Scalable Multirobot Planning for Informed Spatial Sampling

TL;DR

This paper introduces a scalable distributed multi-robot planning algorithm for efficient spatial sampling, capable of real-time operation and effective with limited communication, demonstrated through simulations on synthetic and real data.

Contribution

A novel distributed multi-robot sampling algorithm that is scalable, adaptive, and effective with limited communication, improving data collection in large environments.

Findings

Algorithm performs well with limited communication range.

Scalable and adaptable to various environmental scenarios.

Effective in real-time sampling of large-scale fields.

Abstract

This paper presents a distributed scalable multi-robot planning algorithm for informed sampling of quasistatic spatial fields. We address the problem of efficient data collection using multiple autonomous vehicles and consider the effects of communication between multiple robots, acting independently, on the overall sampling performance of the team. We focus on the distributed sampling problem where the robots operate independent of their teammates, but have the ability to communicate their current state to other neighbors within a fixed communication range. Our proposed approach is scalable and adaptive to various environmental scenarios, changing robot team configurations, and runs in real-time, which are important features for many real-world applications. We compare the performance of our proposed algorithm to baseline strategies through simulated experiments that utilize models derived from both synthetic and field deployment data. The results show that our sampling algorithm is efficient even when robots in the team are operating with a limited communication range, thus demonstrating the scalability of our method in sampling large-scale environments.