Anytime Metaheuristic Framework for Global Route Optimization in Expected-Time Mobile Search

TL;DR

This paper introduces an anytime metaheuristic framework called Milaps for efficient global route optimization in expected-time mobile search, significantly improving solution quality and runtime in continuous environments.

Contribution

It extends minimum latency problem models with auxiliary objectives and adapts a recent metaheuristic, enabling effective global route optimization for mobile search tasks.

Findings

Superior trade-offs between solution quality and runtime compared to baselines

Rapid initial solution generation with static weight assignment

Framework demonstrates flexibility across diverse scenarios

Abstract

Expected-time mobile search (ETS) is a fundamental robotics task where a mobile sensor navigates an environment to minimize the expected time required to locate a hidden object. Global route optimization for ETS in static 2D continuous environments remains largely underexplored due to the intractability of objective evaluation, stemming from the continuous nature of the environment and the interplay of motion and visibility constraints. Prior work has addressed this through partial discretization, leading to discrete-sensing formulations tackled via utility-greedy heuristics. Others have taken an indirect approach by heuristically approximating the objective using minimum latency problems on fixed graphs, enabling global route optimization via efficient metaheuristics. This paper builds on and significantly extends the latter by introducing Milaps (Minimum latency problems), a model-based solution framework for ETS. Milaps integrates novel auxiliary objectives and adapts a recent anytime metaheuristic for the traveling deliveryman problem, chosen for its strong performance under tight runtime constraints. Evaluations on a novel large-scale dataset demonstrate superior trade-offs between solution quality and runtime compared to state-of-the-art baselines. The best-performing strategy rapidly generates a preliminary solution, assigns static weights to sensing configurations, and optimizes global costs metaheuristically. Additionally, a qualitative study highlights the framework's flexibility across diverse scenarios.