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<p>Cooperative localization (CL) is crucial for successful execution of advanced tasks by a team of mobile robots in featureless environments. However, CL requires information exchange between robots in the team, which often results in large communication bandwidth occupancy and energy costs. To address this problem, a new decentralized Event-Triggered Extended Kalman Filter-based Cooperative Localization (ETEKF CL) algorithm is proposed in this paper. Compared with existing CL algorithms, the proposed ETEKF CL algorithm can significantly reduce communication burden and energy costs. By tuning the weight factor in the event-triggered mechanism (ETM), the multirobot system can achieve a desired trade-off between communication rate and localization accuracy. Additionally, the inter-robot correlation is maintained such that the benefits of robot-to-robot measurement can be expanded to the entire team. The localization uncertainty of ETEKF CL is proven to be bounded through theoretical analysis, and the effectiveness of the ETEKF CL algorithm is demonstrated through both simulations and experiments.</p>

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Multirobot cooperative localization based on event-triggered mechanism

  • 期刊论文

  • 发表日期：

    2026年1月3日

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  • Yiyang Liu,
  • Heng Wang,
  • Siqi Wang,
  • Qing Li

摘要

Cooperative localization (CL) is crucial for successful execution of advanced tasks by a team of mobile robots in featureless environments. However, CL requires information exchange between robots in the team, which often results in large communication bandwidth occupancy and energy costs. To address this problem, a new decentralized Event-Triggered Extended Kalman Filter-based Cooperative Localization (ETEKF CL) algorithm is proposed in this paper. Compared with existing CL algorithms, the proposed ETEKF CL algorithm can significantly reduce communication burden and energy costs. By tuning the weight factor in the event-triggered mechanism (ETM), the multirobot system can achieve a desired trade-off between communication rate and localization accuracy. Additionally, the inter-robot correlation is maintained such that the benefits of robot-to-robot measurement can be expanded to the entire team. The localization uncertainty of ETEKF CL is proven to be bounded through theoretical analysis, and the effectiveness of the ETEKF CL algorithm is demonstrated through both simulations and experiments.