aiqbench analysis
This research advances autonomous robotics by integrating Extended Kalman Filter estimation with Fisher Information Matrix optimization to enhance source localization in noisy environments like gas leak detection. The proposed adaptive strategy explicitly regulates motion trajectories based on real-time uncertainty, offering superior tracking performance over existing geometric approaches without requiring prior knowledge of the target location.
Switched Turn-based Adaptive Source Seeking Strategy using Estimation and Information-driven Direction of Improvement
arXiv:2608.23068v1 Announce Type: new 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.
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 this http URL robotic source seeking, estimation and motion are closely linked: measurements improve the source estimate, while the chosen trajectory affects the quality of future this http URL loop-based geometric strategies generate feasible motion but do not explicitly use estimation uncertainty to regulate direction this http URL 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 this http URL results under stationary and moving source scenarios demonstrate improved tracking performance and reduced estimation error compared to purely information-driven or estimate-driven strategies.
Robotics (cs.RO); Systems and Control (eess.SY)
arXiv:2608.23068 [cs.RO]
(or arXiv:2608.23068v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2608.23068 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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