CONCEPT OF AN INTELLIGENT ROBOTIZED SENSOR NETWORK FOR AUTONOMOUS DEMINING BASED ON FUZZY DATA INTEGRATION

Abstract

This article presents an advanced, next-generation concept for robotic support in humanitarian demining (HD), aimed at overcoming the fundamental limitations of classical autonomous systems that operate in environments dominated by uncertainty, noise, and heterogeneous interference. The proposed framework is structured as a multilayer intelligent architecture that integrates a multi-source sensor network, a novel Fuzzy Data Integration System (FS-system), a decentralized dataset-driven verification mechanism (Randomized Verification Routing, RVR), and a complex temporal-pattern Sequence Analysis Algorithm (QS-system). Through their synergistic interaction, the modules collectively enable reliable detection of Explosive Hazards (EH) under ambiguous, dynamic, and resource-constrained field conditions where deterministic robotic approaches or single-sensor solutions fail to ensure the required robustness and decision accuracy. A central contribution of the study is the development of formal mathematical foundations supporting each architectural component. Detailed sensor-channel models capture physical behavior, noise distribution, and uncertainty envelopes for ground-penetrating radar, magnetometric systems, metal detectors, and auxiliary sensors. The FS-system relies on specially constructed fuzzy membership functions that transform noisy sensor readings into interpretable fuzzy risk metrics, thus providing a resilient mechanism for handling imprecision in EH detection. To support collective decision-making, a probabilistic trust model is introduced to evaluate the credibility of distributed “knowledge items,” allowing robotic units to dynamically calibrate their confidence in shared data. Building on these trust estimates, the RVR mechanism optimizes decentralized verification strategies by allocating follow-up inspection tasks to robot–sensor combinations most capable of refining detection in high-risk areas.

The article further presents an integrated system-level model describing the end-to-end flow—from raw data acquisition to final hazard classification—accompanied by precise algorithms governing sequential fusion, temporal sequence analysis, and adaptive spatial verification. A dedicated Python-based simulator was developed to evaluate system performance under synthetic and real-world interference patterns. Experimental results demonstrate that the combined FS-, QS-, and RVR-modules significantly improve detection accuracy and resilience to false alarms, yielding an overall performance increase of 18–34% compared with traditional approaches. These findings confirm that the proposed architecture provides a robust, scalable foundation for future autonomous robotic platforms designed for critical humanitarian demining operations worldwide.