Expedited Multi-Target Search with Guaranteed Performance via Multi-fidelity Gaussian Processes

TL;DR

This paper introduces EMTS, a novel algorithm for autonomous multi-target search that uses multi-fidelity Gaussian processes to balance coverage and accuracy, providing guarantees on detection performance and efficiency.

Contribution

The paper develops EMTS, a new algorithm that integrates multi-fidelity Gaussian processes for efficient, guaranteed multi-target search with formal performance analysis.

Findings

EMTS achieves high detection accuracy with reduced search time.

The algorithm provides formal guarantees on detection performance.

Simulation results demonstrate effectiveness in multi-target scenarios.

Abstract

We consider a scenario in which an autonomous vehicle equipped with a downward facing camera operates in a 3D environment and is tasked with searching for an unknown number of stationary targets on the 2D floor of the environment. The key challenge is to minimize the search time while ensuring a high detection accuracy. We model the sensing field using a multi-fidelity Gaussian process that systematically describes the sensing information available at different altitudes from the floor. Based on the sensing model, we design a novel algorithm called Expedited Multi-Target Search (EMTS) that (i) addresses the coverage-accuracy trade-off: sampling at locations farther from the floor provides wider field of view but less accurate measurements, (ii) computes an occupancy map of the floor within a prescribed accuracy and quickly eliminates unoccupied regions from the search space, and (iii) travels efficiently to collect the required samples for target detection. We rigorously analyze the algorithm and establish formal guarantees on the target detection accuracy and the expected detection time. We illustrate the algorithm using a simulated multi-target search scenario.