Moving-horizon nonlinear least squares-based multirobot cooperative perception

Aamir Ahmad, Heinrich Bulthoff

Research output: Contribution to journalArticle

Abstract

In this article we present an online estimator for multirobot cooperative localization and target tracking based on nonlinear least squares minimization. Our method not only makes the rigorous optimization-based approach applicable online but also allows the estimator to be stable and convergent. We do so by employing a moving horizon technique to nonlinear least squares minimization and a novel design of the arrival cost function that ensures stability and convergence of the estimator. Through an extensive set of real robot experiments, we demonstrate the robustness of our method as well as the optimality of the arrival cost function. The experiments include comparisons of our method with (i) an extended Kalman filter-based online-estimator and (ii) an offline-estimator based on full-trajectory nonlinear least squares.

Original languageEnglish
JournalRobotics and Autonomous Systems
DOIs
Publication statusAccepted/In press - 2016
Externally publishedYes

Fingerprint

Multi-robot
Nonlinear Least Squares
Horizon
Estimator
Cost functions
Extended Kalman filters
Cost Function
Target tracking
Comparison of Experiments
Experiments
Trajectories
Robots
Target Tracking
Stability and Convergence
Kalman Filter
Optimality
Robot
Perception
Trajectory
Robustness

Keywords

  • Cooperative localization and target tracking
  • Multirobot datasets
  • Nonlinear least squares
  • Soccer robots

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Software
  • Mathematics(all)

Cite this

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AB - In this article we present an online estimator for multirobot cooperative localization and target tracking based on nonlinear least squares minimization. Our method not only makes the rigorous optimization-based approach applicable online but also allows the estimator to be stable and convergent. We do so by employing a moving horizon technique to nonlinear least squares minimization and a novel design of the arrival cost function that ensures stability and convergence of the estimator. Through an extensive set of real robot experiments, we demonstrate the robustness of our method as well as the optimality of the arrival cost function. The experiments include comparisons of our method with (i) an extended Kalman filter-based online-estimator and (ii) an offline-estimator based on full-trajectory nonlinear least squares.

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