Adaptive Trust-Based Fault Tolerance for Multi-Drone Systems: Theory and Application in Agriculture

This paper presents RobotTrust, an adaptive trust framework for fault-tolerant coordination in multi-drone systems for precision agriculture. The study aims to improve mission reliability under sensor/actuator faults and uncertain interactions by combining a structured fault taxonomy (behavioral, actuator, sensor) with team-based recovery and an adaptive trust model that integrates direct experience with filtered indirect recommendations. We formalize trust computation (direct, recommended, and global trust) and introduce safeguards such as a minimum-trust threshold and weighted fusion to curb misinformation propagation. The framework is evaluated in simulation using the AgriFleet drone team and is compared against the TReconf baseline across three metrics: (i) time-step efficiency for task completion, (ii) RMSD between predicted and true trustworthiness, and (iii) interaction quality (preference for reliable peers). Results show 20–40% faster task completion, lower RMSD (more accurate trust estimation), and selective interaction patterns that prioritize dependable agents while limiting exposure to unreliable ones. These findings indicate that RobotTrust enhances responsiveness and robustness in decentralized, fault-prone environments typical of agricultural deployments. The work contributes a practical, generalizable approach to trust-aware coordination in multi-robot systems and outlines directions for context-aware weighting, explainable trust signals, heterogeneous teams, adversarial robustness, and large-scale field trials.

@article{GHARBI2025,
  title     = {Adaptive Trust-Based Fault Tolerance for Multi-Drone Systems: Theory and Application in Agriculture},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {9},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Atef GHARBI and Faheed A. F. Alrslani},
  doi       = {10.14569/IJACSA.2025.0160913},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160913}
}