Soil Sample Search in Partially Observable Environments

TL;DR

This paper presents a heuristic search method enabling autonomous robots to efficiently locate soil samples in outdoor environments with limited visibility, improving speed and success rate over naive approaches.

Contribution

The paper introduces a novel heuristic guided search algorithm tailored for soil sampling in partially observable outdoor settings.

Findings

The proposed method outperforms naive baselines in speed and success rate.

Simulation results demonstrate improved efficiency in soil sample localization.

Algorithm reduces travel distance compared to traditional search methods.

Abstract

To work in unknown outdoor environments, autonomous sampling machines need the ability to target samples despite limited visibility and robotic arm reach distance. We design a heuristic guided search method to speed up the search process and more efficiently localize the approximate center of soil regions. Through simulation experiments, we assess the effectiveness of the proposed algorithm and discover superior performance in terms of speed, distance traveled, and success rate compared to naive baselines.