Olfactory pursuit: catching a moving odor source in complex flows

2026-04-13

AI Summary

This study addresses the challenging problem of tracking a moving source in complex turbulent flows using delayed and intermittent odor signals. The task is formulated as a partially observable Markov decision process (POMDP), incorporating a discrete run-and-tumble motion model to maintain a joint belief over the target’s position and velocity. A near-optimal policy is derived by numerically solving the Bellman equation. The work introduces a novel hybrid strategy that combines the information-gain mechanism of Infotaxis with a greedy value function obtained from fully observable control, significantly enhancing tracking performance across varying levels of target motion persistence. Experimental results demonstrate that the proposed approach closely approximates the theoretical optimum, substantially outperforms purely exploratory strategies, and exhibits strong robustness under model mismatch and more realistic plume dynamics.

Technology Category

• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)
• Search and Optimization: Mixed Discrete/Continuous Search
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising

Abstract

Locating and intercepting a moving target from possibly delayed, intermittent sensory signals is a paradigmatic problem in decision-making under uncertainty, and a fundamental challenge for, e.g., animals seeking prey or mates and autonomous robotic systems. Odor signals are intermittent, strongly mixed by turbulent-like transport, and typically lag behind the true target position, thereby complicating localization. Here, we formulate olfactory pursuit as a partially observable Markov decision process in which an agent maintains a joint belief over the target's position and velocity. Using a discrete run-and-tumble model, we compute quasi-optimal policies by numerically solving the Bellman equation and benchmark them against well-established information-theoretic strategies such as Infotaxis. We show that purely exploratory policies are near-optimal when the target frequently reorients, but fail dramatically when the target exhibits persistent motion. We thus introduce a computationally efficient hybrid policy that combines the information-gain drive of Infotaxis with a "greedy" value function derived from an associated fully observable control problem. Our heuristic achieves near-optimal performance across all persistence times and substantially outperforms purely exploratory approaches. Moreover, our proposal demonstrates strong robustness even in more complex search scenarios, including continuous run-and-tumble prey motion with moderate persistence time, model mismatch, and more accurate plume dynamics representation. Our results identify predictive inference of target motion as the key ingredient for effective olfactory pursuit and provide a general framework for search in information-poor, dynamically evolving environments.

Problem

• olfactory pursuit
• moving odor source
• intermittent signals
• turbulent transport
• target localization

Innovation

• olfactory pursuit
• partially observable Markov decision process
• Infotaxis
• predictive inference
• hybrid policy