Micro Quest Book - Summer 2026 (S26)
The Great Objective: Competition Ready
This term, Micro aims to complete the MVP to a competition-ready state while laying the groundwork for early stage learned sensor fusion. This will involve finishing our local planning stack, extensive real-world tuning, and building and integrating our Intel RealSense camera onto the car.
Term Objectives Summary
[Brief overview of what will be accomplished this term, organized into categories.]
-
Hardware
- Intel RealSense Camera Mount
-
Software
- SDF Based collision detection Node
- Hybrid A* + path optmization Node
- Intel RealSense Software integration
- Race Ready Tuning
- infrastructure improvements
Term Objectives and Scoring
Hardware
- Intel RealSense Camera Mount
| Score | Criteria |
|---|---|
| 10/10 | Camera and mount can survive 8 consecutive laps with minor collision with No damage or minor scufs |
| 7/10 | Mount is attached to platfrom + camera mounted on it |
| 4/10 | Mount is Designed + Fabricated |
| 0/10 | No mount is made |
Minimum Requirements: There must be CAD files + a few printed prototypes for a score of 4/10
Software
- SDF Based collision detection Node
| Score | Criteria |
|---|---|
| 10/10 | node can correct detect collision and suggest adjacent point to local planner |
| 5/10 | node can correctly detect when a collision will happen |
| 2/10 | Node can generate a correct SDF from a occupancy grid message |
| 0/10 | Node does not exist in a functional state and produces no useful outputs |
Minimum Requirements: Node must generate a correct SDF from occupancy grid for score of 2/10
- Hybrid A + Path Optimization Node*
| Score | Criteria |
|---|---|
| 10/10 | Hybrid A* and path optimizer are combined and working reliably on the car |
| 7/10 | Hybrid A* and path optimizer are combined and validated in simulation |
| 4/10 | Path optimizer is built out and producing optimized paths independently |
| 0/10 | Hybrid A* does not output a valid path |
Minimum Requirements: Hybrid A* must output a valid path for a score of 4/10.
- Intel RealSense Software Integration
| Score | Criteria |
|---|---|
| 10/10 | ROS driver is installed, fully functional, and capable of automatically recording rosbags with depth, point cloud, and image data |
| 7/10 | Camera produces depth maps, point clouds, and RGB images through the ROS driver |
| 4/10 | Camera outputs raw image and depth data successfully |
| 0/10 | No usable output from the camera |
Minimum Requirements: Camera must produce raw image and depth output for a score of 4/10.
- Race-Ready Tuning
| Score | Criteria |
|---|---|
| 10/10 | Car is reliably running at a top speed of 10 m/s |
| 7/10 | Car is reliably running at a top speed of 8 m/s |
| 3/10 | Car is reliably running at a top speed of 5 m/s |
| 0/10 | Car is stuck at 3 m/s or below |
Minimum Requirements: Car must reach a top speed of 5 m/s for a score of 3/10.
- Infrastructure Improvements
| Score | Criteria |
|---|---|
| 5/5 | Foxglove is working correctly and well-documented, and an automated rosbag recording script is built that saves bags with timestamped run names to a designated location on launch |
| 2/5 | Foxglove is set up, working correctly, and well-documented |
| 0/5 | No functional infrastructure improvements delivered |
Minimum Requirements: Foxglove must be working correctly and documented for a score of 2/5.
Scoring Template
Hardware
| Quest Name | Description | Score |
|---|---|---|
| Intel RealSense Camera Mount | Intel RealSense Camera mounted to car and ready to use in race |
Software
| Quest Name | Description | Score |
|---|---|---|
| SDF Based Collision Detection Node | Build a node that generates SDFs from occupancy grids and detects collisions to assist the local planner | |
| Hybrid A* + Path Optimization Node | Develop and integrate a Hybrid A* path planner with a path optimizer, validated in sim and on the car | |
| Intel RealSense Software Integration | Install ROS drivers and integrate the Intel RealSense camera to produce depth, point cloud, and image data | |
| Race-Ready Tuning | Tune the full stack for competition-level performance, targeting up to 10 m/s top speed | |
| Infrastructure Improvements | Set up Foxglove with documentation and build an automated rosbag recording script on launch |