# Decentralized Vision-Based Byzantine Agent Detection in Multi-Robot Systems with IOTA Smart Contracts

arXiv: https://arxiv.org/abs/2210.03441 · HTML: https://mlsanity.com/paper/2210.03441/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2022.

## Built on by (most cited first, 3 of 3)

- [Enforcing Cybersecurity Constraints for LLM-driven Robot Agents for Online Transactions](https://mlsanity.com/paper/2503.15546/) (2025)
- [Discovering Antagonists in Networks of Systems: Robot Deployment](https://mlsanity.com/paper/2502.20125/) (2025)
- [BARD-MARL: Byzantine-Agent Detection for Learned Communication in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2606.20701/) (2026)

## Abstract (arXiv)

Multiple opportunities lie at the intersection of multi-robot systems and distributed ledger technologies (DLTs). In this work, we investigate the potential of new DLT solutions such as IOTA, for detecting anomalies and byzantine agents in multi-robot systems in a decentralized manner. Traditional blockchain approaches are not applicable to real-world networked and decentralized robotic systems where connectivity conditions are not ideal. To address this, we leverage recent advances in partition-tolerant and byzantine-tolerant collaborative decision-making processes with IOTA smart contracts. We show how our work in vision-based anomaly and change detection can be applied to detecting byzantine agents within multiple robots operating in the same environment. We show that IOTA smart contracts add a low computational overhead while allowing to build trust within the multi-robot system. The proposed approach effectively enables byzantine robot detection based on the comparison of images submitted by the different robots and detection of anomalies and changes between them.