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Vector Functions and Motion: Problem Solving

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Related Experiment Videos

Fault-Tolerant Cooperative Positioning for UAV Swarms in Degraded Environments: A Multi-Objective Deep Reinforcement

Peiru Yang1, Jiayong Li1, Xiaoyang Lan1

  • 1School of Airspace Science and Engineering, Shandong University, Weihai 264200, China.

Sensors (Basel, Switzerland)
|June 26, 2026
PubMed
Summary

This study introduces a fault-tolerant positioning framework for micro UAV swarms, significantly reducing tracking errors and preventing system divergence in challenging environments using multi-agent deep reinforcement learning and cooperative extended Kalman filtering.

Keywords:
UAV swarmcooperative localizationdeep reinforcement learning (DRL)error contagion isolationextended Kalman filter (EKF)fault tolerancemulti-objective optimization

Related Experiment Videos

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Systems

Background:

  • Micro Unmanned Aerial Vehicle (UAV) swarms struggle with positioning in complex environments due to Non-Line-of-Sight (NLOS) interference and sensor drift.
  • Cascaded errors in cooperative positioning can lead to system failures and divergence.

Purpose of the Study:

  • To develop a robust and resource-efficient positioning framework for micro UAV swarms.
  • To address cooperative positioning failures caused by NLOS interference and inertial sensor drift.
  • To integrate advanced AI with robust filtering techniques for enhanced navigation.

Main Methods:

  • Proposed a fault-tolerant positioning framework integrating Multi-Agent Deep Reinforcement Learning with Cooperative Extended Kalman Filtering (MADRL-CEKF).
  • Implemented a dynamic soft isolation mechanism to manage observation covariance and prevent error contagion.
  • Embedded an adaptive Markov smoothing constraint to mitigate control jitter.
  • Utilized a resource-aware multi-objective reward architecture tailored for micro UAVs.

Main Results:

  • Achieved a 96.01% reduction in average tracking error (Root Mean Square Error - RMSE) under extreme multi-node cascaded failures.
  • Completely prevented system divergence in simulated and real-world scenarios.
  • Reduced processing delay by 44% (to 25.1 ms) and energy consumption by 41% while maintaining accuracy within 0.16 m.
  • Ensured execution time remained within the 50 ms real-time threshold.

Conclusions:

  • The MADRL-CEKF framework offers a highly robust and resource-efficient navigation solution for micro UAV swarms.
  • Effectively bridges the gap between AI decision-making and strict engineering constraints in swarm robotics.
  • Demonstrates significant improvements in positioning accuracy and system stability in challenging operational conditions.