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    Area of Science:

    • Robotics
    • Distributed Systems
    • Optimization

    Background:

    • Multi-agent systems often require coordination without a central controller.
    • Agents may have limited information about their environment and other agents.
    • The distributed rendezvous problem aims for agents to meet at a common point.

    Purpose of the Study:

    • To develop and validate algorithms for distributed rendezvous in multi-agent networks.
    • To achieve rendezvous at an optimal location minimizing total squared distance to assigned convex regions.
    • To address scenarios with potentially non-intersecting and time-varying convex regions.

    Main Methods:

    • A distributed time-varying algorithm for bounded convex regions, guaranteeing asymptotic convergence.
    • A distributed tracking and estimation algorithm for general, possibly unbounded convex regions, ensuring finite-time rendezvous.
    • Experimental validation using multiple ground robots to demonstrate algorithm performance.

    Main Results:

    • Algorithms guarantee agents rendezvous at the optimal location, minimizing total squared distance.
    • Finite-time convergence is achieved for general convex regions and time-varying regions with projection compressibility.
    • Experimental results confirm the theoretical findings in a practical multi-robot setting.

    Conclusions:

    • The proposed distributed algorithms effectively solve the shortest distance rendezvous problem for multi-agent systems.
    • The methods are robust to different types of convex regions, including unbounded and time-varying ones.
    • Successful experimental implementation validates the practical applicability of the developed algorithms.