Now that we have a tool to evaluate the optimal scoring of an FRC game we can add constraints to our rational agent (for example, the robot is only allowed to put CUBES on the SWITCH) and approximate the scoring potential of other strategies and confirm/deny our initial assumptions.
The optimal strategy we found in Part 1 had no constraints, the robot could do everything. We will consider this our “MAX” points to start. The evaluation from Part 1 determined the following scores:
- MAX Autonomous Mode: 45 pts
- MAX TELEOP Mode: 126 pts
- MAX End Game: 60 pts
- MAX TOTAL: 233 pts
What if we could place CUBES on the SWITCH or the VAULT?
Assuming a robot could only place CUBES on the SWITCH or in the VAULT (starting at the SWITCH in the end of :auto mode) we could model a match as follows (holding the same assumptions and cycle times from Part 1):
With this strategy, we collect all nine (9) CUBES (45pts) needed for each power up, we control our SWITCH for the entire 135s of TELEOP (1 + 135pts), then we park our robot on the PLATFORM (5pts).
Since we are only controlling the SWITCH, the timing of BOOST and FORCE do not make much difference because they both give us an additional 10pts.
Also, because we are parking on the PLATFORM but assuming we are playing the match “by ourselves” in an imagined scenario where both of the other robots are broken, we can still use LEVITATE to achieve the additional 30pts for one of the “other” robots.
Below are the scores for each mode of this strategy:
- LOW Autonomous Mode: 31 pts
- LOW TELEOP Mode: 201 pts
- LOW End Game: 35 pts
- MIN TOTAL: 267 pts
What if we could place CUBES on the SCALE or the VAULT?
Assuming a robot could only place CUBES on the SCALE or in the VAULT (starting at the SCALE in the end of :auto mode) we could model a match as follows:
With this strategy, we collect all nine (9) CUBES (45pts) needed for each power up, we control our SCALE for the entire 135s of TELEOP (1 + 135pts), then we HANG our robot on the RUNG (30pts).
Since we are only controlling the SCALE, the timing of BOOST and FORCE do not make much difference because they both give us an additional 10pts.
Also, the note about the END GAME from above applies here, so we get both the HANG and the LEVITATE points (30pts)..
Below are the scores for each mode of this strategy:
- HIGH Autonomous Mode: 27 pts
- HIGH TELEOP Mode: 201 pts
- HIGH End Game: 60 pts
- HIGH TOTAL: 288 pts
Decisions Decisions Decisions
Before we choose a strategy, we have to decide what we want to accomplish with our robot.
- Do we want to build a robot that might perform average in the qualifying matches, but will position us to be picked onto a strong Alliance for the Finals?
- Do we want to build a robot that will best position us to win all of our matches and be a top Alliance Captain?
- Do we have limited resources and want to build the simplest robot that can give us the highest impact in any given match?
That is up to each team to decide for themselves, as a group. Approach this question realistically, but don’t sell yourself short. Pick an attainable goal and stick to it. For the purposes of this article, we are going to choose to accomplish a goal in-between winning over 50% of our matches at any given event and winning a world championship: We are going to compete for a State Championship.
To accomplish this goal, our robot will need to qualify for the State Championship by winning a District Event. We will define this goal as our Robot Mission Statement. This term will be the basis of our next article, so tuck it in the back of your head for later.
Given our models, observations, and Robot Mission Statement; we are going to choose the strategy that allows us to have the largest individual impact on a qualifying match. This means we will evaluate designs for a robot that can most efficiently retrieve and deposit CUBES.
Now this is a very good stopping point for your first “brainstorm/strategy” meeting. Before students leave, we always give the most important homework assignment of the season: RESEARCH EXISTING TECHNOLOGIES!
Once you have gone over the game, ran through the game theory, and reasoned out an initial strategy it is time to step back and gather perspective on your assumptions and aspirations. A great way to do this is to see what other teams were able to accomplish in previous games with similar scoring objects and objectives. For example, this year’s game has tote like object and a climbing objective.
If we look back at previous games that had tote like objects, we find:
If we look for games where robots had to lift themselves off the ground and hang on a single bar, we find:
If we look extra hard, we can even find a game that required teams to place oddly shaped scoring pieces high on a goal:
By researching the strategies used in previous games with similar scoring objects and objectives, and by researching the mechanisms teams used to manipulate those scoring objects and complete those objectives, students can put into perspective what is possible and begin to formulate better and more efficient ways of accomplishing the game objectives by building on the work of their forerunners.
This is the same approach college engineering students use when evaluating their Senior Design Projects, scientists use when writing papers to be published, and inventors use when patenting new creations. This process allows us to, as Sir Isaac Newton once said, stand on the shoulders of giants.
Below are the game animations for the aforementioned games to research and a match from each game:
- FIRST Stronghold  Game Reveal
- Recycle Rush  Game Animation
- Triple Play  Game Animation
- FIRST Frenzy  Game Animation
- Stack Attack  Game Animation
So what value did these exercises add? By maximizing each individual scoring objective, and questioning our previous assumptions, we were able to determine the impact of each scoring objective. By walking through the optimal match flow for each maximized scoring objective, new strategies and design considerations can be found. By researching previous games with similar scoring objectives, strategies can be put in perspective and novel approaches can be uncovered.
These exercises also build buy-in with the students and mentors on a strategy, reducing confusion and conflict later in the season, and provide a focus moving forward for defining Robot Requirements.
Head on over to Part 3 – Robot Requirements for more fun!
If you want to learn more about this process, check out our presentation from the 2017 Purdue FIRST Forums on Robot Requirements!