Switched Turn-based Adaptive Source Seeking Strategy using Estimation and Information-driven Direction of Improvement

Shubhra Banerjee; Satadal Ghosh

cs.ROAug 24, 2026

Switched Turn-based Adaptive Source Seeking Strategy using Estimation and Information-driven Direction of Improvement

Authors: Shubhra Banerjee, Satadal Ghosh

Organizations: Department of Aerospace Engineering, Indian Institute of Technology Madras, Chennai 600036, India

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.

Figures & tables

Fig. 1 : Planar source-seeking scenario. A sensor-mounted agent operates in a two-dimensional workspace and collects scalar field measurements generated by an unknown source.

Fig. 2 : Loop-based source-seeking strategy with direction updated at the end of each loop (switching point).

Panel of Figure 3 — Fig. 3 : Trajectory comparison across three source dynamics (rows) and motion regulation strategies (columns). The information-driven $\mathbf{d}_{\text{info}}^{(i)}$ promotes exploration, while the estimate-driven $\mathbf{d}_{\text{est}}^{(i)}$ exploits the current source estimate. The joint strategy $\mathbf{d}_{\text{joint}}^{(i)}$ balances both, resulting in improved convergence, reduced tracking lag, and more stable trajectories across stationary and dynamic scenarios.

Panel of Figure 4 — Fig. 4 : Estimation error $\left\lVert\hat{\mathbf{s}}_{i,N_{i}}-\mathbf{s}_{i,N_{i}}\right\rVert$ evaluated at switching points for stationary, non-maneuvering and maneuvering source source dynamics. The joint strategy achieves faster convergence and lower terminal error across all cases.

Panel of Figure 5 — Fig. 5 : Distance to source $\|\mathbf{r}_{i,N_{i}}\|$ versus loop index $i$ for stationary, non-maneuvering and maneuvering source source dynamics. The joint strategy $\mathbf{d}_{\text{joint}}^{(i)}$ consistently yields faster convergence and reduced tracking lag compared to the information-driven $\mathbf{d}_{\text{info}}^{(i)}$ and estimate-driven $\mathbf{d}_{\text{est}}^{(i)}$ strategies.

Source Type	Turn-switching direction
	d info (i)	d est (i)	d joint (i)
Stationary	1.569	0.457	0.366
Straight-line	1.507	2.033	0.265
Circular	4.761	2.946	1.010

TABLE I : Terminal estimation error (m) for different source types using turn-switching directions.

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Distill-Belief: Closed-Loop Inverse Source Localization and Characterization in Physical Fields

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SEMT, University of Bristol, Bristol, UK · LUCAS, Loughborough University, Loughborough, UK

Belief-Space Planning Reward Hacking Active Sensing Inverse Problems

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{Closed-loop inverse source localization and characterization (ISLC) requires a mobile agent to select measurements that localize sources and infer latent field parameters under strict time constraints.} {The core challenge lies in the belief-space objective: valid uncertainty estimation requires expensive Bayesian inference, whereas using fast learned belief model leads to reward hacking, in which the policy exploits approximation errors rather than actually reducing uncertainty.} {We propose Distill-Belief, a teacher--student framework that decouples correctness from efficiency. A Bayes-correct particle-filter teacher maintains the posterior and supplies a dense information-gain signal, while a compact student distills the posterior into belief statistics for control and an uncertainty certificate for stopping. At deployment, only the student is used, yielding constant per-step cost.} {Experiments on seven field modalities and two stress tests show that Distill-Belief consistently reduces sensing cost and improves success, posterior contraction, and estimation accuracy over baselines, while mitigating reward hacking.}

Abstract

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