Reconfigurable Robots for Scaling Reef Restoration

TL;DR

This paper introduces a reconfigurable autonomous robot system for coral reef restoration that enhances deployment efficiency, reduces human risk, and improves larval settlement success through real-time substrate classification.

Contribution

It presents a novel reconfigurable robot platform with integrated real-time substrate classification, enabling scalable and efficient coral reef restoration.

Findings

21.8 times more area coverage than manual methods

Improved coral larvae release onto suitable substrates

Reduced need for human diving and training

Abstract

Coral reefs are under increasing threat from the impacts of climate change. Whilst current restoration approaches are effective, they require significant human involvement and equipment, and have limited deployment scale. Harvesting wild coral spawn from mass spawning events, rearing them to the larval stage and releasing the larvae onto degraded reefs is an emerging solution for reef restoration known as coral reseeding. This paper presents a reconfigurable autonomous surface vehicle system that can eliminate risky diving, cover greater areas with coral larvae, has a sensory suite for additional data measurement, and requires minimal non-technical expert training. A key feature is an on-board real-time benthic substrate classification model that predicts when to release larvae to increase settlement rate and ultimately, survivability. The presented robot design is reconfigurable, light weight, scalable, and easy to transport. Results from restoration deployments at Lizard Island demonstrate improved coral larvae release onto appropriate coral substrate, while also achieving 21.8 times more area coverage compared to manual methods.