engineering

Multi Autonomous Ground-robotic International Challenge

Multi Autonomous Ground-robotic International Challenge is a engineering topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Multi Autonomous Ground-robotic International Challenge rather than just read about it. In short: 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…

Key takeaways

Multi Autonomous Ground-robotic International Challenge belongs to engineering; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Multi Autonomous Ground-robotic International Challenge to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Multi Autonomous Ground-robotic International Challenge from memory before moving on to harder problems.

Reference excerpt

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.

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

MAGICian – Adelaide/Perth, Australia (UWA, ECU, Flinders, Thales) Strategic Engineering – Adelaide, Australia (U. Adelaide) Northern Hunters – Canada (Royal Military College of Canada) Chiba Team – Japan (Chiba University) Cappadocia – Ankara, Turkey (ASELSAN, Ohio State University) RASR – Gaithersburg, Md. (Robotics Research, LLC; QinetiQ; Embry-Riddle Aeronautical University) Team Cornell – US (Cornell University) Team Michigan – Ann Arbor, Mich. (University of Michigan) Virginia Tech – US (Virginia Tech) University of Pennsylvania – Philadelphia (University of Pennsylvania) Numinence – Brisbane, Australia (Numinence Pty Ltd, La Trobe University) UNSW – Sydney, Australia (UNSW) 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:

Cappadocia – Ankara, Turkey MAGICian – Adelaide/Perth, Australia RASR – Gaithersburg, Md. Team Michigan – Ann Arbor, Mich. University of Pennsylvania – Philadelphia Chiba Team – Japan Before the finals were held, Chiba Team withdrew from the competition, leaving five competitors.

Event 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.

Results The official results of the competition were:

First – Team Michigan ($750,000 prize) Second – University of Pennsylvania ($250,000 prize) Third – RASR ($100,000 prize) Fourth – MAGICian & Cappadocia 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.

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

Team Michigan, a collaboration between the University of Michigan's APRIL Lab and Soar Technology, Inc., had the largest fleet of 14 robots, developed their own Inertial Measurement Unit, and created their skid steer robot chassis out of Baltic birch plywood. Additionally, they had minimal reliance on GPS and used bandwidth limited 900 MHz radios for all telemetry, imaging, and status communications between all robots and the ground station. The code was written primarily in Java and each robot was equipped with an actuated 2D LIDAR, along with a unique 2D barcode for inter-robot recognition. The University of Pennsylvania team consisted of only four members. All code was written using Matlab. The robots were equipped with omnidirectional vision. RASR used the Foster-Miller TALON vehicle. MAGICian used the WAMbot robots developed by The University of Western Australia, Edith Cowan University and Thales Australia. Code was written in C++ and Java. The robots were equipped with SICK laser scanners. See the September/October 2012 special issue of the Journal of Field Robotics for contest highlights, technical approaches taken by several of the teams, and an explanation of the evaluation metrics used by organizers.

References

External links Australian science TV covers the MAGIC 2010 finals

Official site The home page of MAGIC 2010

Team sites The MAGICian WAMbot robot home page Team Michigan home page UPenn team (GRASP) news Team Cappadocia home page Chiba robotics lab

Illustrations

Worked examples

Example 1 — a first encounter with Multi Autonomous Ground-robotic International Challenge

Start with the simplest possible case. Write down what Multi Autonomous Ground-robotic International Challenge claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In engineering, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Multi Autonomous Ground-robotic International Challenge before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Multi Autonomous Ground-robotic International Challenge ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Multi Autonomous Ground-robotic International Challenge

In research: Multi Autonomous Ground-robotic International Challenge appears in engineering research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Multi Autonomous Ground-robotic International Challenge in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Multi Autonomous Ground-robotic International Challenge is common in secondary-school and first-year university syllabi. It links to neighbouring topics Artificial intelligence competitions, DARPA, Robotics competitions, so understanding it makes those chapters shorter.
In everyday life: Look for Multi Autonomous Ground-robotic International Challenge outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Multi Autonomous Ground-robotic International Challenge in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Multi Autonomous Ground-robotic International Challenge means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Multi Autonomous Ground-robotic International Challenge out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Multi Autonomous Ground-robotic International Challenge in simple terms?

Why does Multi Autonomous Ground-robotic International Challenge matter?

Because it connects several engineering ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Multi Autonomous Ground-robotic International Challenge?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Multi Autonomous Ground-robotic International Challenge.