Define
State the game problem, constraints, desired outcome, and how success will be measured.
Our 2026-2027 season
What we build.
How we learn.
This is our public record of the OtterBots BIOBUZZ journey: the decisions we make, the systems we test, the resources we share, and the team we are building for the future.
Engineering process
Questions become measured decisions
We do not treat the first working idea as the final answer. Every major choice should preserve the alternatives, evidence, and lesson that produced it.
Explore
Compare mechanisms, software approaches, calculations, official resources, and strategic tradeoffs.
Prototype
Build the smallest safe test that can disprove an assumption before committing the full robot.
Measure
Capture repeatability, timing, accuracy, failure modes, and the conditions surrounding each result.
Decide
Select an approach for documented reasons, not because it is fashionable or was tried first.
Iterate
Use failures and new measurements to revise the design, code, test, or original question.
Robot architecture
Start simple, leave room to grow
Our current software foundation supports normalized mecanum driving, operator control, telemetry, and camera-guided AprilTag work. We use that foundation to evaluate autonomous movement and repeatable HIVE alignment without hiding the logic from students.
- Readable drivetrain mixing and normalization
- Safe stop behavior and live telemetry
- AprilTag cluster geometry and distance calculations
- TeleOp alignment and autonomous feedback loops
- Timeouts and explicit state transitions
Control systems
Feedback should change behavior
Our vision programs turn camera observations into robot actions. AprilTag detections are converted into distances, target centers, steering corrections, and stopping decisions. Each step is exposed in the Blocks diagrams and explained in plain language.
ObserveRead visible AprilTags and camera-space measurements.
CalculateEstimate 3D distance, rotated offsets, cluster midpoint, and bearing.
ActTurn and approach using bounded motor commands.
ProtectStop on target loss, timeout, completion, or OpMode end.
BIOBUZZ Roblox Simulation
A strategy laboratory we can revisit
The OtterBots Roblox Simulation lets us explore field navigation, HIVE cycles, FLOWER timing, parking, alliance spacing, and driver decisions before every physical mechanism is ready.
It is an educational model, not proof of real robot performance. We use it to form hypotheses and plan physical tests, then record where the physical robot agrees or disagrees.
Design and innovation
Useful beats complicated
Our goal is a robot whose mechanisms, wiring, software, controls, and maintenance access all support one understandable match strategy.
Integrated design
Evaluate the whole robot: packaging, protected wiring, service access, driver visibility, weight, and interaction between mechanisms.
Reliability first
A creative mechanism earns its place by working repeatedly. We track jams, missed detections, bounce-outs, reset behavior, and recovery time.
Human-centered controls
Driver controls should be predictable, clearly labeled, safely bounded, and practiced under realistic match pressure.
Professional development
Learn from the engineering community
Our learning plan is to deepen skills in mechanical design, control systems, computer vision, Roblox Simulation development, technical communication, and project leadership.
- Study the current Competition Manual, Team Updates, and official Q&A
- Use official FTC, REV, and vendor technical documentation
- Ask STEM professionals for technical feedback and career context
- Apply feedback in a prototype, program, test, or design revision
- Record what students learned and how it changed the work
Team continuity
Leave the next student a stronger starting point
Our public Code Lab, glossary, downloadable programs, diagrams, math explainers, and Roblox Simulation turn one season's work into reusable training material.
- Keep code and explanations together
- Document setup, assumptions, and safe test procedures
- Use shared vocabulary and named subsystem ownership
- Review milestones, risks, and open questions regularly
- Teach newer members by letting them operate and explain the system
Community impact
Share work people can actually use
We publish educational resources instead of reporting inflated audience numbers. When future outreach creates new teams, coaches, mentors, or volunteers, we will document those outcomes separately and accurately.
01
Open learning
Code Lab
Interactive FTC Blocks diagrams, full student-facing explanations, downloadable programs, and generated JavaScript.
Explore Code Lab →02Accessible language
Robot glossary
Plain-language definitions for the vision, math, programming, and control concepts used by the robot.
Open glossary →03Interactive strategy
Roblox Simulation
A public way to explore the BIOBUZZ field, practice decisions, and discuss strategy with shared visual context.
Launch Roblox Simulation →Student ownership and values
Every student should be able to explain the work
DiscoveryAsk questions and learn unfamiliar tools.
InnovationUse creativity and persistence to solve real problems.
ImpactTurn learning into resources others can use.
InclusionMake technical work understandable and invite every voice.
TeamworkShare ownership, review decisions, and help partners succeed.
FunCelebrate the experiments, progress, and people behind the robot.
Evidence boundary: This page describes current OtterBots work and operating goals visible through our public resources. It does not claim unverified competition results, recruitment outcomes, mentor relationships, or physical performance measurements.