29th June 2019 marks the first time MSP went into space. ACRUX-1 was the effort of a dedicated multi-disciplinary team and one of the first completely student-led satellite development and launch initiatives in Australia. After three years in the making, ACRUX-1 was launched into orbit on Rocket Lab’s Make It Rain mission on their rocket, Electron. The CubeSat powered up and established two-way communication to our ground station back on Earth - mission success!
ACRUX-1 was not only a technical endeavour, but an educational one too. By providing unique and complex opportunities such as ACRUX-1, MSP is advancing our overarching goal of developing students into pioneers and changemakers.
AUTONOMOUS DEORBIT SYSTEM
From natural meteoroids to defunct satellites, there are thousands and thousands of pieces of space debris orbiting the Earth. At the speeds they travel, a collision with even the smallest piece of space junk can cause serious damage to a spacecraft or destroy functioning satellites that we use every day. Space debris is an ever-growing issue that highlights the importance of the responsible use of space.
The ACRUX-2 mission is to develop an autonomous deorbit system. Our student-based team will design, build and launch a 3U CubeSat that utilises the electro-conductive tether system from Saber Astronautics - DragEN. The aim is to demonstrate the effect of the deorbiting tether on the satellite, and to correlate tether performance to ionosphere location. Experimentation in the area of cleaning up space debris is still relatively uncharted, so a successful mission will mean we can contribute to the understanding of the effectiveness of a tether mechanism for future missions.
AI & ROBOTICS
The University Rover Challenge (URC) is an annual student competition that takes place at the Mars Desert Research Station (MDRS) in Utah, USA, to design and build a rover that would be of use to early explorers on Mars. This competition is a prestigious podium to showcase individual country’s capabilities by manufacturing a state-of-the-state rover to meet the competition regulations as well as to participate in the rigorous challenges.
The team at MSP, consisting of 8 members of the AI and Robotics lab, is exclusively involved in building the software suite required for Rover. This involves autonomous path navigation, object detection, processing of hardware data & sensor suite etc.
After seeing the devastating effects of the 2019-20 Australian bushfires, MSP decided to look into ways we could create something that could have a large impact on bushfire season. The team found that there’s a lot of research into reactive solutions such as real-time fire detection, but saw a gap in the field of proactive responses to bushfires. The aim for this project is to use predictive modelling to identify potential high-risk locations and anticipate how a fire could behave if ignited in such an area. Our team is developing a machine learning based model that uses data from satellite imagery and elevation maps, assesses the temperature in the area, and looks at historical data in order to establish patterns. This information will help calculate the risk-level of a fire and determine what level of response is needed. A tool like this could prove invaluable when approaching firefighting, and could be an important inclusion in the discussion around fire management.
MSP was excited to participate in SmartSat CRC’s Ideation Challenge - Firefly! The challenge? To rapidly conceive a payload for natural disaster preparation, response or recovery and demonstrate on a stratospheric balloon. Due to the nature of the challenge, participants were given a timeframe of less than 2 months to complete this project. MSP’s proposed solution seeks to provide a real-time service to predict the extent to which communication will be impeded in areas impacted by natural disaster. Specifically focusing on bushfires, the team found that the research in the effects of smoke attenuation did not translate into many real-world applications. To address this, the aim is to build a machine learning (ML) model that predicts communication blackout areas caused by bush-fire smoke using cell tower location and real-time multispectral imaging.