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

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.

Underwater Simulators Analysis for Digital Twinning

“…An example is [142], where a diver was chased by a vehicle sensing its presence through the images taken from the stereo camera it was equipped with. Further advances in target search are described in [143] where a methodology to provide the sampling points that the robot must visit was developed. Interestingly, the methodology also considers the level of fidelity with which the robot must collect data.…”