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Kick-off in a Small Dimension League match. Picture credit score: Nicolai Ommer.
RoboCup is a world scientific initiative with the objective of advancing the state-of-the-art of clever robots, AI and automation. The annual RoboCup occasion is because of happen from 15-21 July in Salvador, Brazil. The Soccer part of RoboCup contains numerous Leagues, with one among these being the Small Dimension League (SSL). We caught up with Government Committee member Nicolai Ommer to seek out out extra in regards to the SSL, how the auto referees work, and the way groups use AI.
May begin by giving us a fast introduction to the Small Dimension League?
Within the Small Dimension League (SSL) we’ve 11 robots per staff – the one bodily RoboCup soccer league to have the total variety of gamers. The robots are small, cylindrical robots on wheels and so they can transfer in any path. They’re self-built by the groups, so groups need to do each the {hardware} and the programming, and a number of issues need to work collectively to make a staff work. The AI is central. We don’t have brokers, so groups have a central pc on the subject the place they will do all of the computation after which they ship the instructions to the robots in several abstractions. Some groups will simply ship velocity instructions, different groups ship a goal.
We have now a central imaginative and prescient system – that is maintained by the League, and has been since 2010. There are cameras above the sphere to trace all of the robots and the ball, so everybody is aware of the place the robots are.
The robots can transfer as much as 4 meters per second (m/s), after this level it will get fairly unstable for the robots. They’ll change path in a short time, and the ball might be kicked at 6.5 m/s. It’s fairly quick and we’ve already needed to restrict the kick velocity. Beforehand we had a restrict of 8 m/s and earlier than that 10m/s. Nevertheless, no robotic can catch a ball with this velocity, so we determined to scale back it and put extra give attention to passing. This offers the keeper and the defenders an opportunity to really intercept a kick.
It’s so quick that for people it’s fairly obscure all of the issues which are occurring. And that’s why, some years in the past, we launched auto refs, which assist quite a bit in monitoring, particularly issues like collisions and so forth, the place the human referee can’t watch the whole lot on the identical time.
How do the auto refs work then, and is there a couple of working on the identical time?
After we developed the present system, to maintain issues truthful, we determined to have a number of implementations of an auto ref system. These unbiased methods implement the identical guidelines after which we do a majority vote on the selections.
To do that we would have liked a center part, so some years in the past I began this venture to have a brand new recreation controller. That is the person interface (UI) for the human referee who sits at a pc. Within the UI you see the present recreation state, you possibly can manipulate the sport state, and this part coordinates the auto refs. The auto refs can join and report fouls. If just one auto ref detects the foul, it received’t depend it. However, if each auto refs report the foul throughout the time window, then it’s counted. A part of the problem was to make this all visible for the operator to grasp. The human referee has the final phrase and makes the ultimate resolution.
We managed to determine two implementations. The intention was to have three implementations, which makes it simpler to type a majority. Nevertheless, it nonetheless works with simply two implementations and we’ve had this for a number of years now. The implementations are from two totally different groups who’re nonetheless lively.
How do the auto refs cope with collisions?
We will detect collisions from the info. Nevertheless, even for human referees it’s fairly onerous to find out who was at fault when two robots collide. So we needed to simply outline a rule, and all of the implementations of the auto ref implement the identical rule. We wrote within the rulebook actually particularly the way you calculate if a collision occurred and who was at fault. The primary consideration relies on the speed – under 1.5m/s it’s not a collision, above 1.5m/s it’s. There’s additionally one other issue, referring to the angle calculation, that we additionally consider to find out which robotic was at fault.
What else do the auto refs detect?
Different fouls embrace the kick velocity, after which there’s fouls referring to the adherence to regular recreation process. For instance, when the opposite staff has a free kick, then the opposing robots ought to preserve a sure distance from the ball.
The auto refs additionally observe non-fouls, in different phrases recreation occasions. For instance, when the ball leaves the sphere. That’s the most typical occasion. This one is definitely not really easy to detect, notably if there’s a chip kick (the place the ball leaves the enjoying floor). With the digital camera lens, the parabola of the ball could make it appear like it’s exterior the sphere of play when it isn’t. You want a sturdy filter to cope with this.
Additionally, when the auto refs detect a objective, we don’t belief them fully. When a objective is detected, we name it a “doable objective”. The match is halted instantly, all of the robots cease, and the human referee can test all of the obtainable information earlier than awarding the objective.
You’ve been concerned within the League for numerous years. How has the League and the efficiency of the robots developed over that point?
My first RoboCup was in 2012. The introduction of the auto refs has made the play much more fluent. Earlier than this, we additionally launched the idea of ball placement, so the robots would place the ball themselves for a free kick, or kick off, for instance.
From the {hardware} aspect, the primary enchancment lately has been dribbling the ball in one-on-one conditions. There has additionally been an enchancment within the specialised expertise carried out by robots with a ball. For instance, some years in the past, one staff (ZJUNlict) developed robots that might pull the ball backwards with them, transfer round defenders after which shoot on the objective. This was an sudden motion, which we hadn’t seen earlier than. Earlier than this you needed to do a go to trick the defenders. Our staff, TIGERs Mannheim, has additionally improved on this space now. However it’s actually tough to do that and requires a number of tuning. It actually will depend on the sphere, the carpet, which isn’t standardized. So there’s somewhat little bit of luck that your particularly constructed {hardware} is definitely performing effectively on the competitors carpet.
The Small Dimension League Grand Closing at RoboCup 2024 in Eindhoven, Netherlands. TIGERs Mannheim vs. ZJUNlict. Video credit score: TIGERs Mannheim. You could find the TIGERs’ YouTube channel right here.
What are among the challenges within the League?
One massive problem, and likewise perhaps it’s an excellent factor for the League, is that we’ve a number of undergraduate college students within the groups. These college students have a tendency to depart the groups after their Bachelor’s or Grasp’s diploma, the staff members all change fairly usually, and that implies that it’s tough to retain data within the groups. It’s a problem to maintain the efficiency of the staff; it’s even onerous to breed what earlier members achieved. That’s why we don’t have giant steps ahead, as a result of groups need to repeat the identical issues when new members be a part of. Nevertheless, it’s good for the scholars as a result of they actually be taught quite a bit from the expertise.
We’re repeatedly engaged on figuring out issues which we are able to make obtainable for everybody. In 2010 the imaginative and prescient system was established. It was an enormous issue, that means that groups didn’t need to do pc imaginative and prescient. And we’re at the moment taking a look at establishing requirements for wi-fi communication – that is at the moment carried out by everybody on their very own. We need to advance the League, however on the identical time, we additionally need to have this nature of with the ability to be taught, with the ability to do all of the issues themselves in the event that they need to.
You really want to have a staff of individuals from totally different areas – mechanical engineering, electronics, venture administration. You additionally need to get sponsors, and you must promote your venture, get college students in your staff.
May you speak about among the AI parts to the League?
Most of our software program is script-based, however we apply machine studying for small, delicate issues.
In my staff, for instance, we do mannequin calibration with fairly easy algorithms. We have now a selected mannequin for the chip kick, and one other for the robotic. The wheel friction is kind of sophisticated, so we provide you with a mannequin after which we gather the info and use machine studying to detect the parameters.
For the precise match technique, one good instance is from the staff CMDragons. One yr you may actually observe that that they had educated their mannequin in order that, as soon as they scored objective, they upvoted the technique that they utilized earlier than that. You may actually see that the opponent reacted the identical method on a regular basis. They have been in a position to rating a number of objectives, utilizing the identical technique time and again, as a result of they realized that if one technique labored, they might use it once more.
For our staff, the TIGERs, our software program may be very a lot based mostly on calculating scores for a way good a go is, how effectively can a go be intercepted, and the way we are able to enhance the scenario with a specific go. That is hard-coded typically, with some geometry-based calculations, however there’s additionally some fine-tuning. If we rating a objective then we monitor again and see the place the go got here from and we give bonuses on among the rating calculations. It’s extra sophisticated than this, after all, however normally it’s what we attempt to do by studying throughout the recreation.
Folks typically ask why we don’t do extra with AI, and I believe the primary problem is that, in comparison with different use circumstances, we don’t have that a lot information. It’s onerous to get the info. In our case we’ve actual {hardware} and we can’t simply do matches all day lengthy for days on finish – the robots would break, and so they must be supervised. Throughout a contest, we solely have about 5 to seven matches in complete. In 2016, we began to report all of the video games with a machine-readable format. All of the positions are encoded, together with the referee choices, and the whole lot is in a log file which we publish centrally. I hope that with this rising quantity of knowledge we are able to truly apply some machine studying algorithms to see what earlier matches and former methods did, and perhaps get some insights.
What plans do you might have to your staff, the TIGERs?
We have now truly received the competitors for the final 4 years. We hope that there can be another groups who can problem us. Our defence has not likely been challenged so we’ve a tough time discovering weaknesses. We truly play towards ourselves in simulation.
One factor that we need to enhance on is precision as a result of there’s nonetheless some guide work to get the whole lot calibrated and dealing as exactly as we would like it. If some small element just isn’t working, for instance the dribbling, then it dangers the entire event. So we’re engaged on making all these calibration processes simpler, and to do extra automated information processing to find out one of the best parameters. Lately we’ve labored quite a bit on dribbling within the 1 vs 1 conditions. This has been a extremely massive enchancment for us and we’re nonetheless engaged on that.
About Nicolai
Nicolai Ommer is a Software program Engineer and Architect at QAware in Munich, specializing in designing and constructing sturdy software program methods. He holds a B.Sc. in Utilized Laptop Science and an M.Sc. in Autonomous Methods. Nicolai started his journey in robotics with Group TIGERs Mannheim, collaborating in his first RoboCup in 2012. His dedication led him to hitch the RoboCup Small Dimension League Technical Committee and, in 2023, the Government Committee. Obsessed with innovation and collaboration, Nicolai combines tutorial perception with sensible expertise to push the boundaries of clever methods and contribute to the worldwide robotics and software program engineering communities.
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