Autonomous Navigation in Duckietown with QuackCruiser

QuackCruiser: Autonomous Navigation with Dijkstra

Posted on August 30, 2025 | by Duckietown Admin

QuackCruiser: Autonomous Navigation with Dijkstra

Project Resources

Objective: Enable robust Duckietown autonomous navigation using integrated perception, planning, and control modules.
Approach: Use AprilTag detection, YOLO-based obstacle recognition, Dijkstra planning, and ROS-based state machine coordination.
Authors: Li Yunwen, Farian Keck, Jiranyi from ETH Zurich, Switzerland

Autonomous navigation in Duckietown with QuackCruiser - objectives and approach

The objective of this project is to implement autonomous navigation in Duckietown by integrating perception, Dijkstra planning, and control into a Duckiebot (DB21J).

Localization at intersection is achieved using AprilTag detection, YOLO-ROS is used for real-time obstacle recognition, and onboard sensors such as wheel encoders and IMU are used for odometry. These inputs provide both exteroceptive data (from the environment) and interoceptive data (from the robot itself), which are fused to estimate pose and environment state.

Planning is performed with Dijkstra planning algorithm, a graph search method that computes the shortest path on a grid-based map where intersections are nodes and lanes are edges with associated costs.

Control is implemented through PID-based lane following and parameterized turning services, where each maneuver is defined by velocity, radius, and execution time. A ROS state machine coordinates perception inputs and planning outputs to trigger the correct control actions in ‘real time’.

Autonomous navigation in Duckietown with QuackCruiser - highlights

The challenges

The principal challenges in implementing this agent emerge from hardware calibration, computational limitations, and cross-module synchronization.

Wheel encoder calibration directly influences odometric drift, while camera calibration governs the reliability of AprilTag-based localization and lane geometry estimation. The deployment of CUDA-accelerated YOLO models within the ROS ecosystem introduces compatibility constraints across GPU drivers, compiler toolchains, and real-time inference pipelines, which collectively impose significant computational overhead on limited embedded resources.

At the system integration level, temporal synchronization across perception modules (AprilTag detection, obstacle detection) and control modules (lane following, turning) constitutes a critical factor, as phase offsets and latencies propagate into localization uncertainty and trajectory deviation.

Sensor fusion must accommodate inconsistency between odometry estimates and visual updates, with conflict resolution strategies directly shaping the stability of pose estimation.

Furthermore, the tuning of control gains and maneuver execution parameters remains non-trivial, since cumulative deviations over extended trajectories amplify minor discrepancies in actuation dynamics and timing precision.

Duckietown Autonomous Navigation system architecture with perception, planning, control, and actuation modules — System Architecture for Duckietown Autonomous Navigation

Duckietown Autonomous Navigation environment with AprilTag-based intersection and goal tag setup — Environment Setup for Duckietown Autonomous Navigation

Duckietown Autonomous Navigation map graph for Dijkstra planning with start, goal, and path costs — Map Graph for Duckietown Autonomous Navigation

Duckietown Autonomous Navigation sample terminal log showing planner, state machine, and lane following execution — Terminal Log for Duckietown Autonomous Navigation

Looking for similar projects?

Check out the following works on path planning with Duckietown:

Autonomous navigation in Duckietown with QuackCruiser: authors

Yunwem Li is a Computer engineering graduate with a master’s in robotics from ETH Zurich, Switzerland.

Farian Keck is currently working as an Autonomy intern at Airbus Defence and Space , Switzerland.

Jiranyi has a master’s in robotics from ETH Zurich, Switzerland.

Learn more

Duckietown is a modular, customizable, and state-of-the-art platform for creating and disseminating robotics and AI learning experiences.

Duckietown is designed to teach, learn, and do research: from exploring the fundamentals of computer science and automation to pushing the boundaries of knowledge.

These spotlight projects are shared to exemplify Duckietown’s value for hands-on learning in robotics and AI, enabling students to apply theoretical concepts to practical challenges in autonomous robotics, boosting competence and job prospects.

Visual Feedback for Autonomous Navigation in Duckietown

Features for Efficient Autonomous Navigation in Duckietown

Posted on June 28, 2025 | by Duckietown Admin

Features for Efficient Autonomous Navigation in Duckietown

Project Resources

Objective: Improving the range of autonomous behaviors of physical Duckiebots in Duckietown.
Approach: Improving and integrating novel control (pure pursuit), perception (YOLO), and navigation algorithms on the Duckietown out-of-the-box "lane following" visual pipeline for autonomy.
Authors: Servesh Khandwe, Ayush Kumar, and Parth Karkar from the Technical University of Munich, Germany.

Project highlights

: SSD Model Performance (mAP)

: SSD Model Loss Over Iterations

: Object Detection in Duckietown

: Labels of the baseline Model

: Labels of the new fine-tuned Model

: Confusion Matrix of the baseline Model

: Confusion Matrix of the new finetuned Model

: Model Performance on real Duckietown environment

: Annotations format used by YOLO for output

: Ground Projection for Path Tracking

: Edge Detection of Lane Boundaries

: Color detector for stop line filter

: Bot detector for Collision avoidance

: April Tag sign detection

Visual Feedback for Autonomous Navigation in Duckietown - the objectives

This project from students at TUM (Technische Universität of Munich) builds on the preexisting Duckietown autonomy stack to add/reintegrate/improve upon much-needed autonomous navigation features: improved control (pure pursuit instead of PID), red stop line detection, AprilTag detection, intersection navigation, and obstacle detection (using YOLO v3), making Duckietowns more complex and interesting!

The resulting agent includes modules for lane following, stop line detection, and intersection handling using AprilTags, following the legacy infrastructure of Duckietown.

The autonomy pipeline relies heavily on vision as the primary means of perception: lane edges are projected from image space to the ground plane using inverse perspective mapping learned after running a camera calibration procedure.

The Duckiebot then estimates a dynamic target point by offsetting yellow or white lane markers depending on visibility. The curvature is computed based on the geometric relation between the Duckiebot and the goal point, and the steering command is derived from this curvature.

The Duckiebot velocity and angular velocity are then modulated using a second-degree polynomial function based on detected path geometry.

Visual input from an onboard monocular camera is processed through a lane filter with adaptive Gaussian variance scaling relative to frame timing.

When running by an intersection, stop lines are detected using HSV color segmentation. AprilTag detection determines intersection decisions, with tag IDs mapped to turn directions.

Every module is implemented as an independent ROS package with dedicated launch files, coordinated via a central launch file. A YOLOv3 object detection model, trained on a custom Duckietown dataset, provides real-time obstacle recognition.

SSD object detection performance for autonomous navigation using pure pursuit in Duckietown — SSD Model Performance (mAP)

Loss graph of SSD model during autonomous navigation training using pure pursuit in Duckietown — SSD Model Loss Over Iterations

SSD-based object detection output in autonomous navigation using pure pursuit in Duckietown — Object Detection in Duckietown

Initial YOLO model label annotations in autonomous navigation using pure pursuit in Duckietown — Labels of the baseline Model

Refined label annotations after YOLO model fine-tuning for autonomous navigation using pure pursuit in Duckietown — Labels of the new fine-tuned Model

Confusion matrix showing classification results of the baseline YOLO model in autonomous navigation using pure pursuit in Duckietown — Confusion Matrix of the baseline Model

Confusion matrix showing improved detection accuracy of the fine-tuned YOLO model in autonomous navigation using pure pursuit in Duckietown — Confusion Matrix of the new finetuned Model

YOLO model output in real-time autonomous navigation using pure pursuit in Duckietown — Model Performance on real Duckietown environment

Annotation format structure used by YOLO for object detection in autonomous navigation using pure pursuit in Duckietown — Annotations format used by YOLO for output

Ground projection technique applied for lane tracking in autonomous navigation using pure pursuit in Duckietown — Ground Projection for Path Tracking

Lane edge detection method supporting autonomous navigation using pure pursuit in Duckietown — Edge Detection of Lane Boundaries

Color-based stop line detection system for autonomous navigation using pure pursuit in Duckietown — Color detector for stop line filter

Real-time bot detection for collision avoidance in autonomous navigation using pure pursuit in Duckietown — Bot detector for Collision avoidance

AprilTag detection system for intersection navigation in autonomous navigation using pure pursuit in Duckietown — April Tag sign detection

The challenges and approach

One major hurdle was integrating object detection models like Single-Shot Detector (SSD) and YOLO with the Duckiebot’s ROS-based camera system.

While the SSD model was trained on a custom Duckietown dataset, ROS publisher-subscriber mismatches prevented live inference. Transitioning to the YOLO model involved adapting annotation formats and re-training for compatibility with the YOLO architecture. In lane following, the default controller from Duckietown demos showed high deviation, prompting the implementation of a modified pure pursuit approach.

Additional challenges arose from limited computational resources on the Duckiebot, with CPU overuse causing processing delays when running all modules concurrently. The approach focused on modular development, isolating lane following, stop line detection, and intersection navigation into separate ROS packages with fine-tuned parameters. The pure pursuit algorithm was adapted for ground-projected lane estimation, dynamic speed control, and target point calculation based on visible lane markers. Integration of AprilTag-based intersection logic and LED signaling provided directional control at intersections.

This structured, iterative methodology enabled real-time, vision-guided behavior while operating within the constraints.

Project Report

Did this work spark your curiosity?

Check out the follow works on path planning with Duckietown:

Visual Feedback for Autonomous Navigation in Duckietown: Authors

Servesh Khandwe is currently working as a Software Engineer at Porsche Digital, Germany.

Ayush Kumar is currently working as a Research Assistant at Fraunhofer IIS, Germany.

Parth Karkar is currently working as an Analytical Consultant at Mutares SE & Co. KGaA, Germany.

Learn more

Duckietown is a modular, customizable, and state-of-the-art platform for creating and disseminating robotics and AI learning experiences.

Duckietown is designed to teach, learn, and do research: from exploring the fundamentals of computer science and automation to pushing the boundaries of knowledge.

Autonomous Navigation System Development in Duckietown

Posted on May 31, 2025 | by Duckietown Admin

Autonomous Navigation System Development in Duckietown

Project Resources

Objective: Enable safe and reliable autonomous navigation for Duckiebots in Duckietowns.
Approach: Use onboard sensors, computer vision, and Dijkstra algorithm to navigate lanes, intersections, and avoid obstacles.
Authors: Julien-Alexandre Bertin Klein, Andrea Pellegrin, Fathia Ismail from the Technical University of Munich, Germany.

Project highlights

Camera and line detector for autonomous navigation — Detecting and stopping at the stop line

AprilTag and line detector views for autonomous navigation — Detecting intersections and executing turns

Dijkstra algorithm map for autonomous navigation — Transforming Roads into a Navigable Network

Autonomous Navigation System Development in Duckietown - the objectives

The primary objective of this project is to develop and refine an Autonomous Navigation System within the Duckietown environment, leveraging ROS-based control and computer vision to enable reliable lane following and safe intersection navigation. This includes calibrating sensor inputs, particularly from the camera, IMU, and encoders, and integrating advanced algorithms such as Dijkstra algorithm for optimal path planning. The project aims to ensure that the Duckiebot can autonomously detect lanes, stop lines, and obstacles while dynamically computing the shortest path to any designated point within the mapped environment. Additionally, the system is designed to transition smoothly between operational states (lane following, intersection handling, and recovery) using a refined Finite State Machine approach, all while maintaining robust communication within the ROS ecosystem.

Project Report

The challenges and approach

The project faced several challenges, beginning with hardware constraints, such as the physical limitations of wheel traction and battery lifespan, which affected motion stability and operational time. The integration of various ROS packages, some with incomplete documentation and inconsistent coding practices, complicated the development of a reliable and maintainable codebase. The method adopted involved precise sensor calibration to ensure accurate perception and control, incorporating camera intrinsic and extrinsic calibration for improved visual data interpretation, and adjusting wheel parameters to maintain balanced motion. The lane following module required parameter tuning for gain, trim, and heading correction to adapt to Duckietown’s environment. The original FSM-based intersection navigation system was re-engineered due to unreliability in node transitions, replaced with a distance-based approach for intersection stops and turns, ensuring deterministic and reliable behavior. Dijkstra’s algorithm was implemented to create a structured graph representation of the city map, enabling dynamic path planning that adapts to real-time inputs from the perception system. Custom web dashboards built with React.js and roslibjs facilitated monitoring and debugging by providing live data feedback and control interfaces. Through this rigorous and iterative process, the project achieved a robust autonomous navigation system capable of precise path planning and safe maneuvering within Duckietown.

Did this work spark your curiosity?

Check out the follow works on path planning with Duckietown:

Autonomous Navigation System Development in Duckietown: Authors

Julien-Alexandre Bertin Klein is currently a Bachelor of Science (BSc.), Information Engineering at the Technical University of Munich, Germany.

Andrea Pellegrin is currently a Bachelor of Science (BSc.), Information Engineering at the Technical University of Munich, Germany.

Fathia Ismail is currently a Bachelor of Science (BSc.), Information Engineering at the Technical University of Munich, Germany.

Learn more

Duckietown is a modular, customizable, and state-of-the-art platform for creating and disseminating robotics and AI learning experiences.

Duckietown is designed to teach, learn, and do research: from exploring the fundamentals of computer science and automation to pushing the boundaries of knowledge.

Path Planning for Multi-Robot Navigation in Duckietown

Posted on April 5, 2025 | by Duckietown Admin

Path Planning for Multi-Robot Navigation in Duckietown

Project Resources

Objective: Design a scalable and optimal path planning system for autonomous robot navigation in Duckietown.
Approach: Implemented a nodegraph-based path planning algorithm that maps quarter-tile nodes and calculates efficient movement sequences for robots.
Authors: Alexander Hatteland, Marc-Philippe Frey, Demetris Chrysostomou

Project highlights

Path planning nodegraph for Goto-N algorithm — Figure 1. Nodegraph of the Goto-N Pipeline

Path planning script overview in Duckietown — Figure 2. Overview of Python Files

Path planning move count per random trial — Figure 7. Movements per Trial with Random Start and End

Path planning analysis with two robots — Figure 8. Total Movements with Two Robots

Path planning evaluation with three robots — Figure 9. Total Movements with Three Robots

Path planning average movement vs team size — Figure 10. Avg. Total Movements vs. Bots Used

Path planning effort per robot based on team — Figure 11. Movements per Bot vs. Bots Used

Path planning comparison of actual vs expected — Figure 12. Desired vs. Actual Termination Positions

: Figure 1. Nodegraph of the Goto-N Pipeline

: Figure 2. Overview of Python Files

: Figure 3. Map Divided into 14 Tile Nodes

: Figure 4. Allowable Moves from Nodes in Autolab

: Figure 5. Optimal Moves by Termination Position

: Figure 6. Final Precision Difference on Autobot

: Figure 7. Movements per Trial with Random Start and End

: Figure 8. Total Movements with Two Robots

: Figure 9. Total Movements with Three Robots

: Figure 10. Avg. Total Movements vs. Bots Used

: Figure 11. Movements per Bot vs. Bots Used

: Figure 12. Desired vs. Actual Termination Positions

Path planning for multi-robot navigation in Duckietown - the objectives

Navigating Duckietown should not feel like solving a maze blindfolded!

The “Goto-N” path planning algorithm gives Duckiebots the map, the plan, and the smarts to take the optimal path from here to there, without wandering around by turning the map into a graph and every turn into a calculated choice.

While Duckiebots have long been able to follow lanes and avoid obstacles, truly strategic navigation, thinking beyond the next tile, toward a distant goal, requires a higher level of reasoning. In a dynamic Duckietown, robots need more than instincts. They need a plan.

This project introduces a node-based path-planning system that represents Duckietown as a graph of interconnected positions. Using this abstraction, Duckiebots can evaluate both allowable and optimal routes, adapt to different goal positions, and plan their moves intelligently.

The Goto-N project integrates several key concepts like:

Nodegraph representation: transforms the tile-based Duckietown map into a graph of quarter-tile nodes, capturing all possible robot positions and transitions.
Allowable and optimal move generation: differentiates between all legal movements and the most efficient moves toward a goal, supporting informed decision-making.
Termination-aware planning: computes optimal actions relative to a chosen destination, enabling precise goal-reaching behaviors.
Multi-robot scalability: validates the planner across one, two, and three Duckiebots to assess coordination, efficiency, and performance under shared conditions.
Real-world implementation and validation: demonstrates the effectiveness of Goto-N through trials in the Autolab, comparing planned movements to real robot behavior.

The challenges and approach

Navigating Duckietown poses several technical challenges: translating a continuous environment into a discrete planning space, handling edge cases like partial tile positions, and enabling efficient coordination among multiple autonomous agents.

The Goto-N project addresses these by discretizing the Duckietown map into a graph of ¼-tile resolution nodes, capturing all possible robot poses and orientations.

Using this representation, the system classifies allowable moves based on physical constraints and tile connectivity, then computes optimal moves to minimize distance or steps to a termination node using heuristics and precomputed lookup tables.

A Python-based pipeline then ingests the map layout, builds the nodegraph, and generates movement policies, which are then validated through simulated and physical trials. The system scales to multiple Duckiebots by assigning independent paths while analyzing overlap and bottlenecks in shared spaces, ensuring robust, efficient multi-robot planning.

Path planning (Goto-n) in Duckietown: full report

The design and implementation of this path planning algorithm is documented in the following report.

Path planning (goto-n) in Duckietown: Authors

Alexander Hatteland is currently working as a Consultant at Boston Consulting Group (BCG), Switzerland.

Marc-Philippe Frey is currently working as a Consultant at Boston Consulting Group (BCG), Switzerland.

Demetris Chrysostomou is currently a PhD candidate at Delft University of Technology, Netherlands.

Learn more

Duckietown is a modular, customizable, and state-of-the-art platform for creating and disseminating robotics and AI learning experiences.

Duckietown is designed to teach, learn, and do research: from exploring the fundamentals of computer science and automation to pushing the boundaries of knowledge.

Smart Lighting: Realistic Day and Night in Duckietown

Posted on October 11, 2024 | by Duckietown Admin

Smart Lighting: Realistic Day and Night in Duckietown

Project Resources

Project Highlights

Here is the output of the authors’ work on smart lighting autonomous driving.

A diagram showing the flow of nodes in the image processing pipeline, from camera input to lane detection using color filtering and line detection to enable smart lighting autonomous driving in Duckietown. — Figure 1. Image Processing Pipeline for Duckiebot Lane Detection.

A diagram illustrating the open loop control system of the Duckietown street lighting system, showing the interaction between light sources and the image processing pipeline. — Figure 3. Open Loop Control of Duckietown Street Lighting System.

A pair of wooden streetlight prototypes designed by Aurel Neff, positioned in Duckietown to provide lighting for smart lighting autonomous driving experiments. — Figure 4. Aurel Neff's Wooden Light Stands for Duckietown.

A control loop diagram showing the Duckiebot as the sole sensor for managing street lighting in Duckietown, using detected segments to control lighting conditions. — Figure 6. Control Loop with Duckiebot as the Only Sensor.

A feedback loop diagram showing how the Duckiebot controls its own smart lighting system using detected lane segments and environmental lighting conditions. — Figure 7. Feedback Control Loop of Duckiebot Lighting System.

A control loop diagram showing the watchtower's camera acting as a sensor to manage street lighting in Duckietown, influencing the Duckiebot's lane detection. — Figure 8. Control Loop with Watchtower Camera as Sensor.

A feedback loop diagram illustrating how the watchtower's camera acts as a sensor to control the street lighting system in Duckietown. — Figure 9. Feedback Loop with Watchtower Camera as Sensor.

A control loop diagram showing how the RGB sensor in the watchtower is used to manage street lighting conditions in Duckietown. — Figure 10. Control Loop Using RGB Sensor as Sensor.

A graphical representation showing the effect of updated color ranges on the color detection capabilities of the Duckiebot in Duckietown. — Figure 13. Impact of Modified Color Ranges on Detection.

Investigated lighting conditions in RGB space. The colored dots illustrates the colors of the edges of the grid. — Figure 14. Investigated lighting conditions in RGB space. The colored dots illustrates the colorsof the edges of the grid

A comparison of (a) lane locations considered valid and (b) segments detected by the Duckiebot in Duckietown. — Figure 15. Filtering of Detected Segments in Duckietown.

An image depicting the experimental setup used to assess optimal lighting conditions for the Duckiebot on a straight street in Duckietown. — Figure 16. Experimental Setup for Evaluating Optimal Lighting Conditions on a Straight Street.

An image depicting the experimental setup used to assess the Duckiebot lighting system, highlighting that the WT04 watchtower is not operational. — Figure 17. Experimental Setup for Evaluating the Duckiebot Lighting System with Non-Functional WT04.

Measured values for d and phi while the Duckiebot is following the lane — Figure 18. Lane following performance of the Duckiebot at different and changing light condition of the ceiling light and the street lighting system controlling the light on the streets of Duckietown

Why day and night autonomous driving in Duckietown?

Autonomous driving is already inherently hard. Driving at night makes it even more challenging! This is why smart lighting is an interesting application that intersects with autonomous driving: having city infrastructure, such as traffic lights and watchtowers, generate dynamically varying light – only where and when they’re needed – to make driving at night not only possible but safe. Here are some reasons for which this project is interesting:

Realistic driving scenarios: autonomous driving systems must handle varying lighting conditions. Day and night cycles are just the beginning: transitions like sunrise or sunset make the spectrum of experimental corner cases more complex, hence Duckietown a valuable testbed.

Robust lane-following capabilities: developing an adaptive lighting system in which the city infrastructure “collaborates” with Duckiebot to provide optimal driving scenarios reinforces driving performances and general robustness for lane following.

Decentralized control for scalability: a decentralized approach to managing lighting implies that the system can be scalable across Duckietowns of arbitrary dimensions, making it more adaptable and resilient.

Autonomous lighting management: a responsive street lighting system, working in tandem with the Duckiebot’s onboard sensors, improves energy efficiency and ensures safety by adjusting to local lighting needs automatically.

Smart Lighting: Realistic Day and Night in Duckietown - the challenges

Implementing smart lighting in Duckietown to improve autonomous driving during day and night cycles presents several challenges. Here are a few examples:

Hardware modifications: while Duckiebots are equipped with controllable LEDs, city infrastructure does not possess lighting capabilities out of the box. The first step is integrating light sources in the design of Duckietown’s city infrastructure.

Variable lighting conditions: Duckiebots, which in this project rely uniquely on vision in their autonomy pipeline, must adapt to changing lighting conditions such as full darkness, sunrise, sunset, and artificial lighting, which impacts camera vision and lane detection accuracy.

Decentralized control: managing street lighting in a decentralized way across Duckietown ensures that each area adapts to its local lighting needs, compensating for example for the presence of passing Duckiebots with their own lights on. Join control algorithms including both city infrastructure and vehicle lighting intensity add complexity to the system’s design and coordination.

Scalability: the street lighting system must be scalable across the entire city, requiring a design that can be expanded without significant complications.

Safe and reliable operation: the system needs to be safe, adapting to issues such as occasional watchtower lighting source failure, while ensuring consistent lane-following performance.

Smart Lighting: Realistic Day and Night in Duckietown: Results

Smart Lighting: Realistic Day and Night in Duckietown: Authors

David Müller is a former Duckietown student of class Autonomous Mobility on Demand at ETH Zurich, and currently works as a Research Engineer at Disney Research, Switzerland.

Learn more

Duckietown is a modular, customizable and state-of-the-art platform for creating and disseminating robotics and AI learning experiences.

It is designed to teach, learn, and do research: from exploring the fundamentals of computer science and automation to pushing the boundaries of knowledge.

Introducing Autonomous Parking in Duckietown Cities

Posted on May 27, 2024 | by Duckietown Admin

Introducing Autonomous Parking in Duckietown Cities

Project Resources

Why Autonomous Parking?

Parking is notoriously a hard task to master for many humans. Hence, students of the Autonomous Mobility on Demand course at ETH Zurich wanted to determine to what degree this applied to autonomous parking with Duckiebots.

The goal of the Autonomous Parking project was to design, implement, and test a complete autonomous parking solution compliant with the Duckietown ecosystem.

Duckiebots should be able to enter and exit a parking area, identify viable parking lots, actually park and exit their parking spot safely, and avoid collision with other Duckiebots during the entire process.

The vision is to integrate autonomous charging solutions into the parking area, so Duckiebots can charge themselves when needed.

Autonomous parking in Duckietown: the challenges

Leveraging the Duckietown lane following vision baseline provided a basic infrastructure to build upon.

Some technical challenges specific to this projects were:

Backward Lane Following: Duckiebots must drive backward to exit the parking lots but only have cameras on the front. It is required to adjust the Duckiebot’s control system for stable backward driving, by changing the pose estimation process and re-tuning the PID controller.

Dynamic Color Adaptation: the new parking lot design introduced additional appearance specifications to the Duckietown city setup, such as blue lines identifying parking areas. Modifying the Duckiebots’ native lane detector to recognize blue lines in addition to yellow, red, and white, allows for additional flexibility in lane following based on specified colors.

Time Slot Coordination: Managing the availability of parking spaces is crucial to minimize the probability of collisions between Duckiebots. This project tackled this challenge by implementing a time-slot system to manage parking exits to prevent collisions, using red LEDs for signaling to other Duckiebots.

Project Highlights

Here is a visual tour of the work of the authors.

Check out the documents for more details!

Autonomous parking lot with Duckiebots in Duckietown — Resulting parking lot area configuration within Duckietown. The parking lot is compliant with Duckietown appearance specifications, and features one entry/exit intersection. The parking lot closed-loop and modular configuration With the goal of enabling Duckiebots to autonomously enter, park, and exit parking spaces while avoiding collisions. This design exemplifies the project's focus on creating a functional and adaptable parking solution.

Figure 2 illustrates a conceptual design of a parking area within the Duckietown environment. Comprising various standardized tiles, this depiction showcases a modular layout suitable for accommodating Duckiebots. The design includes six straight tiles, four curved left tiles, one three-way center tile, and two empty tiles. These components are arranged to form a configurable parking space that can be adapted to fit different spatial constraints. Notably, the configuration incorporates a T-intersection positioned such that Duckiebots are required to execute specific maneuvers upon entry and exit, ensuring adherence to the predetermined functionality of the parking area. — Design of a parking area within the Duckietown environment. Comprising various standardized tiles, this example showcases a modular layout suitable for accommodating Duckiebots. The design includes six straight tiles, four curved left tiles, one three-way center tile, and two empty tiles. Notably, the configuration incorporates a T-intersection positioned such that Duckiebots are required to execute specific maneuvers upon entry and exit, ensuring adherence to the predetermined functionality of the parking area.

Autonomous parking in Duckietown, parking lot specifications — Parking spaces are delimited by a blue line with specified geometry. This configuration is functional to allow the Duckietown lane filter to determine the pose of the robot and facilitate precise entrance and exit from the parking space.

Project Parking Results

(Turn on the sound for best experience!)

Project Authors

Trevor Phillips is a former Duckietown student, now a Machine Learning SWE at Apple in Switzerland.

Vincenzo Polizzi is a former Duckietown student, now a Ph. D. student at the University of Toronto, Canada.

Linus Lingg is a former Duckietown student, now the Co-Founder and CTO of bottleplus in Switzerland.

Learn more

Duckietown is a modular, customizable and state-of-the-art platform for creating and disseminating robotics and AI learning experiences.

It is designed to teach, learn, and do research: from exploring the fundamentals of computer science and automation to pushing the boundaries of knowledge.