Adaptive search with path-constrained mobile sensors using multi-arm bandits
Abstract
We study a constrained sequential decision problem for detecting regions of interest from noisy online measurements collected by a team of energy-constrained mobile sensors. The sensor motion is restricted to a precomputed set of feasible paths that satisfy mobility constraints, limited available energy by requiring return to charging stations, and avoidance of known obstacles. We cast the resulting active path selection as a multi-armed bandit problem and propose an iterative search algorithm that adaptively assigns paths using confidence bounds computed from the collected data. We also propose a graph-based method to construct low-cardinality path covers under the team's constraints. We establish finite-time and anytime probabilistic correctness guarantees for the proposed algorithm, derive non-asymptotic upper bounds on the times required to detect all regions of interest and to label all nodes, and show that these bounds match algorithm-independent lower bounds up to constant factors for a broad class of problem instances. Using numerical simulations, we illustrate the behavior of the proposed approach and the effect of path redundancy and team size on search performance.
Related
- Adaptive Random Matrices in Gaussian Bandits: Spectral Universality and Selection-Induced Outliers
- PDE-constrained optimization for virtual sensing in structural dynamics: Full-field displacement and force recovery from sparse sensors
- NomadBBO: A direct-search framework for constrained mixed-variable blackbox optimization in Python
- Trajectory Parameterization and Evolutionary Search for Constrained Optimal Control
- Automated Priority Rule Design for the Resource-Constrained Project Scheduling Problem: A Large Language Model-Guided Population-Based Search
BibTeX
@misc{vinod2026adaptive,
title = {{Adaptive search with path-constrained mobile sensors using multi-arm bandits}},
author = {Abraham P. Vinod and Parth K. Thaker and Gautam Dasarathy and Stefano Di Cairano},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.40431},
eprint = {2610.40431},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.40431},
}
RIS
TY - UNPB
TI - Adaptive search with path-constrained mobile sensors using multi-arm bandits
AU - Abraham P. Vinod
AU - Parth K. Thaker
AU - Gautam Dasarathy
AU - Stefano Di Cairano
PY - 2026
DA - 2026/10/29
PB - arXiv
DO - 10.48550/arxiv.2610.40431
UR - https://arxiv.org/abs/2610.40431
AB - We study a constrained sequential decision problem for detecting regions of interest from noisy online measurements collected by a team of energy-constrained mobile sensors. The sensor motion is restricted to a precomputed set of feasible paths that satisfy mobility constraints, limited available energy by requiring return to charging stations, and avoidance of known obstacles. We cast the resulting active path selection as a multi-armed bandit problem and propose an iterative search algorithm that adaptively assigns paths using confidence bounds computed from the collected data. We also propose a graph-based method to construct low-cardinality path covers under the team's constraints. We establish finite-time and anytime probabilistic correctness guarantees for the proposed algorithm, derive non-asymptotic upper bounds on the times required to detect all regions of interest and to label all nodes, and show that these bounds match algorithm-independent lower bounds up to constant factors for a broad class of problem instances. Using numerical simulations, we illustrate the behavior of the proposed approach and the effect of path redundancy and team size on search performance.
ER -