Time-Inverted Kuramoto Model Meets Lissajous Curves

Multi-Robot Persistent Monitoring and Target Detection

Journal article (2023)

Authors

M. Boldrer Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
L. Lyons Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
Luigi Palopoli Università di Trento
Daniele Fontanelli Università di Trento
L. Ferranti Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
Research Group
Learning & Autonomous Control (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2023 M. Boldrer, L. Lyons, Luigi Palopoli, Daniele Fontanelli, L. Ferranti
DOI: https://doi.org/10.1109/LRA.2022.3224661
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Published Date

2023

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Learning & Autonomous Control

Abstract

This letter proposes a distributed strategy to achieve both persistent monitoring and target detection in a rectangular and obstacle-free environment. Each robot has to repeatedly follow a smooth trajectory and avoid collisions with other robots. To achieve this goal, we rely on the time-inverted Kuramoto dynamics and the use of Lissajous curves. We analyze the resiliency of the system to perturbations or temporary failures, and we validate our approach through both simulations and experiments on real robotic platforms. In the letter, we adopt Model Predictive Contouring Control as a low level controller to minimize the tracking error while accounting for the robots' dynamical constraints and the control inputs saturation. The results obtained in the experiments are in accordance with the simulations.

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