. It is often the case that the human-robot communication links are extremely bandwidth constrained and/or have significantly higher latency than inter-robot communication links, so it is impossible for all robots to send their task-specific data together. Thus, only a subset of robots, which we call the knowledge leaders, can send their data at a time. In this paper, we study the knowledge leader selection problem, where the goal is to select a subset of robots with a given cardinality that transmits the most informative task-specific data for the human. We prove that the knowledge leader selection problem is a submodular optimization problem under some explicit conditions. We also present a novel distributed submodular optimization algorithm that has the same approximation guarantees as the centralized greedy algorithm for submodular function maximization. The effectiveness of our approach is demonstrated using numerical simulations.

BibTeX

@mastersthesis{Luo-2016-5520,
author = {Wenhao Luo},
title = {Distributed Knowledge Leader Selection for Multi-Robot Environmental Sampling},
year = {2016},
month = {May},
school = {Carnegie Mellon University},
address = {Pittsburgh, PA},
number = {CMU-RI-TR-16-05},
}