Trench
Team Inspiration is a seventh-year community FTC team working out of our Captain’s garage. The team mission is to Learn, Share, Innovate, and Inspire. This season, the team focused on making their robot as reliable as they could to fulfill our team’s mission. Team Inspiration has increased in size relative to the previous year: now with five people and an “intern”, three of them being new to FTC.
Mechanical Overview
Our robot consists of a drivetrain, extension mechanism, and claw. Our goal was to develop a mechanically effective robot that can successfully and consistently complete numerous missions. Therefore, our team decided to parallel prototype throughout the season, so that we could design different robots at the same time, and then compare them to analyze the strengths and weaknesses of the designs. This way, we could develop a final prototype which would incorporate the best components of each design, resulting in a mechanically effective design.
Software Overview
The software system is divided into three major parts. Path implementation, computer vision, and position tracking systems are all included. Path implementation tells the robot where to travel and what to detect, it's built and designed to follow the path that users define. The computer vision system is a collection of cv detectors that may be turned on and off based on whether they are required at a given moment in the program's performance. The position tracking system collects the data and inputs from various sensors to keep the robot's coordinates and angle consistent across the field.
Featured Content
January 2023 Newsletter
Engineering Process
KISS
We used the KISS, Keep it Simple Silly, principle in the development of this year's robots. An example of this is the claw. We made use of the resources available to us and built it in a simple yet effective way. KISS is an effective building method because it minimizes points of failure but keeps the robot fully functional.
First iteration of the robot
Preloaded cone deposited in autonomous on the bottom level with 50% accuracy
Preloaded cone deposited in autonomous on the middle level with 20% accuracy
Preloaded cone deposited in autonomous on the high level with 10% accuracy
Parked in parking area with 10% accuracy
Second iteration of the robot
Preloaded cone deposited in autonomous on the bottom level with 50% accuracy
Preloaded cone deposited in autonomous on the middle level with 20% accuracy
Preloaded cone deposited in autonomous on the high level with 10% accuracy
Parked in parking area with 33.33% accuracy
Third iteration of the robot
Preloaded cone deposited in autonomous on the bottom level with 70% accuracy
Preloaded cone deposited in autonomous on the middle level with 50% accuracy
Preloaded cone deposited in autonomous on the high level with 40% accuracy
Parked in parking area with 85% accuracy
Final iteration of the robot
This final iteration of the robot is a modification of our second iteration. Due to the claw’s low reliability, we decided to add more support to the bottom half of each side of the claw. Before making this change, the claw would sometimes be bent, therefore tilting the cone sideways and making it difficult to score. With the new addition of more supports, the claw became much stronger and more reliable. We also added an additional rev bar stage to the X-Rail lift to ensure that the arm would be able to reliably and consistently reach the highest pole. Because the rev stage had more friction between the bars, we also added a retraction rig.
DAVE
We used DAVE (Discover a Problem, Address multiple ideas, Vote on a solution, and Execute the plan), an organized method to develop mechanisms. An example of this is the flipper; we noticed that the slides could not reach the third junction height nor did they fit in the size constraints, so we brainstormed and designed the flipper. After voting, we executed this solution and tested it several times.
Software
First Novel Aspect - Encoders:
Encoders were used because we wanted to be as precise as possible. We had previously employed drive-by-time, which, while far from realistic because it became substantially less accurate as the battery voltage decreased, provided a framework for our software. Because encoder ticks correlate to particular changes in angle, encoders can be used to control and accurately move anyplace on the field, to within a few millimeters.
Second Novel Aspect - Computer Vision:
This season, our team is preparing to use computer vision to locate the location of the marker. We used this knowledge to develop the rest of the program's logic.
Third Novel Aspect - Turret Color Sensor
In order to precisely turn the turret to point towards the shipping hub, we utilized a color sensor. This automatic alignment of the turret makes the autonomous significantly faster, since the robot simply has to move back and forth to cycle, and can stay in one position to align (the turret) and deposit shipping elements.
Fourth Novel Aspect - Odometry + Road Runner
We use a motion planning tool for odometry, Road Runner. It was created for FTC and mainly designed and used for autonomous robotic movement purposes. This allows for complex path following and generation while maintaining velocity and acceleration control. We used it to understand, study, implement, and test odometry which resulted in very easy problem identifying.
