Stochastic Planning for ASV Navigation Using Satellite Images

TL;DR

This paper introduces a robust route-planning algorithm for autonomous surface vessels that uses satellite images as a coarse map and accounts for environmental disturbances to improve water-quality monitoring efficiency.

Contribution

It presents a novel stochastic planning algorithm that minimizes expected travel distance under environmental uncertainties, validated through simulations and real-world experiments.

Findings

Algorithm reduces travel distance despite disturbances

Effective in simulations of over a thousand lakes

Successful real-world deployment on a 3.7 km lake route

Abstract

Autonomous surface vessels (ASV) represent a promising technology to automate water-quality monitoring of lakes. In this work, we use satellite images as a coarse map and plan sampling routes for the robot. However, inconsistency between the satellite images and the actual lake, as well as environmental disturbances such as wind, aquatic vegetation, and changing water levels can make it difficult for robots to visit places suggested by the prior map. This paper presents a robust route-planning algorithm that minimizes the expected total travel distance given these environmental disturbances, which induce uncertainties in the map. We verify the efficacy of our algorithm in simulations of over a thousand Canadian lakes and demonstrate an application of our algorithm in a 3.7 km-long real-world robot experiment on a lake in Northern Ontario, Canada. Videos are available on our website https://pcctp.github.io/.