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The structure of a crystalline solid, whether a metal or not, is best described by considering its simplest repeating unit, which is referred to as its unit cell. The unit cell consists of lattice points that represent the locations of atoms or ions. The entire structure then consists of this unit cell repeating in three dimensions. The three different types of unit cells present in the cubic lattice are illustrated in Figure 1.
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Large-Scale Multi-UAV Task Allocation via a Centrality-Driven Load-Aware Adaptive Consensus Bundle Algorithm for

Weifei Gan1, Hongxuan Xu1, Yunwei Bai1

  • 1School of Computer and Information Engineering, Institute for Artificial Intelligence, Shanghai Polytechnic University, Shanghai 201209, China.

Biomimetics (Basel, Switzerland)
|January 27, 2026
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Summary
This summary is machine-generated.

This study introduces CLAC-CBBA, a novel algorithm for task assignment in large drone swarms. It enhances scalability and robustness by using adaptive clustering and load balancing, improving mission efficiency.

Keywords:
K-medoidscentrality-driven clusteringconsensus-based bundle algorithmheterogeneous UAV systemslarge multi-UAV task allocationload-aware cluster self-regulation

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Area of Science:

  • Robotics and Autonomous Systems
  • Distributed Artificial Intelligence
  • Swarm Intelligence

Background:

  • Large multi-UAV systems face challenges in distributed task assignment due to time-varying communication and heterogeneous platforms.
  • Classical methods often lead to excessive message passing and suboptimal task-resource matching in complex swarm operations.

Purpose of the Study:

  • To propose CLAC-CBBA, an enhanced Consensus-Based Bundle Algorithm (CBBA) for scalable and robust task allocation in large heterogeneous UAV swarms.
  • To improve upon existing distributed task assignment methods by integrating swarm-inspired self-organization and load-aware self-regulation.

Main Methods:

  • CLAC-CBBA employs a centrality-based adaptive cluster-reconfiguration (CenCluster) to partition networks and reduce communication.
  • A load-aware cluster self-regulation (LCSR) mechanism uses K-medoids clustering and real-time load balancing for dynamic cluster adjustments.
  • CBBA bidding is localized within clusters, with synchronization of winners/bids by anchors and representatives for global consistency.

Main Results:

  • Simulations demonstrate CLAC-CBBA significantly reduces communication overhead and runtime compared to CBBA and other variants.
  • The algorithm achieves statistically significant improvements in total task score across various network densities and swarm sizes.
  • CLAC-CBBA exhibits enhanced scalability and robustness in large-scale heterogeneous UAV task allocation scenarios.

Conclusions:

  • CLAC-CBBA offers a biomimetic, decentralized approach to task assignment in large UAV swarms.
  • The proposed method effectively balances scalability, robustness, and assignment optimality for complex multi-UAV missions.
  • CLAC-CBBA represents a significant advancement in distributed task allocation for heterogeneous robotic swarms.