HomeRoboticsAn interview with Nicolai Ommer: the RoboCupSoccer Small Dimension League

An interview with Nicolai Ommer: the RoboCupSoccer Small Dimension League


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 cutting-edge of clever robots, AI and automation. The annual RoboCup occasion is because of happen from 15-21 July in Salvador, Brazil. The Soccer element of RoboCup contains quite a few Leagues, with one in every of these being the Small Dimension League (SSL). We caught up with Government Committee member Nicolai Ommer to search out out extra in regards to the SSL, how the auto referees work, and the way groups use AI.

Might begin by giving us a fast introduction to the Small Dimension League?

Within the Small Dimension League (SSL) we’ve got 11 robots per workforce – the one bodily RoboCup soccer league to have the complete variety of gamers. The robots are small, cylindrical robots on wheels they usually can transfer in any route. They’re self-built by the groups, so groups need to do each the {hardware} and the programming, and quite a lot of issues need to work collectively to make a workforce work. The AI is central. We don’t have brokers, so groups have a central pc on the subject the place they’ll 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 now have a central imaginative and prescient system – that is maintained by the League, and has been since 2010. There are cameras above the sector 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 will change route in a short time, and the ball could 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 cut back it and put extra give attention to passing. This offers the keeper and the defenders an opportunity to truly intercept a kick.

It’s so quick that for people it’s fairly obscure all of the issues which might be happening. And that’s why, some years in the past, we launched auto refs, which assist lots in monitoring, particularly issues like collisions and so forth, the place the human referee can’t watch all the pieces 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 impartial methods implement the identical guidelines after which we do a majority vote on the choices.

To do that we wanted a center element, so some years in the past I began this venture to have a brand new sport controller. That is the consumer interface (UI) for the human referee who sits at a pc. Within the UI you see the present sport state, you’ll be able to manipulate the sport state, and this element coordinates the auto refs. The auto refs can join and report fouls. If just one auto ref detects the foul, it received’t rely 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 choice.

We managed to determine two implementations. The goal 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 completely different groups who’re nonetheless energetic.

How do the auto refs cope with collisions?

We will detect collisions from the information. Nevertheless, even for human referees it’s fairly arduous 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 rate – under 1.5m/s it’s not a collision, above 1.5m/s it’s. There’s additionally one other issue, regarding the angle calculation, that we additionally take note of 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 regarding the adherence to regular sport process. For instance, when the opposite workforce 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 sport occasions. For instance, when the ball leaves the sector. That’s the commonest occasion. This one is definitely not really easy to detect, significantly if there’s a chip kick (the place the ball leaves the taking part in floor). With the digital camera lens, the parabola of the ball could make it seem like it’s outdoors the sector of play when it isn’t. You want a strong filter to cope with this.

Additionally, when the auto refs detect a objective, we don’t belief them utterly. 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 verify all of the accessible information earlier than awarding the objective.

You’ve been concerned within the League for quite a few years. How has the League and the efficiency of the robots advanced 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 abilities carried out by robots with a ball. For instance, some years in the past, one workforce (ZJUNlict) developed robots that would 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 cross to trick the defenders. Our workforce, TIGERs Mannheim, has additionally improved on this space now. However it’s actually troublesome to do that and requires quite a lot of tuning. It actually will depend on the sector, the carpet, which isn’t standardized. So there’s a bit of little bit of luck that your particularly constructed {hardware} is definitely performing effectively on the competitors carpet.

The Small Dimension League Grand Remaining at RoboCup 2024 in Eindhoven, Netherlands. TIGERs Mannheim vs. ZJUNlict. Video credit score: TIGERs Mannheim. You’ll find the TIGERs’ YouTube channel right here.

What are a few of the challenges within the League?

One huge problem, and likewise perhaps it’s an excellent factor for the League, is that we’ve got quite a lot of undergraduate college students within the groups. These college students have a tendency to go away the groups after their Bachelor’s or Grasp’s diploma, the workforce members all change fairly frequently, and that signifies that it’s troublesome to retain data within the groups. It’s a problem to maintain the efficiency of the workforce; it’s even arduous 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 study lots from the expertise.

We’re constantly engaged on figuring out issues which we are able to make accessible 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 achieved 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 study, with the ability to do all of the issues themselves in the event that they need to.

You really want to have a workforce of individuals from completely different areas – mechanical engineering, electronics, venture administration. You additionally need to get sponsors, and you must promote your venture, get college students in your workforce.

Might you speak about a few of the AI components to the League?

Most of our software program is script-based, however we apply machine studying for small, delicate issues.

In my workforce, for instance, we do mannequin calibration with fairly easy algorithms. We now have a particular mannequin for the chip kick, and one other for the robotic. The wheel friction is kind of sophisticated, so we give you a mannequin after which we gather the information and use machine studying to detect the parameters.

For the precise match technique, one good instance is from the workforce CMDragons. One 12 months you would actually observe that they’d skilled their mannequin in order that, as soon as they scored objective, they upvoted the technique that they utilized earlier than that. You could possibly actually see that the opponent reacted the identical means on a regular basis. They had been capable of rating a number of targets, utilizing the identical technique repeatedly, as a result of they realized that if one technique labored, they might use it once more.

For our workforce, the TIGERs, our software program could be very a lot based mostly on calculating scores for a way good a cross is, how effectively can a cross be intercepted, and the way we are able to enhance the state of affairs with a specific cross. That is hard-coded generally, with some geometry-based calculations, however there’s additionally some fine-tuning. If we rating a objective then we observe again and see the place the cross got here from and we give bonuses on a few of the rating calculations. It’s extra sophisticated than this, after all, however typically it’s what we attempt to do by studying throughout the sport.

Individuals typically ask why we don’t do extra with AI, and I feel the primary problem is that, in comparison with different use instances, we don’t have that a lot information. It’s arduous to get the information. In our case we’ve got actual {hardware} and we can’t simply do matches all day lengthy for days on finish – the robots would break, they usually have to be supervised. Throughout a contest, we solely have about 5 to seven matches in whole. In 2016, we began to document all of the video games with a machine-readable format. All of the positions are encoded, together with the referee choices, and all the pieces is in a log file which we publish centrally. I hope that with this rising quantity of information we are able to really apply some machine studying algorithms to see what earlier matches and former methods did, and perhaps get some insights.

What plans do you’ve gotten in your workforce, the TIGERs?

We now have really received the competitors for the final 4 years. We hope that there will likely be another groups who can problem us. Our defence has not likely been challenged so we’ve got a tough time discovering weaknesses. We really 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 all the pieces calibrated and dealing as exactly as we would like it. If some small element is just not 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 computerized information processing to find out the most effective parameters. In recent times we’ve labored lots on dribbling within the 1 vs 1 conditions. This has been a extremely huge 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 strong software program methods. He holds a B.Sc. in Utilized Pc Science and an M.Sc. in Autonomous Programs. Nicolai started his journey in robotics with Workforce 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. Captivated 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.




AIhub
is a non-profit devoted to connecting the AI neighborhood to the general public by offering free, high-quality data in AI.


AIhub
is a non-profit devoted to connecting the AI neighborhood to the general public by offering free, high-quality data in AI.



Lucy Smith
is Managing Editor for AIhub.

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