Distributed Multi-robot Online Sampling with Budget Constraints

TL;DR

This paper introduces a distributed multi-robot sampling algorithm using Monte Carlo Tree Search for efficient data collection under travel budget constraints, improving field reconstruction accuracy in precision agriculture.

Contribution

It presents a novel online, distributed sampling method that effectively manages travel budgets and outperforms baseline approaches in multi-robot field data collection.

Findings

Outperforms baseline methods in tight budget scenarios

Reduces reconstruction errors in scalar field estimation

Effective across different team sizes and environments

Abstract

In multi-robot informative path planning the problem is to find a route for each robot in a team to visit a set of locations that can provide the most useful data to reconstruct an unknown scalar field. In the budgeted version, each robot is subject to a travel budget limiting the distance it can travel. Our interest in this problem is motivated by applications in precision agriculture, where robots are used to collect measurements to estimate domain-relevant scalar parameters such as soil moisture or nitrates concentrations. In this paper, we propose an online, distributed multi-robot sampling algorithm based on Monte Carlo Tree Search (MCTS) where each robot iteratively selects the next sampling location through communication with other robots and considering its remaining budget. We evaluate our proposed method for varying team sizes and in different environments, and we compare our solution with four different baseline methods. Our experiments show that our solution outperforms the baselines when the budget is tight by collecting measurements leading to smaller reconstruction errors.