In this episode, Melanie Moses shares her experience of studying volcanoes in Iceland using a swarm of drones to map out the CO2 plume emitted by the volcano. The chapter discusses the use of unmanned drones to measure the gases emitted by volcanoes and the potential for predicting future eruptions. It explores the innovative use of drones in collecting data from volcanoes and showcases the programming and self-organization of co-robots. The process of mapping volcanic plumes and measuring gas concentrations using drones is explained. The episode also explores the utilization of the sketch algorithm and human intelligence to guide the drones in mapping volcanic plumes.
Read more
AI Summary
AI Chapters
Episode notes
auto_awesome
Podcast summary created with Snipd AI
Quick takeaways
Using a swarm of drones programmed with flocking algorithms can provide more comprehensive measurements of CO2 emissions from volcanoes.
The Vulcan project aims to create a cooperative system where drones and human scientists collaborate to gather data on volcanic CO2 emissions, mimicking the principles of flocking and combining the skills of drones with human intelligence.
Deep dives
Using drones to study CO2 emissions from volcanoes
In this podcast episode, Melanie Moses discusses her trip to Iceland to study volcanoes using a swarm of drones to measure CO2 emissions. They aimed to get more accurate measures of carbon dioxide emissions to help predict volcanic eruptions and understand the volcanic contribution to climate change. They visited both an active and a dormant volcano, collecting measurements and studying the CO2 plumes. The drones, programmed with flocking algorithms, were able to fly in formation and collect dispersed measurements, providing a more comprehensive understanding of the CO2 distribution.
Designing an intelligent cooperative system
The goal of the Vulcan project is to create a cooperative system where a team of drones collaboratively work with human scientists to gather data on volcanic CO2 emissions. By using adaptive natural algorithms, the drones are programmed to fly autonomously and change their behavior based on the measurements they collect. They can form flocks, follow concentration gradients, and adjust their paths to locate sources of high CO2 emissions. The drones' interactions mimic the principles of flocking and demonstrate the potential for building smarter systems by combining the specific skills of the individual drones with human intelligence.
Challenges and future directions
One challenge faced in the project is the need for real-time feedback and seamless coordination between the drones and humans. The complexity of the field environment, including factors like wind and equipment failures, adds further complexity to the system. Future steps involve testing more autonomous algorithms, determining when the drones should work independently or concentrate on specific tasks, and designing a turnkey system that allows for seamless coordination between the drones and human scientists. The ultimate aim is to create an integrated system where drones serve as an extension of human intelligence, aiding in the collection of valuable data about volcanic emissions.
Today, we're going to return to the idea of taking concepts from complexity science and applying them to situations in the real world.
In this episode, we're joined again by Melanie Moses, Professor of Computer Science at the University of New Mexico, and External Faculty at the Santa Fe Institute. She's going to share with us about her recent trip to Iceland to study active volcanoes. More specifically, Melanie is going to explain how you can program a swarm of drones to fly in formation and map the CO2 plume of a volcano.