Real-Time Area Coverage and Target Localization Using Receding-Horizon Ergodic Exploration

Anastasia Mavrommati*, Emmanouil Tzorakoleftherakis, Ian Abraham, Todd D. Murphey

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Although a number of solutions exist for the problems of coverage, search, and target localization - commonly addressed separately - whether there exists a unified strategy that addresses these objectives in a coherent manner without being application specific remains a largely open research question. In this paper, we develop a receding-horizon ergodic control approach, based on hybrid systems theory, that has the potential to fill this gap. The nonlinear model-predictive control algorithm plans real-time motions that optimally improve ergodicity with respect to a distribution defined by the expected information density across the sensing domain. We establish a theoretical framework for global stability guarantees with respect to a distribution. Moreover, the approach is distributable across multiple agents so that each agent can independently compute its own control while sharing statistics of its coverage across a communication network. We demonstrate the method in both simulation and in experiment in the context of target localization, illustrating that the algorithm is independent of the number of targets being tracked and can be run in real time on computationally limited hardware platforms.

Original languageEnglish (US)
Article number8114522
Pages (from-to)62-80
Number of pages19
JournalIEEE Transactions on Robotics
Volume34
Issue number1
DOIs
StatePublished - Feb 2018

Keywords

  • Mobile robots
  • motion planning
  • nonlinear control systems
  • unmanned autonomous vehicles

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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