Active Engineering Computing & AI

Enhancing Drone Performance in Confined Spaces Using Pressure Sensors and Adaptive Control Algorithms

Funder EPSRC (part of UKRI)
Recipient organisation University of Strathclyde
Funding Not disclosed
Period Sept 2025 — Dec 2028

In plain English

AI plain-English summary

Drones flying inside nuclear plants, warehouses, and tunnels lose their GPS signal and often crash into walls or drift unstably. This project gives drones a new way to “feel” their surroundings by measuring tiny changes in air pressure with MEMS sensors—the same kind of microchip-scale devices found in smartphones and weather stations. The core problem is that existing indoor navigation tools—LIDAR, cameras, and GPS—fail in dusty, dark, or confined spaces. Pressure sensors detect turbulence, ground effect, and airflow disruptions that other sensors miss. The team will build modular pressure-sensor arrays for drones, fuse their data with other sensors, and design AI algorithms that adjust flight in real time. They will also use pressure differences to map an indoor space without GPS, a technique called pressure-based SLAM. If successful, this could make drones reliable for inspecting nuclear reactor vessels, searching collapsed buildings, or managing warehouse inventories—tasks currently done by humans in hazardous conditions. The research is applied engineering, not fundamental science, and aims directly at industrial safety and efficiency.

View original technical description
This project aims to enhance drone operations in confined and GPS-denied environments by integrating Micro-Electro-Mechanical Systems (MEMS) pressure sensors into UAV platforms. It focuses on improving drone stability, manoeuvrability, and navigation in complex indoor settings such as nuclear facilities, warehouses, and infrastructure sites. By detecting subtle atmospheric pressure variations, MEMS sensors enable real-time obstacle detection and adaptive control, helping to mitigate aerodynamic challenges such as turbulence, airflow disruption, and ground effect. Key research areas include the development of modular MEMS pressure sensor arrays optimized for UAVs, the advancement of sensor fusion strategies, and the design of AI-driven control algorithms for dynamic flight adjustments. The project also investigates the use of pressure differentials for Simultaneous Localization and Mapping (SLAM), offering a novel, reliable alternative to GPS, LIDAR, or vision-based systems, which often underperform indoors. The project will push the boundaries of drone aerodynamics and autonomous navigation. The developed systems will be directly applicable to industries requiring safe and efficient drone operation in confined or hazardous spaces.

View the original record at the funder ↗

Researchers

Peter Webb (Student)

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Original classification

Studentship

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