CYCLE TIME ANALYSIS
As promised in the last post, this one is going to be all about cycle time analysis. First we will explain the calculations of the autonomous cycles and then the tele-op cycles.
In order to analyse the cycle times, first the cycle paths are determined and some basic parameters are determined. The following parameters are estimated before analysis:
- Robot speed: 3,5 m/s which is 11,5 ft/s
- Cargo collect time: 1 s / per cargo
- Cargo score time 1 s/ per cargo
To illustrate a robot position on the field we’ve used red numbered boxes.
Autonomous Cycles
The layout of the field indicates that there are 5 cycles that can be chosen. Each of these will be shown below.
1 Cargo - Auto Cycle
The first possibility is the 1 Cargo autonomous cycle. This option only scores 6 points (if Cargo is scored in the upper hub).
| Action |
Time |
|
| Score 1 Cargo in upper hub |
1.00 |
s |
| TAXI out of TARMAC |
0.85 |
s |
| Total time |
1.85 |
s |
| Points |
6.00 |
p |
| Points per time |
0.40 |
p/s |
2 Cargo - Auto Cycle
The 2 Cargo auto can score 6, 8 or 10 points depending on how many cargo is scored in the lower or upper hub.
This auto is a valid option if the robot is not positioned on the right side of the alliance station. The reason is the amount of Cargo that is located on that side of the field which creates potential for more.
| Action |
Time |
|
| Score 1 Cargo in upper hub |
1.00 |
s |
| TAXI out of TARMAC |
0.85 |
s |
| Collect CARGO |
1.00 |
s |
| Move to scoring position |
0.85 |
s |
| Score 1 CARGO in upper hub |
1.00 |
s |
| Total time |
3.69 |
s |
| Points |
10.00 |
p |
| Points per time |
0.67 |
p/s |
This version of the 2 Cargo auto takes a bit less time due to the reduced driving location. This is the result of a different starting position.
| Action |
Time |
|
| TAXI out of TARMAC |
0.26 |
s |
| Collect CARGO |
1.00 |
s |
| Move to scoring position |
0.85 |
s |
| Score 2 CARGO in upper hub |
2.00 |
s |
| Total time |
4.11 |
s |
| Points |
10.00 |
p |
| Points per time |
0.67 |
p/s |
3 Cargo - Auto Cycle
The 3 Cargo cycle could be executed in multiple ways and combinations. The result will be expected to look as shown below.
This auto scores between 14, 12, 10 or 8 points depending on whether Cargo is scored in the lower or upper hub.
| Action |
Time |
|
| TAXI out of TARMAC |
0.26 |
s |
| Collect CARGO |
1.00 |
s |
| Move to scoring position |
0.85 |
s |
| Score 2 CARGO in upper hub |
2.00 |
s |
| Drive to CARGO |
0.85 |
s |
| Collect CARGO |
1.00 |
s |
| Drive to scoring position |
0.85 |
s |
| Score 1 CARGO in upper hub |
1.00 |
s |
| Total time |
7.80 |
s |
| Points |
14.00 |
p |
| Points per time |
0.93 |
p/s |
4 Cargo - Auto Cycle
The 4 Cargo auto is the ultimate autonomous cycle. It scores the most points and it has potential to complete the QUINTET.
The Quintet allows an alliance to gain a ranking point after only 18 scored cargo instead of having to score 20 cargo. This means either a 1 or 2 cycles advantage. The 4 cargo auto is strong because it allows 1 team to complete this Quintet. This is done when the human player scores their cargo shot.
| Action |
Time |
|
| TAXI out of TARMAC |
0.26 |
s |
| Collect CARGO |
1.00 |
s |
| Move to scoring position |
0.85 |
s |
| Score 2 CARGO in upper hub |
2.00 |
s |
| Drive to CARGO |
0.85 |
s |
| Collect CARGO |
1.00 |
s |
| Drive to CARGO |
1.37 |
s |
| Collect CARGO |
1.00 |
s |
| Drive to scoring position |
2.22 |
s |
| Score 2 CARGO in upper hub |
2.00 |
s |
| Total time |
12.54 |
s |
| Points |
18.00 |
p |
| Points per time |
1.20 |
p/s |
5 Cargo - Auto Cycle
Having all the faith in the world in human players our variation of the 5 cargo cycle will be performing a 4 cargo cycle and having the human player yeet a cargo into the high goal.
Another variation of the 5 cargo auto is possible which is outlined below.
Being 1678.
TELE OP CYCLES
In this game the tele-op cycles aren’t easily predicted. Ths has the following reasons:
-
The Cargo is located all over the field. It’s not stacked in 1 place. There are no discrete (human player) spots in the field where cargo can be entered into the game.
-
The cargo is a bounce / rolly game piece and is easily moved by touching.
-
After being scored, cargo is ejected back onto the field by the scoring hub. The cargo can be released on four different sides and on each side there are two exit sides. Cargo will therefore be placed all over the field after the first several cycles.
-
Due to the nature of the game, the cargo will probably be all over the field after autonomous due to there not being a specific stack to collect.
Basic Tele-op Cycles
The first basic tele-op cycle is the fastest one as well. This cycle assumes there is still cargo located in the cargo ring on your team’s alliance side of the field. As is shown, this cycle is fast. The only downside is the limited availability.
Even after auto it is questionable whether this cycle is still available. Therefore it is expected that this cycle will only be possible if the autonomous period is not executed as planned.
| Action |
Time |
| Drive to CARGO position (1) |
0.85 |
| Collect CARGO |
1.00 |
| Drive to scoring position |
0.85 |
| Score CARGO in upper hub |
1.00 |
| Totaal |
3.69 |
| Points |
2.00 |
| Points per second |
0.54 |
The terminal cycle is shown here. This cycle is necessary if the robot does not have a solid way to collect Cargo from the ground. The obvious disadvantage is the distance a robot has to drive for this cycle. This results in a longer duration of the cycle.
Another fact that works against this cycle is that the cargo will bounce towards the center of the field where the hub is located. Driving all the way to the terminal is therefore a waste of time when a robot could be doing other things while a cargo ball being dropped in the terminal is bouncing towards the hub for an easy score later on.
| Action |
Time |
| Drive to TERMINAL |
2.22 |
| Collect CARGO |
1.00 |
| Drive to scoring position |
2.22 |
| Score CArGO in higher Hub |
1.00 |
| Total |
6.43 |
| Points |
2.00 |
| Points per second |
0.31 |
Match simulation
xRC ball location analysis
The heat map is shown in the picture below. This heat map is created by educated assumptions and gameplay experience in the XRC game. This game is a simulation of the FIRST Rapid React game. The results have shown that Cargo after being scored will roll or bounce towards the edges of the field. This indicates that the areas shown in yellow will and orange will be the destinations of Cargo that are ejected by the hub. The orange zones are believed to be key zones to collect cargo.
These are the estimated times it takes to travel back and forth between scoring position and each of the 8 marked zones in the figure above. These are estimations:
- 3 seconds
- 5 seconds
- 6 seconds
- 10 seconds
- 5 seconds
- 4 seconds
- 4 seconds
- 8 seconds
One of the aspects that is introduced to this game is the random eject pattern by the hub. This means that it isn’t possible to make educated guesses about where the cargo will end up exactly.
It can be said however that all 4 exits have a chance to eject cargo in a nearby orange zone (1, 5 and 7). The only orange zone that is a bit out of reach would be zone 3. This zone can be reached by 2 out of 8 exit ways. This however is far-fetched because the interaction between the cargo and the hub exit is not consistent.
Cargo availability
This year there are only 11 game pieces per alliance. With 3 robots attempting to score those and the field only returning the cargo after 4 to 7 seconds one of the things we worried about was if there would end up being too few cargo on the field to get efficient cycle times.
So we set out to find if that was true by writing a python script that simulates matches. First thing we needed to do was to decide which variables would impact cargo availability. Of course the return time to the field is set to 4-7 seconds, but the main two variables that impact how many pieces of cargo on the field are the average time that it takes for a robot to collect cargo and the average time that it takes a robot to drive to their shooting position and shoot.
Simulating 10000s of match time per combination of time that it takes to shoot and collect gave us the heat map that can be seen below. We’re pretty happy to be able to do this with software, looking at that much match time (with the 30s cut of already heavily reducing the total amount) would take us 9.000.000 seconds or which is roughly 104 days, meaning that we would have been just on time for the einstein finals… provided that we came up with it on kickoff and not a week into build season.
What we conclude from this graph is the following: unless it takes your entire alliance insanely low time to collect balls you’re unlikely to get into real trouble with cargo availability, we project during general matches there will be about 7-8 pieces of cargo available to pick up and score.
Strategy analysis
It’s a bit early to start thinking about exactly how we would like our alliances to play matches, but the mind goes where it wants and we have been considering a couple of basic strategies. One being the best robot on the alliance is responsible for the half of the field furthest away from the alliance stations, and the other two both having a quarter on the side closest to the alliance stations.
We thought about this strategy for a bit and came to the conclusion that it might be too rigid. What if one half of the field outperforms the other side, won’t they have too few pieces of cargo soon? So once again we turned our eyes to simulation and we decided that the two variables that impacted this were cycle time of one half of the field and the ratio of both halfs combined cycle time.
I.e. if the best robot has a cycle time of 10 seconds for scoring 2 pieces of cargo, and the ratio is 2 the two robots on the close by half need to score 4 pieces of cargo in that time frame. Inputting this in python we ran scenarios for cycle times ranging up to 60 seconds and ratios from exactly the same cycle time to 3 times faster.
This gave us the graph below, if a field is dark blue it means you could last the entire teleop before it becomes a problem, everything not dark blue may mean you’re getting trouble at some point during the match and you would need to adjust your strategy on the fly. To us this means the strategy is unlikely to be rigid, it might work during the qualifiers, but during the playoffs you will have to be able to adjust to the flow of a match and it might not hurt to have one robot play some defense.
For those of you who are interested in taking a closer look at both graphs you can find them as html files in this drive map: https://tinyurl.com/2p9aza5s .
Conclusion
After two blog posts we know a bit more about what to expect of the game, we know that we don’t know everything but still a little bit of data is better than no data.
Of course eventually we will see things happen on the field that we could never have imagined and those things will inevitably bite us in the end, but for now we’re happy.
The next post will be the conclusion to our data project, which cycles are we going to use to get to the holy number of 44.7?!
Congrats for this.
