Multi Autonomous Ground-robotic International Challenge

The Multi Autonomous Ground-robotic International Challenge (MAGIC) is a 1.6 million dollar prize competition for autonomous mobile robots funded by TARDEC and the DSTO, the primary research organizations for Tank and Defense research in the United States and Australia respectively. The goal of the competition is to create multi-vehicle robotic teams that can execute an intelligence, surveillance and reconnaissance mission in a dynamic urban environment. The challenge required competitors to map a 500 m x 500 m challenge area in under 3.5 hours and to correctly locate, classify and recognise all simulated threats. The challenge event was conducted in Adelaide, Australia, during November 2010.

Initially 12 teams were selected for the competition in November 2009, of which 10 teams received funding. These included:

The first downselection trial required teams to map an indoor area and outdoor area, and to demonstrate distributing and handing over tasks between robots. During the first downselection trial, the top six teams were selected:

Before the finals were held, Chiba Team withdrew from the competition, leaving five competitors.

Ultimately the overall goal of fully autonomous operations without human intervention was not achieved, however, the Secretary for Defence stated "The competing vehicles demonstrated new advances in robotics technology, which are very promising for their potential deployment in combat zones where they can replace our troops in carrying out life-threatening tasks" and considered the competition a success.

The official results of the competition were:

The "Old Ram Shed Challenge" was a single-day competition held after the completion of MAGIC. It was smaller in scale, allowing all of the teams to demonstrate their systems during a single day. The University of Pennsylvania won this challenge, having found a greater number of the target objects than the other teams.

Key technology used by all teams was computer vision, sensor fusion, human-robot interaction, and simultaneous localization and mapping (SLAM).

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