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F25 micro (#37)
* update teh s25 micro quest book * updated f25 quest * update --------- Co-authored-by: muhtasim001 <[email protected]>
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‎pages/quest_books/s25_micro_quests.mdx‎

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# Micro Autonomy Quest Book - Spring 2025
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## The Great Objective: Fully Autonomous F1tenth Racing Car with LiDAR and Camera
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Micro Autonomy aims to win the F1teneth Autonomous Racing Competition in the future
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## Term Objectives Summary
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The objectives for this term focuses on integrating the hardware and software stacks. This includes:
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1. **Hardware Setup**
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- Assemble & test everything including Jetson, LiDAR and VESC to work properly and publish ros messages needed for autonomous navigation
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2. **State Estimation**
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- Estimates current position of vehicle against known world map
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2. **Mapping: SLAM**
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- Generate a map in image format and a yaml file for the map specifications based on IMU, encoder and LiDAR specifications on a real world setup
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3. **Planning: Lattice Planner**
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- Static obstacle avoidance support
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4. **Controls: Pure Pursuit**
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- Static obstacle avoidance support
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1. **Hardware Setup**
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| Score | Criteria |
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|-------|-------------------------------------------------------------------------|
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| 10/10 | Setup interfaces for sensors to publish readings and actuators to take ROS topic commands to support autonomous control|
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| 7/10 | Test electronic & tune motor controllers |
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| 5/10 | Assemble all components for F1Tenth Car to start manually controlled movement|
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| 0/10 | No Progress |
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**Minimum Requirements:** Autonomous control ready (10/10)
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2. **State Estimation**
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| Score | Criteria |
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|-------|-------------------------------------------------------------------------|
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| 5/5 | Tuned state estimation position based on IMU, encoder and LiDAR in actual vehicle |
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| 3/5 | Un-tuned state estimation position based on IMU, encoder and LiDAR in actual vehicle |
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| 0/5 | No Progress |
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**Minimum Requirements:** Untuned vehicle estimation (3/5)
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3. **Mapping: SLAM**
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| Score | Criteria |
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|-------|-------------------------------------------------------------------------|
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| 10/10 | High quality map and yaml file generated through state estimation and LiDAR scans on actual vehicle for complex routes|
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| 7/10 | Map and yaml file generated through state estimation and LiDAR scans on actual vehicle for simple routes|
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| 5/10 | Map and yaml file generated through state estimation and LiDAR scanes in simulation |
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| 0/10 | No Progress |
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**Minimum Requirements:** Map and yaml file generated through state estimation and LiDAR scans on actual vehicle for simple routes (7/10)
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4. **Raceline Generation/Optimization**
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| Score | Criteria |
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|-------|-------------------------------------------------------------------------|
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| 10/10 | Optimizes centerline to generate a raceline that minimizes actual lap time based on complex vehicle dynamics |
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| 0/10 | No Progress |
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**Minimum Requirements:** (TBD) No minimum requirements
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5. **Planning: Lattice Planner**
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| Score | Criteria |
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|-------|-------------------------------------------------------------------------|
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| 10/10 | Dynamic obstacle avoidance |
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| 8/10 | Genenerates/optimizes local trajectory based on cost map and vehicle dynamics to achieve static obstalce avoidance on actual vehicle|
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| 5/10 | Generates & optimize a local trajectory for obstacle avoidance static obstacles are on the ideal path |
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| 3/10 | Generates local trajectory by using a cost map to achieve obstacle avoidance when static obstacles are on the ideal path |
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| 0/10 | No Progress |
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**Minimum Requirements:** Real vehicle static obstacle avoidance (8/10)
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6. **Controls: Pure Pursuit**
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| Score | Criteria |
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|-------|-------------------------------------------------------------------------|
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| 10/10 | Tune pure pursuit controller for dynamic obstacle avoidance on actual vehicle |
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| 8/10 | Tune pure pursuit controller for static obstacle avoidance on actual vehicle |
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| 5/10 | Follows local trajectories and ideal velocities smoothly on actual vehicle |
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| 0/10 | No Progress |
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**Minimum Requirements:** Real vehicle static obstacle avoidance (8/10)
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7. **Integration**
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| Score | Criteria |
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|-------|-------------------------------------------------------------------------|
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| 10/10 | Full software integration & hardware interfaces for real life autonomous racing |
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| 8/10 | Full software integration in simulation for autonomous driving, limited hardware interfaces |
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| 0/10 | No progress|
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**Minimum Requirements:** Full integration (10/10)
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## Scoring Template
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| Quest Name | Description | Due Date | Score |
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|------------|-------------|----------|-------|
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| Hardware Setup | | 2025-08-31 | |
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| State Estimation | | 2025-08-31 | |
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| Mapping: SLAM | | 2025-08-31 | |
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| Raceline Generation/Optimization | | 2025-08-31 | |
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| Planning: Lattice Planner | | 2025-04-31 | |
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| Controls: Pure Pursuit | | 2025-08-31 | |
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| Integration | Full Integration | 2025-08-31 | |
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# Micro Autonomy Quest Book - Spring 2025
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## The Great Objective: Fully Autonomous F1tenth Racing Car with LiDAR and Camera
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Micro Autonomy aims to win the F1teneth Autonomous Racing Competition in the future
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## Term Objectives and Tasks
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- **2.3. Camera Integration with Localization**: Integrate Camera Object Detection with Localization to use real-time slam mapping
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- **5. Planning: Local Planning**: Enable Obstacle Avoidance in lattice planner on actual vehicle
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- **6. Control: Pure Pursuit**: Integrate and tune algorithm with local planner to work on actual vehicle
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## **Hardware Setup**
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1.1 Traxis Car Assembly
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| Score | Criteria |
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|-------|-------------------------------------------------------------------------|
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| 10/10 | Fully built with all sensors and hardware mounted and power system working|
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| 8/10 | Have all the sensors and Jetson mounted to the Platfrom Plate |
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| 6/10 | Fabriate the Platfrom mounting plate and all other mounting hardware |
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| 4/10 | Gut and stip all the components from the original chassis |
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| **2/10** | Purchase & manufacture all (within current budget) components |
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| 0/10 | No Progress |
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Progress: 10/10
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1.2 LiDAR
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| Score | Criteria |
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|-------|-------------------------------------------------------------------------|
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| 10/10 | Setup interfaces for LiDAR to ouput a ROS /scan topic |
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| 7/10 | setup the docker compose file to correctly interface with the LiDAR |
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| 5/10 | configure the ip and network settings for the LiDAR |
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| 0/10 | No Progress |
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Progress: 10/10
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1.3 Vec
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| Score | Criteria |
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|-------|-------------------------------------------------------------------------|
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| 10/10 | tune the IMU to correctly how roll, pitch and yaw data and accleration values |
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| 7/10 | tune the motor PID controller to produce a step response |
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| 5/10 | configure all the hardware limits and current settings |
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| 2/10 | Have the vesc power on and showup in the Vesc tool software |
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| 0/10 | No Progress |
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Progress: 10/10
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1.4 Overall Hardware integration
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| Score | Criteria |
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|-------|-------------------------------------------------------------------------|
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| 10/10 | Setup interface with the correct ROS Drivers and have a tuned and accurate odometry output |
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| 7/10 | configure the yaml files to fine tune odometry |
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| 5/10 | Install the correct ROS Transport drivers along with F1teneth Driver Stack |
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| 0/10 | No Progress |
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Progress: 10/10
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## **State Estimation and Localization**
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2.1 Extended Kalman Filter
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| Score | Criteria |
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|-------|-------------------------------------------------------------------------|
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| 10/10 | Have a fully functional Extended Kalman Filter working on the physical vehicle |
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| 8/10 | Have a fully functional Extended Kalman Filter working in the simulation |
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| 6/10 | Have fully Defined sensor models for the EKF |
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| 4/10 | Have Fully Defined motion model for the EKF along with the corresponding Jacobian |
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| 2/10 | have fully Defined state to propigate for the EKF |
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| 0/10 | No Progress |
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Progress: 10/10
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2.2 Particle Filter
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| Score | Criteria |
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|-------|-------------------------------------------------------------------------|
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| 5/5 | Have Particle Filter working on physical vehicle |
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| 3/5 | Have Particle Filter working in the simulation |
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| 0/5 | No Progress |
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Progress: 10/10
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2.3 Integrate Camera Object Detection with Localization
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| Score | Criteria |
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|-------|-------------------------------------------------------------------------|
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| 10/10 | Localization works for changing environments in actual vehicle |
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| 5/10 | Ignores LiDAR scans that interfering with real-time mapping |
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| 0/10 | No Progress |
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Progress: 0/10
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## **Mapping: SLAM**
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| Score | Criteria |
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|-------|-------------------------------------------------------------------------|
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| 10/10 | Map and yaml file generated through state estimation and LiDAR scans on actual vehicle |
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| 8/10 | Map and yaml file generated through state estimation and LiDAR scanes in simulation |
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| **5/10** | Map and yaml file generated through true position (odometry) and LiDAR scans in simulation |
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| 0/10 | No Progress |
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Progress: 10/10
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## **Raceline Generation/Optimization**
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| Score | Criteria |
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|-------|-------------------------------------------------------------------------|
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| 10/10 | Optimizes centerline to generate a raceline and velocity profile that minimizes steering for all maps |
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| 7/10 | Optimizes centerline to generate a raceline that minimizes steering for all maps |
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| 5/10 | Generates a centerline for the vehicle to follow |
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| 0/10 | No Progress |
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Progress: 10/10
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## **Planning: Lattice Planner**
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| Score | Criteria |
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|-------|-------------------------------------------------------------------------|
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| 10/10 | Genenerates/optimizes local trajectory based on cost map and vehicle dynamics to achieve obstalce avoidance on actual vehicle|
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| 9/10 | Genenerates/optimizes local trajectory based on cost map and vehicle dynamics to achieve obstalce avoidance |
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| 8/10 | Generates local trajectory by using a cost map to achieve obstacle avoidance when obstalces are on the ideal path |
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| 5/10 | Generates local trajectory to make vehicle follow the raceline (ideal path)|
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| 0/10 | No Progress |
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Progress: 5/10
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## **Controls: Pure Pursuit**
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| Score | Criteria |
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|-------|-------------------------------------------------------------------------|
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| 10/10 | Follows local trajectories and ideal velocities smoothly on actual vehicle |
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| 7/10 | Follows local trajectories and ideal velocities smoothly in simulation |
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| 4/10 | Follows a global trajectory by controlling steerring angle with a constant velocity |
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| 0/10 | No Progress |
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Progress: 7/10
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## **Camera Object Detection and Tracking**
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| Score | Criteria |
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|-------|-------------------------------------------------------------------------|
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| 10/10 | Camera detects opponent cars and returns bounding boxes reliabilty in real-time with low latency |
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| 6/10 | Camera detects opponent cars and returns bounding boxes |
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| 0/10 | No Progress |
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Progress: 0/10
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## Scoring Template Summary
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| Quest Name | Description | Due Date | Score |
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|------------|-------------|----------|-------|
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| Hardware Setup 1.1 | Fully built with all sensors and hardware mounted and power system working | 2025-08-31 | 10/10 |
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| Hardware Setup 1.2 | Setup interfaces for LiDAR to ouput a ROS /scan topic | 2025-08-31 | 10/10 |
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| Hardware Setup 1.3 | Tune the IMU to correctly how roll, pitch and yaw data and accleration values | 2025-08-31 | 10/10 |
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| Hardware Setup 1.4 | Setup interface with the correct ROS Drivers and have a tuned and accurate odometry output | 2025-08-31 | 10/10 |
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| State Estimation 2.1 | Have a fully functional Extended Kalman Filter working on the physical vehicle | 2025-08-31 | 10/10 |
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| State Estimation 2.2 | Have Particle Filter working on physical vehicle | 2025-08-31 | 5/5 |
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| State Estimation 2.3 | Integrate Camera Object Detection with Localization to use real-time slam mapping | | 0/10 |
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| Mapping: SLAM | Generate a map in image format and a yaml file for the map specifications based on IMU, encoder and LiDAR specifications. | 2025-08-31 | 10/10 |
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| Raceline Generation/Optimization | Generate a optimized raceline as global trajectory (ideal path) for the race car to follow to minimize lap time. | 2025-08-31 | 10/10 |
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| Planning: Lattice Planner | Generate local trajectories for obstacle avoidance and following global trajectory during Racing. | 2025-08-31 | 5/10 |
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| Controls: Pure Pursuit | Controls the vehicle's steering and trottle to following the trajectories generated by the planning module. | 2025-08-31 | 7/10 |
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| Camera Object Detection and Edge Tracking | Camera detects opponent cars and returns bounding boxes reliabilty in real-time with low latency | | 0/10 |

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