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A flocking algorithm for multi-agent systems with connectivity preservation under hybrid metric-topological

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This study introduces a new flocking algorithm for multi-agent systems using a hybrid metric-topological distance. This approach enhances efficiency and flexibility by creating sparser interaction topologies, leading to stable formations.

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Area of Science:

  • Robotics and Control Systems
  • Distributed Systems
  • Computational Geometry

Background:

  • Flocking algorithms are crucial for coordinating multi-agent systems.
  • Existing methods often rely on simpler graph structures like disk graphs, which can be inefficient.
  • Maintaining connectivity and flexibility in agent interactions remains a challenge.

Purpose of the Study:

  • To propose a novel connectivity-preserving flocking algorithm for multi-agent systems.
  • To leverage a hybrid metric-topological distance for defining agent interactions.
  • To improve efficiency and flexibility compared to existing flocking algorithms.

Main Methods:

  • Defining agent neighbor sets using a hybrid metric-topological distance.
  • Representing the interaction topology as a range-limited Delaunay graph.
  • Proving stability conditions for flocking motion under specific system parameters.

Main Results:

  • The proposed algorithm utilizes a sparser range-limited Delaunay graph, reducing information exchange.
  • Dynamic deletion of irrelevant links enhances system flexibility.
  • The system spontaneously forms regular quasi-lattice formations without specific range constraints.

Conclusions:

  • The developed flocking algorithm is more efficient and flexible than traditional methods.
  • It enables stable flocking motion in multi-agent systems through a novel interaction topology.
  • The algorithm's effectiveness is validated through numerical simulations.