Handling Uncertainty in UAV Sensor Information using Bayesian Belief Network and Large Language Model

Sagir Yusuf, Chris Baber, Eyal Ofek

Published: 19 Feb 2025, Last Modified: 01 Mar 2026Drones and Unmanned SystemsEveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: This paper describes how UAVs can handle uncertainty in information collected from UAVs with heterogenous sensors. The approach reported here combines Bayesian Belief Network (BBN) with a Large Language Model (LLM). Our primary use case concerns the detection of forest fires but we also report laboratory experiments that are conducted using non-combustible objects. Objects’ colour, shape, are detected and interpreted using on-board sensors. Images from the UAV are also passed for interpretation to an LLM. None of the sources is perfectly applicable in all situations, as such, the UAV requires situation-based confirmation. Each of the sources is mapped to a node in BBN node with relations between nodes pre-defined through a Conditional Probability Distribution (CPD) created with input from Subject Matter Experts. We demonstrate the approach using DJI Ryze Tello programmable UAV and PyBBN scripts. The approach shows flexibility, adaptability, real-time analysis, and data saving (little data is required).