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    Area of Science:

    • Robotics
    • Control Theory
    • Artificial Intelligence

    Background:

    • Distributed coverage control is crucial for multi-robot systems.
    • Heterogeneous robots with nonlinear dynamics present unique deployment challenges.
    • Environmental uncertainty requires adaptive control strategies.

    Purpose of the Study:

    • To address the distributed coverage control problem for heterogeneous robots with nonlinear dynamics.
    • To develop a partitioning algorithm that accounts for robot heterogeneity.
    • To propose a distributed deployment strategy for persistent monitoring.

    Main Methods:

    • Formulating an optimal tracking control problem with a discounted cost function.
    • Utilizing the state-dependent Riccati equation (SDRE) approach.
    • Designing a partitioning cost metric based on state-dependent proximity, tracking error, and control energy.

    Main Results:

    • A novel partitioning algorithm effectively captures robot dynamics heterogeneity.
    • The size of robot-assigned subgraphs is shown to depend on individual robot capabilities.
    • A distributed deployment strategy optimizes robot distribution for persistent monitoring.

    Conclusions:

    • The proposed methodology is viable and effective for deploying heterogeneous multi-agent systems.
    • The approach enhances the performance of robots in partially known environments.
    • Simulations and experimental studies validate the efficacy of the developed control strategy.