Kick-off in a Small Dimension League match. Picture credit score: Nicolai Ommer.
RoboCup is a world scientific initiative with the aim 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 element of RoboCup contains quite a lot of Leagues, with certainly one of these being the Small Dimension League (SSL). We caught up with Government Committee member Nicolai Ommer to seek out out extra concerning 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 have now 11 robots per group – 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 should do each the {hardware} and the programming, and a whole lot of issues should work collectively to make a group work. The AI is central. We don’t have brokers, so groups have a central laptop on the discipline the place they will do all of the computation after which they ship the instructions to the robots in numerous abstractions. Some groups will simply ship velocity instructions, different groups ship a goal.
We’ve 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’ll change route in a short time, and the ball may 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 concentrate on passing. This provides 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 might be occurring. And that’s why, some years in the past, we launched auto refs, which assist loads in monitoring, particularly issues like collisions and so forth, the place the human referee can’t watch every part on the similar time.
How do the auto refs work then, and is there multiple working on the similar time?
After we developed the present system, to maintain issues honest, 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 selections.
To do that we wanted a center element, so some years in the past I began this mission to have a brand new sport controller. That is the person interface (UI) for the human referee who sits at a pc. Within the UI you see the present sport state, you may 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 gained’t rely it. However, if each auto refs report the foul inside 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 determination.
We managed to ascertain 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 completely different groups who’re nonetheless lively.
How do the auto refs take care of collisions?
We are able to detect collisions from the information. Nevertheless, even for human referees it’s fairly exhausting 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 – beneath 1.5m/s it’s not a collision, above 1.5m/s it’s. There may be additionally one other issue, regarding the angle calculation, that we additionally keep in mind to find out which robotic was at fault.
What else do the auto refs detect?
Different fouls embody the kick velocity, after which there’s fouls regarding the adherence to regular sport process. For instance, when the opposite group 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 most typical occasion. This one is definitely not really easy to detect, significantly 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 outdoors the sector of play when it isn’t. You want a strong filter to take care of this.
Additionally, when the auto refs detect a aim, we don’t belief them utterly. When a aim is detected, we name it a “doable aim”. The match is halted instantly, all of the robots cease, and the human referee can test all of the obtainable knowledge earlier than awarding the aim.
You’ve been concerned within the League for quite a lot of 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 abilities carried out by robots with a ball. For instance, some years in the past, one group (ZJUNlict) developed robots that might pull the ball backwards with them, transfer round defenders after which shoot on the aim. This was an surprising motion, which we hadn’t seen earlier than. Earlier than this you needed to do a move to trick the defenders. Our group, TIGERs Mannheim, has additionally improved on this space now. However it’s actually troublesome to do that and requires a whole lot of tuning. It actually will depend on the sector, the carpet, which isn’t standardized. So there’s slightly little bit of luck that your particularly constructed {hardware} is definitely performing nicely 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 could find the TIGERs’ YouTube channel right here.
What are a few of the challenges within the League?
One huge problem, and in addition possibly it’s an excellent factor for the League, is that we have now a whole lot 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 group members all change fairly recurrently, and that signifies that it’s troublesome to retain data within the groups. It’s a problem to maintain the efficiency of the group; it’s even exhausting to breed what earlier members achieved. That’s why we don’t have giant steps ahead, as a result of groups should 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 loads from the expertise.
We’re constantly engaged on figuring out issues which we will make obtainable for everybody. In 2010 the imaginative and prescient system was established. It was an enormous issue, which means that groups didn’t should do laptop imaginative and prescient. And we’re at present establishing requirements for wi-fi communication – that is at present performed by everybody on their very own. We need to advance the League, however on the similar 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 group of individuals from completely different areas – mechanical engineering, electronics, mission administration. You additionally should get sponsors, and you must promote your mission, get college students in your group.
Might you discuss a few of the AI parts to the League?
Most of our software program is script-based, however we apply machine studying for small, refined issues.
In my group, for instance, we do mannequin calibration with fairly easy algorithms. We’ve a selected mannequin for the chip kick, and one other for the robotic. The wheel friction is sort of difficult, so we provide you with a mannequin after which we accumulate the information and use machine studying to detect the parameters.
For the precise match technique, one good instance is from the group CMDragons. One yr you may actually observe that they’d educated their mannequin in order that, as soon as they scored aim, they upvoted the technique that they utilized earlier than that. You would actually see that the opponent reacted the identical approach on a regular basis. They had 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 group, the TIGERs, our software program could be very a lot primarily based on calculating scores for a way good a move is, how nicely can a move be intercepted, and the way we will enhance the scenario with a specific move. That is hard-coded typically, with some geometry-based calculations, however there may be additionally some fine-tuning. If we rating a aim then we observe again and see the place the move got here from and we give bonuses on a few of the rating calculations. It’s extra difficult than this, in fact, however basically it’s what we attempt to do by studying throughout the sport.
Folks usually ask why we don’t do extra with AI, and I feel the primary problem is that, in comparison with different use circumstances, we don’t have that a lot knowledge. It’s exhausting to get the information. In our case we have now actual {hardware} and we can not simply do matches all day lengthy for days on finish – the robots would break, they usually must be supervised. Throughout a contest, we solely have about 5 to seven matches in complete. In 2016, we began to file all of the video games with a machine-readable format. All of the positions are encoded, together with the referee choices, and every part is in a log file which we publish centrally. I hope that with this rising quantity of knowledge we will really apply some machine studying algorithms to see what earlier matches and former methods did, and possibly get some insights.
What plans do you could have on your group, the TIGERs?
We’ve really gained the competitors for the final 4 years. We hope that there will likely be another groups who can problem us. Our defence has probably not been challenged so we have now a tough time discovering weaknesses. We really play in opposition to ourselves in simulation.
One factor that we need to enhance on is precision as a result of there may be nonetheless some handbook work to get every part calibrated and dealing as exactly as we would like it. If some small element will not be 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 knowledge processing to find out one of the best parameters. In recent times we’ve labored loads on dribbling within the 1 vs 1 conditions. This has been a very 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 Techniques. Nicolai started his journey in robotics with Crew TIGERs Mannheim, taking part in his first RoboCup in 2012. His dedication led him to affix the RoboCup Small Dimension League Technical Committee and, in 2023, the Government Committee. Keen about 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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