Quest Books
S25 Micro Quests

Micro Autonomy Quest Book - Spring 2025

The Great Objective: Fully Autonomous F1tenth Racing Car with LiDAR and Camera

Micro Autonomy aims to win the F1teneth Autonomous Racing Competition in the future

Term Objectives Summary

The objectives for this term focuses on integrating the hardware and software stacks. This includes:

  1. Hardware Setup

    • Assemble & test everything including Jetson, LiDAR and VESC to work properly and publish ros messages needed for autonomous navigation

  2. State Estimation

    • Estimates current position of vehicle against known world map

  3. Mapping: SLAM

    • 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

  4. Planning: Lattice Planner

    • Static obstacle avoidance support

  5. Controls: Pure Pursuit

    • Static obstacle avoidance support

  6. Hardware Setup

ScoreCriteria
10/10Setup interfaces for sensors to publish readings and actuators to take ROS topic commands to support autonomous control
7/10Test electronic & tune motor controllers
5/10Assemble all components for F1Tenth Car to start manually controlled movement
0/10No Progress

Minimum Requirements: Autonomous control ready (10/10)

  1. State Estimation
ScoreCriteria
5/5Tuned state estimation position based on IMU, encoder and LiDAR in actual vehicle
3/5Un-tuned state estimation position based on IMU, encoder and LiDAR in actual vehicle
0/5No Progress

Minimum Requirements: Untuned vehicle estimation (3/5)

  1. Mapping: SLAM
ScoreCriteria
10/10High quality map and yaml file generated through state estimation and LiDAR scans on actual vehicle for complex routes
7/10Map and yaml file generated through state estimation and LiDAR scans on actual vehicle for simple routes
5/10Map and yaml file generated through state estimation and LiDAR scanes in simulation
0/10No Progress

Minimum Requirements: Map and yaml file generated through state estimation and LiDAR scans on actual vehicle for simple routes (7/10)

  1. Raceline Generation/Optimization
ScoreCriteria
10/10Optimizes centerline to generate a raceline that minimizes actual lap time based on complex vehicle dynamics
0/10No Progress

Minimum Requirements: (TBD) No minimum requirements

  1. Planning: Lattice Planner
ScoreCriteria
10/10Dynamic obstacle avoidance
8/10Genenerates/optimizes local trajectory based on cost map and vehicle dynamics to achieve static obstalce avoidance on actual vehicle
5/10Generates & optimize a local trajectory for obstacle avoidance static obstacles are on the ideal path
3/10Generates local trajectory by using a cost map to achieve obstacle avoidance when static obstacles are on the ideal path
0/10No Progress

Minimum Requirements: Real vehicle static obstacle avoidance (8/10)

  1. Controls: Pure Pursuit
ScoreCriteria
10/10Tune pure pursuit controller for dynamic obstacle avoidance on actual vehicle
8/10Tune pure pursuit controller for static obstacle avoidance on actual vehicle
5/10Follows local trajectories and ideal velocities smoothly on actual vehicle
0/10No Progress

Minimum Requirements: Real vehicle static obstacle avoidance (8/10)

  1. Integration
ScoreCriteria
10/10Full software integration & hardware interfaces for real life autonomous racing
8/10Full software integration in simulation for autonomous driving, limited hardware interfaces
0/10No progress

Minimum Requirements: Full integration (10/10)

Scoring Template

Quest NameDescriptionDue DateScore
Hardware Setup2025-08-31
State Estimation2025-08-31
Mapping: SLAM2025-08-31
Raceline Generation/Optimization2025-08-31
Planning: Lattice Planner2025-04-31
Controls: Pure Pursuit2025-08-31
IntegrationFull Integration2025-08-31
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