Anytime Metaheuristic Framework for Global Route Optimization in Expected-Time Mobile Search
🤖 AI Summary
This work addresses the global path optimization problem for mobile sensors in static two-dimensional continuous environments, aiming to minimize the expected time to localize hidden objects. Due to the intractability of analytically evaluating the objective—stemming from continuous-space modeling and tight perception-motion coupling—existing methods struggle to balance accuracy and efficiency. To overcome this, we propose Milaps: a novel framework featuring (i) model-based formulation integrated with auxiliary objectives; (ii) an adaptive anytime metaheuristic algorithm that guarantees progressively improving solutions upon arbitrary termination; and (iii) synergistic components including TSP-D-inspired heuristics, explicit continuous-environment modeling, static sensing-weighted coverage, and minimum-latency approximation. Evaluated on a large-scale, newly constructed benchmark, Milaps significantly outperforms state-of-the-art approaches—generating high-quality initial solutions within milliseconds and advancing the Pareto frontier of solution quality versus computational efficiency.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Optimizes expected-time mobile search routes in continuous 2D environments
Addresses intractability of objective evaluation due to motion and visibility constraints
Improves solution quality and runtime trade-offs via anytime metaheuristic framework
Innovation
Methods, ideas, or system contributions that make the work stand out.
Model-based framework integrates novel auxiliary objectives
Adapts anytime metaheuristic for traveling deliveryman problem
Rapidly generates preliminary solution with static weight assignment
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