Switched Turn-based Adaptive Source Seeking Strategy using Estimation and Information-driven Direction of Improvement
2026-08-24 • Robotics
Robotics
AI summaryⓘ
The authors study how robots can find the source of a signal, like a gas leak, by moving around and taking measurements that are often noisy. They combine a technique called the Extended Kalman Filter to estimate the source location with a method that decides movement directions based on how much new information will be gained. Their approach updates the robot's path at certain points to balance where it thinks the source is and where it can learn the most. Simulations show this method improves tracking and reduces errors compared to strategies that rely only on estimation or information gain alone.
source seekingExtended Kalman FilterFisher Information Matrixestimation uncertaintyrobotic motion planningsignal localizationsensor noisedirection selectionmeasurement updateconvergence detection
Authors
Shubhra Banerjee, Satadal Ghosh
Abstract
Source seeking arises in applications such as gas leak localization, radiation monitoring, and environmental surveillance, where the origin of an unknown signal field must be estimated from spatial measurements. In practice, the source location is not directly observable and must be inferred from noisy scalar measurements collected during motion.In robotic source seeking, estimation and motion are closely linked: measurements improve the source estimate, while the chosen trajectory affects the quality of future measurements.Existing loop-based geometric strategies generate feasible motion but do not explicitly use estimation uncertainty to regulate direction updates.This paper presents a loop-based source-seeking framework that combines Extended Kalman Filter (EKF) estimation with Fisher Information Matrix (FIM)-based direction selection. The source estimate is updated during motion, and the heading is changed at loop boundaries using both estimation uncertainty and predicted information gain. A measurement-based stopping condition is used to detect convergence without requiring prior knowledge of the source location.Simulation results under stationary and moving source scenarios demonstrate improved tracking performance and reduced estimation error compared to purely information-driven or estimate-driven strategies.