Confidence Boosts Trust-Based Resilience in Cooperative Multi-Robot Systems

The evolution of multi-robot systems has transitioned from simple distributed algorithms to complex, security-aware control frameworks. Early works like the average consensus protocol laid the foundation for cooperative control, but lacked robustness against malicious interference. Recent advances incorporate trust mechanisms, such as filtering and neighbor selection, to mitigate attacks. The emergence of physical channel-based trust estimation, leveraging wireless signal characteristics, has opened new avenues for enhancing resilience. However, challenges remain in accurately modeling trust under environmental variability and integrating dynamic trust assessments into real-time control. This paper situates itself within this context, aiming to develop a theoretically grounded, practically implementable protocol that combines trust estimation from physical channels with adaptive confidence tuning, addressing the limitations of static trust models and dense connectivity assumptions.

The core challenge is ensuring reliable multi-robot cooperation in environments where some agents may behave maliciously or communication channels are compromised. Existing methods depend heavily on network topology and static trust assumptions, which are insufficient when facing dynamic adversaries or unreliable signals. Misclassification of trustworthy neighbors can lead to system divergence or safety violations. The difficulty lies in designing a protocol that dynamically assesses trustworthiness, adapts to environmental changes, and guarantees convergence despite uncertainties. Achieving a balance between rapid task execution and robustness against malicious inputs remains a fundamental bottleneck, especially in large-scale, real-time robotic networks.

The key innovations include: 1) introducing a decaying confidence parameter λt that dynamically adjusts the influence of trust information, enabling a smooth transition from conservative to aggressive trust reliance; 2) utilizing physical channel features such as signal strength and delay to derive trust observations, reducing dependence on network connectivity; 3) providing rigorous theoretical proofs of almost sure convergence and explicit bounds on deviation and convergence rate, which were lacking in prior static trust-based schemes. This approach effectively combines physical-layer trust signals with adaptive control, offering a flexible and resilient framework for hostile environments.

• �� Establish trust variables αij(t) based on physical channel measurements, modeling the probability of neighbor trustworthiness.
• �� Compute aggregate trust βij(t) by summing deviations of αij(t) from 0.5, updating trust neighborhoods Ni(t) dynamically.
• �� Assign communication weights wij(t) based on trust neighborhood, with weights inversely proportional to trusted neighbor count.
• �� Introduce a decaying confidence parameter λt, modeled as λt = c e^{-γt}, controlling the influence of initial states versus trust-based information.
• �� Prove that as λt→0, the system converges almost surely to a consensus among legitimate robots, with bounds on steady-state deviation derived from trust estimation accuracy.
• �� Validate through simulations and real-world experiments, analyzing the impact of decay rate γ on convergence speed and bias.

Simulations involved a platoon of 50 autonomous vehicles with 20 malicious spoofers, testing the protocol under various malicious ratios and trust signal noise. Metrics included consensus accuracy, state deviation, and convergence time. Hyperparameters such as λt decay rate γ and trust thresholds were tuned for optimal performance. Real-world tests used a scaled autonomous vehicle platform, introducing spoofing attacks via signal spoofers, measuring stabilization time and deviation. Results consistently showed high resilience, with over 98% consensus accuracy and deviations below 0.05, validating the protocol’s robustness and practical feasibility.

The protocol maintains high consensus accuracy (>98%) under malicious ratios up to 50%, with steady-state deviations below ±0.05. The exponential decay λt enables rapid convergence within 5 seconds in real experiments, outperforming static trust schemes. The bounds on deviation confirm that the trust-based weighting effectively suppresses malicious influence, while the convergence analysis guarantees system stability. These results demonstrate the protocol’s capability to operate reliably in hostile environments, significantly advancing the robustness of distributed robotic control.

Applicable to autonomous vehicle platoons, UAV swarms, and industrial robot networks, especially where security threats are prevalent. The trust estimation from physical channels reduces reliance on dense communication graphs, enabling deployment in sparse or dynamic networks. It enhances safety and reliability in critical applications like automated transportation, surveillance, and disaster response, where malicious interference could cause catastrophic failures. The protocol’s adaptability makes it suitable for evolving threat landscapes, ensuring long-term operational resilience.

The trust estimation relies on assumptions about the statistical properties of physical signals, which may be affected by environmental factors like multipath or interference. The exponential λt decay, while effective in many scenarios, may require tuning for different attack intensities. Computational complexity increases with network size, necessitating scalable algorithms. Additionally, the current model assumes stationary trust statistics, which might not hold in highly dynamic environments, calling for adaptive trust models and real-time parameter tuning to maintain robustness.