Interpretable Reinforcement Learning for Visual Policies

Posted on July 2, 2025 | by Duckietown Admin

General Information

Title: Self-Supervised Discovering of Interpretable Features for Reinforcement Learning
Authors: Wenjie Shi, Gao Huang, Shiji Song, Zhuoyuan Wang, Tingyu Lin, Cheng Wu
Institution: Tsinghua University, Beijing, China
Citation: W. Shi, G. Huang, S. Song, Z. Wang, T. Lin and C. Wu, "Self-Supervised Discovering of Interpretable Features for Reinforcement Learning," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 5, pp. 2712-2724, 1 May 2022, doi: 10.1109/TPAMI.2020.3037898.

Interpretable Reinforcement Learning for Visual Policies

Diagram of a two-stage architecture for Interpretable Reinforcement Learning using Self-Supervised Attention Mechanisms in Duckietown — Figure 1. Two-Stage Framework for Interpretable Reinforcement Learning

Heatmaps showing basic attention patterns for Interpretable Reinforcement Learning using Self-Supervised Attention Mechanisms in Duckietown RL agents — Figure 2. Visual Attention Patterns in Reinforcement Learning

Graph comparing returns for Interpretable Reinforcement Learning with Self-Supervised Attention Mechanisms in Duckietown — Figure 3. Expert vs Mask Policy Return Comparison

Comparison of Interpretable Reinforcement Learning with Self-Supervised Attention Mechanisms applied to Atari and Duckietown tasks — Figure 4. RL Performance on Atari Using Masked Inputs

Saliency map comparison for Interpretable Reinforcement Learning with Self-Supervised Attention Mechanisms in Duckietown and Atari agents — Figure 6. Saliency Map Comparison Across Methods

Example of Interpretable Reinforcement Learning mask evaluation using Self-Supervised Attention Mechanisms in Duckietown — Figure 9. Mask Evaluation Example on Duckietown

Masked state sequences showing PPO, SAC, and TD3 agent behavior in Interpretable Reinforcement Learning with Self-Supervised Attention Mechanisms in Duckietown — Figure 12. Visual Comparison of PPO, SAC, and TD3 Agent Behavior

Masked state sequence comparison of Unet, RefineNet, DeepLab-v3, and FC DenseNet for Interpretable Reinforcement Learning using Self-Supervised Attention Mechanisms in Duckietown — Figure 14. Masked State Visualization Across Actor Architectures

Reinforcement Learning (RL) has enabled solving complex problems, especially in relation to visual perception in robotics. An outstanding challenges is that of allowing humans to make sense of the decision making process, so to enable deployment in safety-critical applications such as, e.g., autonomous driving. This work focuses on the problem of interpretable reinforcement learning in vision-based agents.

In particular, this research introduces a self-supervised framework for interpretable reinforcement learning in vision-based agents. The focus lies in enhancing policy interpretability by generating precise attention maps through Self-Supervised Attention Mechanisms (SSAM).

The method does not rely on external labels and works using data generated by a pretrained RL agent. A self-supervised interpretable network (SSINet) is deployed to identify task-relevant visual features. The approach is evaluated across multiple environments, including Atari and Duckietown.

Key components of the method include:

A two-stage training process using pretrained policies and frozen encoders
Attention masks optimized using behavior resemblance and sparsity constraints
Quantitative evaluation using FOR and BER metrics for attention quality
Comparative analysis with gradient and perturbation-based saliency methods
Application across various architectures and RL algorithms including PPO, SAC, and TD3

The proposed approach isolates relevant decision-making cues, offering insight into agent reasoning. In Duckietown, the framework demonstrates how visual interpretability can aid in diagnosing performance bottlenecks and agent failures, offering a scalable model for interpretable reinforcement learning in autonomous navigation systems.

Highlights - interpretable reinforcement learning for visual policies

Here is a visual tour of the implementation of interpretable reinforcement learning for visual policies by the authors. For all the details, check out the full paper.

Abstract

Here is the abstract of the work, directly in the words of the authors:

Deep reinforcement learning (RL) has recently led to many breakthroughs on a range of complex control tasks. However, the agent’s decision-making process is generally not transparent. The lack of interpretability hinders the applicability of RL in safety-critical scenarios. While several methods have attempted to interpret vision-based RL, most come without detailed explanation for the agent’s behavior. In this paper, we propose a self-supervised interpretable framework, which can discover interpretable features to enable easy understanding of RL agents even for non-experts. Specifically, a self-supervised interpretable network (SSINet) is employed to produce fine-grained attention masks for highlighting task-relevant information, which constitutes most evidence for the agent’s decisions. We verify and evaluate our method on several Atari 2600 games as well as Duckietown, which is a challenging self-driving car simulator environment. The results show that our method renders empirical evidences about how the agent makes decisions and why the agent performs well or badly, especially when transferred to novel scenes. Overall, our method provides valuable insight into the internal decision-making process of vision-based RL. In addition, our method does not use any external labelled data, and thus demonstrates the possibility to learn high-quality mask through a self-supervised manner, which may shed light on new paradigms for label-free vision learning such as self-supervised segmentation and detection.

Conclusion - interpretable reinforcement learning for visual policies

Here is the conclusion according to the authors of this paper:

In this paper, we addressed the growing demand for human-interpretable vision-based RL from a fresh perspective. To that end, we proposed a general self-supervised interpretable framework, which can discover interpretable features for easily understanding the agent’s decision-making process. Concretely, a self-supervised interpretable network (SSINet) was employed to produce high-resolution and sharp attention masks for highlighting task-relevant information, which constitutes most evidence for the agent’s decisions. Then, our method was applied to render empirical evidences about how the agent makes decisions and why the agent performs well or badly, especially when transferred to novel scenes. Overall, our work takes a significant step towards interpretable vision-based RL. Moreover, our method exhibits several appealing benefits. First, our interpretable framework is applicable to any RL model taking as input visual images. Second, our method does not use any external labelled data. Finally, we emphasize that our method demonstrates the possibility to learn high-quality mask through a self-supervised manner, which provides an exciting avenue for applying RL to self automatically labelling and label-free vision learning such as self-supervised segmentation and detection.

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Project Authors

Wenjie Shi received the BS degree from the School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan, China, in 2016. He is currently working toward the PhD degree in control science and engineering from the Department of Automation, Institute of Industrial Intelligence and System, Tsinghua University, Beijing, China.

Gao Huang (Member, IEEE) received the BS degree in automation from Beihang University, Beijing, China, in 2009, and the PhD degree in automation from Tsinghua University, Beijing, in 2015. He is currently an associate professor with the Department of Automation, Tsinghua University.

Shiji Song (Senior Member, IEEE) received the PhD degree in mathematics from the Department of Mathematics, Harbin Institute of Technology, Harbin, China, in 1996. He is currently a professor at the Department of Automation, Tsinghua University, Beijing, China.

Zhuoyuan Wang is currently working toward the BS degree in control science and engineering in the Department of Automation, Tsinghua University, Beijing, China.

Tingyu Lin received the B.S. degree and the Ph.D. degree in control system from School of Automation Science and Electrical Engineering in Beihang University in 2007 and 2014, respectively. He is now a Member of China Simulation Federation (CSF).

Cheng Wu received the M.Sc. degree in electrical engineering from Tsinghua University, Beijing, China, in 1966. He is currently a Professor with the Department of Automation, Tsinghua University.

Learn more

Duckietown is a platform for creating and disseminating robotics and AI learning experiences.

It is modular, customizable and state-of-the-art, and designed to teach, learn, and do research. From exploring the fundamentals of computer science and automation to pushing the boundaries of knowledge, Duckietown evolves with the skills of the user.

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

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.

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 Lane Tracking in Duckietown

Visual Feedback for Autonomous Lane Tracking in Duckietown

Posted on June 20, 2025 | by Duckietown Admin

General Information

Title: Lane Following with a Duckiebot Vehicle using Visual Feedback
Authors: Oscar Castro, Axel Céspedes, Roosevelt Ubaldo, Oscar E. Ramos
Institution: Universidad de Ingenieria y Tecnologia - UTEC Lima, Peru
Citation: O. Castro, A. Céspedes, R. Ubaldo and O. E. Ramos, "Lane Following with a Duckiebot Vehicle using Visual Feedback," 2019 IEEE Sciences and Humanities International Research Conference (SHIRCON), Lima, Peru, 2019, pp. 1-4, doi: 10.1109/SHIRCON48091.2019.9024875.

Visual Feedback for Autonomous Lane Tracking in Duckietown

How can vehicle autonomy be achieved by relying only on visual feedback from the onboard camera?

This work presents an implementation of lane following for the Duckietbot (DB17) using visual feedback as the only onboard sensor. The approach relies on real-time lane detection, and pose estimation, eliminating the need for wheel encoders.

The onboard computation is provided by a Raspberry Pi, which performs low-level motor control, while high-level image processing and decision-making are offloaded to an external ROS-enabled computer.

The key technical aspects of the implemented autonomy pipeline include:

Camera calibration to correct fisheye lens distortion;
HSV-based image segmentation for lane line detection;
Aerial perspective transformation for geometric consistency;
Histogram-based color separation of continuous and dashed lines;
Piecewise polynomial fitting for path curvature estimation;
Closed-loop motion control based on computed linear and angular velocities.

The methodology demonstrates the feasibility of using camera-based perception to control robot motion in structured environments. By using Duckiebot and Duckietown as the development platform, this work is another example of how to bridge the gap between real-world testing and cost-effective prototyping, making vehicle autonomy research more accessible in educational and research contexts.

Highlights - visual feedback for lane tracking in Duckietown

Here is a visual tour of the implementation of vehicle autonomy by the authors. For all the details, check out the full paper.

Visual feedback communication structure for vehicle autonomy in Duckietown using Duckiebot — ROS Node Communication

Histogram analysis for lane segmentation using visual feedback in Duckiebot for vehicle autonomy in Duckietown — Lane Histogram Segmentation

Visual feedback processing in Duckiebot for vehicle autonomy in Duckietown using HSV filtering and edge detection — HSV and Edge Detection

Polynomial segmentation using visual feedback in Duckiebot for vehicle autonomy in Duckietown — Polynomial Adjustment Techniques

: Real-Time Lane Overlay

: ROS Node Communication

: System Workflow Overview

: Image Distortion Correction

: Lane Histogram Segmentation

: Perspective Warping Process

: HSV and Edge Detection

: Polynomial Adjustment Techniques

: Duckiebot used at UTEC

Abstract

Here is the abstract of the work, directly in the words of the authors:

The autonomy of a vehicle can be achieved by a proper use of the information acquired with the sensors. Real-sized autonomous vehicles are expensive to acquire and to test on; however, the main algorithms that are used in those cases are similar to the ones that can be used for smaller prototypes. Due to these budget constraints, this work uses the Duckiebot as a testbed to try different algorithms as a first step to achieve full autonomy. This paper presents a methodology to properly use visual feedback, with the information of the robot camera, in order to detect the lane of a circuit and to drive the robot accordingly.

Conclusion - visual feedback for lane tracking in Duckietown

Here is the conclusion according to the authors of this paper:

Autonomous cars are currently a vast research area. Due to this increase in the interest of these vehicles, having a costeffective way to implement algorithms, new applications, and to test them in a controlled environment will further help to develop this technology. In this sense, this paper has presented a methodology for following a lane using a cost-effective robot, called the Duckiebot, using visual feedback as a guide for the motion. Although the whole system was capable of detecting the lane that needs to be followed, it is still sensitive to illumination conditions. Therefore, in places with a lot of lighting and brightness variations, the lane recognition algorithm can affect the autonomy of the vehicle.
As future work, machine learning, and particularly convolutional neural networks, is devised as a means to develop robust lane detectors that are not sensitive to brightness variation. Moreover, more than one Duckiebot is intended to drive simultaneously in the Duckietown.

Did this work spark your curiosity?

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Project Authors

Oscar Castro is currently working at Blume, Peru.

Axel Eliam Céspedes Duran is currently working as a Laboratory Professor of the Industrial Instrumentation course at the UTEC – Universidad de Ingeniería y Tecnología, Peru.

Roosevelt Jhans Ubaldo Chavez is currently working as a Laboratory Professor of the Industrial Instrumentation course at the UTEC – Universidad de Ingeniería y Tecnología, Peru.

Oscar E. Ramos is currently working toward the Ph.D. degree in robotics with the Laboratory for Analysis and Architecture of Systems, Centre National de la Recherche Scientifique, University of Toulouse, Toulouse, France.

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Duckietown is a platform for creating and disseminating robotics and AI learning experiences.

Pure Pursuit Lane Following with Obstacle Avoidance

Posted on June 16, 2025 | by Duckietown Admin

Pure Pursuit Lane Following with Obstacle Avoidance

Project Resources

Objective: Develop a lane following and obstacle avoidance system using the pure pursuit control for Duckiebots. (DB18)
Approach: Use an adaptive pure pursuit controller with dynamic speed tuning, integrated with vision-based deep-learning based object detection for obstacle-aware navigation.
Authors: Soroush Saryazdi, Dhaivat Bhatt, Robotics and Embodied AI Lab (REAL), Université de Montréal and is also affiliated with MILA.

Project highlights

Pure Pursuit Lane Following with Obstacle Avoidance - the objectives

Pure pursuit is a geometric path tracking algorithm used in autonomous vehicle control systems. It calculates the curvature of the road ahead by determining a target point on the trajectory and computing the required angular velocity to reach that point based on the vehicle’s kinematics.

Unlike proportional integral derivative (PID) control, which adjusts control outputs based on continuous error correction, pure pursuit uses a lookahead point to guide the vehicle along a trajectory, enabling stable convergence to the path without oscillations. This method avoids direct dependency on derivative or integral feedback, reducing complexity in environments with sparse or noisy error signals.

This project aims to implement a pure pursuit-based lane following system integrated with obstacle avoidance for autonomous Duckiebot navigation. The goal is to enable real-time tracking of lane centerlines while maintaining safety through detection and response to dynamic obstacles such as other Duckiebots or cones.

The pipeline includes a modified ground projection system, an adaptive pure pursuit controller for path tracking, and both image processing and deep learning-based object detection modules for obstacle recognition and avoidance.

Faster RCNN model architecture for pure pursuit and obstacle avoidance in Duckietown object detection — Faster RCNN Architecture with Feature Pyramid Network

Object detection results using Faster RCNN for pure pursuit and obstacle avoidance in Duckietown with bounding boxes for duckiebots and traffic cones — Faster RCNN Detection Output with Bounding Boxes

Duckietown object detection for pure pursuit and obstacle avoidance showing detected duckiebots and cones using deep learning — Detection Results for Obstacle Avoidance in Duckietown

The challenges and approach

The primary challenges in this project include robust target point estimation under variable lighting and environmental conditions, real-time object detection with limited computational resources, and smooth trajectory control in the presence of dynamic obstacles.

The approach involves modular integration of perception, planning, and control subsystems.

For perception, the system uses both classical image processing methods and a trained deep learning model for object detection, enabling redundancy and simulation compatibility.

For planning and control, the pure pursuit controller dynamically adjusts speed and steering based on the estimated target point and obstacle proximity. Target point estimation is achieved through ground projection, a transformation that maps image coordinates to real-world planar coordinates using a calibrated camera model. Real-time parameter tuning and feedback mechanisms are included to handle variations in frame rate and sensor noise.

Obstacle positions are also ground-projected and used to trigger stop conditions within a defined safety zone, ensuring collision avoidance through reactive control.

Looking for similar projects?

Check out the following works on path planning with Duckietown:

Pure Pursuit Lane Following with Obstacle Avoidance: Authors

Soroush Saryazdi is currently leading the Neural Networks team at Matic, supervised by Navneet Dalal.

Dhaivat Bhatt is currently working as a Machine learning research engineer at Samsung AI centre, Toronto.

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.

Reproducible Sim-to-Real Traffic Signal Control Environment

Posted on June 12, 2025 | by Duckietown Admin

General Information

Title: Reproducible and Low-cost Sim-to-Real Environment for Traffic Signal Control
Authors: Yiran Zhang, Khoa Vo, Longchao Da, Tiejin Chen, Xiaoou Liu, Hua Wei
Institution: Arizona State University, USA
Citation: Zhang, Y., Vo, K., Da, L., Chen, T., Liu, X. and Wei, H., 2025, May. Reproducible and Low-cost Sim-to-Real Environment for Traffic Signal Control. In Proceedings of the ACM/IEEE 16th International Conference on Cyber-Physical Systems (with CPS-IoT Week 2025) (pp. 1-2).

Reproducible Sim-to-Real Traffic Signal Control Environment

As urban environments become increasingly populated and automobile traffic soars, with US citizens spending on average 54 hours a year stuck on the roads, active traffic control management promises to mitigate traffic jams while maintaining (or improving) safety.

LibSignal++ is a Duckietown-based testbed for reproducible and low-cost sim-to-real evaluation of traffic signal control (TSC) algorithms. Using Duckietown enables consistent, small-scale deployment of both rule-based and learning-based TSC models.

LibSignal++ integrates visual control through camera-based sensing and object detection via the YOLO-v5 model. It features modular components, including Duckiebots, signal controllers, and an indoor positioning system for accurate vehicle trajectory tracking. The testbed supports dynamic scenario replication by enabling both manual and automated manipulation of sensor inputs and road layouts.

Key aspects of the research include:

Sim-to-real pipeline for Reinforcement Learning (RL)-based traffic signal control training and deployment
Multi-simulator training support with SUMO, CityFlow, and CARLA
Reproducibility through standardized and controllable physical components
Integration of real-world sensors and visual control systems
Comparative evaluation using rule-based policies on 3-way and 4-way intersections

The work concludes with plans to extend to Machine Learning (ML)-based TSC models and further sim-to-real adaptation.

Highlights - Reproducible Sim-to-Real Traffic Signal Control Environment

Here is a visual tour of the sim-to-real work of the authors. For all the details, check out the full paper.

Abstract

Here is the abstract of the work, directly in the words of the authors:

This paper presents a unique sim-to-real assessment environment for traffic signal control (TSC), LibSignal++, featuring a 14-ft by 14-ft scaled-down physical replica of a real-world urban roadway equipped with realistic traffic sensors such as cameras, and actual traffic signal controllers. Besides, it is supported by a precise indoor positioning system to track the actual trajectories of vehicles. To generate various plausible physical conditions that are difficult to replicate with computer simulations, this system supports automatic sensor manipulation to mimic observation changes and also supports manual adjustment of physical traffic network settings to reflect the influence of dynamic changes on vehicle behaviors. This system will enable the assessment of traffic policies that are otherwise extremely difficult to simulate or infeasible for full-scale physical tests, providing a reproducible and low-cost environment for sim-to-real transfer research on traffic signal control problems.

Results

Three traffic control policies were tested over a number of experiment repetitions, evaluating each time traffic throughput, average vehicle waiting times, and vehicle battery consumption. Standard deviations for all policies were found to be within acceptable ranges, leading the authors to confirm the ability of the testbed to deliver reproducible results within controlled environments.

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Project Authors

Yiran Zhang is associated with the Arizona State University, USA.

Khoa Vo is associated with the Arizona State University, USA.

Longchao Da is pursuing his Ph.D. at the Arizona State University, USA.

Tiejin Chen is pursuing his Ph.D. at the Arizona State University, USA.

Xiaoou Liu is pursuing her Ph.D. at the Arizona State University, USA.

Hua Wei is an Assistant Professor at the School of Computing and Augmented Intelligence, Arizona State University, USA.

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Duckietown is a platform for creating and disseminating robotics and AI learning experiences.

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.

Transformer Visual Control for Dynamic Obstacle Avoidance

Posted on May 10, 2025 | by Duckietown Admin

General Information

Title: An Adaptive Obstacle Avoidance Model for Autonomous Robots Based on Dual-Coupling Grouped Aggregation and Transformer Optimization
Authors: Yuhu Tang, Ying Bai, and Qiang Chen
Institution: Hefei University, China
Citation: Tang, Y., Bai, Y., & Chen, Q. (2025). An Adaptive Obstacle Avoidance Model for Autonomous Robots Based on Dual-Coupling Grouped Aggregation and Transformer Optimization. Sensors, 25(6), 1839. https://doi.org/10.3390/s25061839

Transformer Visual Control for Dynamic Obstacle Avoidance

This work details a transformer visual control approach for autonomous robotic obstacle avoidance in dynamic environments. It introduces the GAS-H-Trans model, which integrates a dual-coupling grouped aggregation strategy with transformer-based attention mechanisms.

Key components of the approach include grouped spatial feature aggregation, Harris hawk optimization (HHO) for parameter tuning, and semantic segmentation for real-time visual perception. The output of the segmentation is used to compute potential fields for navigation. An artificial potential field (APF) method, further optimized using particle swarm optimization (PSO), enhances obstacle avoidance. The system was evaluated in Unity3D virtual environments and on datasets including KITTI, and ImageNet.

The model architecture improves local and global feature extraction, enabling adaptive navigation. Simulation results demonstrate that GAS-H-Trans outperforms baseline models in segmentation accuracy and avoidance reliability. The implementation uses Transformer structures, self-attention, and heuristic optimization for enhanced environmental understanding.

Experiments using Duckietown-based simulations confirm that the proposed Transformer Visual Control strategy with GAS-H-Trans significantly improves obstacle avoidance reliability with respect to typical approaches.

Highlights - Transformer Visual Control for Dynamic Obstacle Avoidance

Here is a visual tour of this work. For all the details, check out the full paper.

GAS-H-Trans Transformer Visual Control system architecture for Duckietown robotic navigation — Figure 1. GAS-H-Trans Framework Architecture

Transformer Visual Control model architecture using GAS-H-Trans and grouped aggregation in Duckietown simulation — Figure 2. Detailed Architecture of GAS-H-Trans Model

PSO-optimized artificial potential field for Transformer Visual Control in Duckietown simulation — Figure 3. Artificial Potential Field Optimization Flow

Transformer Visual Control model accuracy chart in Duckietown obstacle avoidance task — Figure 4. Model Accuracy Comparison Across Epochs

Swin Transformer comparison with GAS-H-Trans in Transformer Visual Control on Duckietown data — Figure 6. Swin Transformer vs GAS-H-Trans Performance

Duckietown virtual environment dataset creation for Transformer Visual Control experiments — Figure 7. Dataset Creation Process

GAS-H-Trans Transformer Visual Control segmentation accuracy in Duckietown virtual dataset — Figure 8. Image Segmentation Accuracy in Duckietown Scene

Segmentation output of Transformer Visual Control with JSON mapping in Duckietown task — Figure 9. Segmentation Mask and Data Structure

Transformer Visual Control path planning with GAS-H-Trans and PSO in Duckietown — Figure 10. Obstacle Avoidance in Duckietown Environment

Comparison of traditional and PSO-optimized APF for Transformer Visual Control in Duckietown — Figure 11. Effect of PSO Optimization on APF

Enhanced Transformer Visual Control robot navigation path in Duckietown simulation — Figure 12. Optimized Obstacle Avoidance Path

Transformer Visual Control success rate with traditional vs PSO-optimized APF in Duckietown — Figure 13. Obstacle Avoidance Success Rate

Abstract

In the author’s words:

Accurate obstacle recognition and avoidance are critical for ensuring the safety and operational efficiency of autonomous robots in dynamic and complex environments. Despite significant advances in deep-learning techniques in these areas, their adaptability in dynamic and complex environments remains a challenge. To address these challenges, we propose an improved Transformer-based architecture, GAS-H-Trans.

This approach uses a grouped aggregation strategy to improve the robot’s semantic understanding of the environment and enhance the accuracy of its obstacle avoidance strategy. This method employs a Transformer-based dual-coupling grouped aggregation strategy to optimize feature extraction and improve global feature representation, allowing the model to capture both local and long-range dependencies.

The Harris hawk optimization (HHO) algorithm is used for hyperparameter tuning, further improving model performance. A key innovation of applying the GAS-H-Trans model to obstacle avoidance tasks is the implementation of a secondary precise image segmentation strategy. By placing observation points near critical obstacles, this strategy refines obstacle recognition, thus improving segmentation accuracy and flexibility in dynamic motion planning. The particle swarm optimization (PSO) algorithm is incorporated to optimize the attractive and repulsive gain coefficients of the artificial potential field (APF) methods.

This approach mitigates local minima issues and enhances the global stability of obstacle avoidance. Comprehensive experiments are conducted using multiple publicly available datasets and the Unity3D virtual robot environment. The results show that GAS-H-Trans significantly outperforms existing baseline models in image segmentation tasks, achieving the highest mIoU (85.2%). In virtual environment obstacle avoidance tasks, the GAS-H-Trans + PSO-optimized APF framework achieves an impressive obstacle avoidance success rate of 93.6%. These results demonstrate that the proposed approach provides superior performance in dynamic motion planning, offering a promising solution for real-world autonomous navigation applications.

Conclusion - Transformer Visual Control for Dynamic Obstacle Avoidance

Here is the author’s summary and overview of lessons learned from this work:

In this study, we proposed the GAS-H-Trans framework for image segmentation and dynamic obstacle avoidance in autonomous robots. The key contributions are summarized as follows. (1) Dual-coupling grouped aggregation strategy: A Transformer-based dualcoupling grouped aggregation method optimizes feature extraction and enhances global feature representation, thereby improving the model’s perception performance in dynamic motion planning. (2) Harris hawk optimization (HHO): The integration of the HHO algorithm into the GAS-Trans framework optimizes the number of Transformer layers and iterations, improving model accuracy and reducing computational costs. (3) PSOoptimized artificial potential field (APF): We integrated the PSO algorithm with APF to optimize the attractive and repulsive gain coefficients, addressing local minima issues and enhancing the global stability of the obstacle avoidance system.

This study also proposes a secondary precise image segmentation strategy. By setting the observation points near critical obstacles for fine-tuned segmentation, the flexibility and accuracy of the segmentation model’s environmental perception are effectively enhanced, thereby improving the robot’s obstacle avoidance capabilities.

Through the integration of PSO-optimized APF with image segmentation, the GAS-HTrans + PSO-optimized APF framework demonstrated significant improvements in obstacle avoidance. In the experimental validation of this study, the obstacles remained static throughout the navigation process. Using this method, the autonomous robot dynamically adjusted its obstacle avoidance trajectory based on segmented environmental features. This integration significantly enhanced environmental perception capabilities and the accuracy of obstacle avoidance decisions, enabling more efficient navigation in static obstacle environments.

Extensive experiments on publicly available datasets (Duckiebot, KITTI, ImageNet) and in the Unity3D virtual robot environment validate the effectiveness of the proposed framework. The GAS-H-Trans framework outperformed traditional models in image segmentation tasks, achieving the highest mIoU of 85.2%. Furthermore, in virtual obstacle avoidance experiments, the GAS-H-Trans + PSO-optimized APF framework achieved an obstacle avoidance success rate of 93.6%.

These results effectively validate the proposed strategy, which combines secondary image segmentation from GAS-H-Trans with the PSO-optimized APF method, significantly improving obstacle avoidance performance in dynamic motion planning. Additionally, the GAS-H-Trans framework has the potential to be extended to fully dynamic environments by incorporating real-time object tracking and adaptive obstacle modeling. However, some limitations exist. The majority of the experiments were conducted in simulated environments, and future research will focus on validating the framework in real-world scenarios and improving real-time performance.

Additionally, the integration of multi-modal sensor data (such as LiDAR and ultrasonic sensors) will be an important direction for future work to further enhance environmental perception and robustness.

In conclusion, the new framework offers an innovative solution for autonomous robot obstacle avoidance in dynamic motion planning. Its powerful environmental perception and obstacle avoidance performance demonstrate significant potential for practical applications. With further optimization and real-world validation, this framework will play a crucial role in the future development of autonomous navigation and robotics technology.

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Project Authors

Yuhu Tang is affiliated with the School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China.

Ying Bai is affiliated with the School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China.

Qiang Chen is affiliated with School of Electrical Engineering and Automation
National and Local Joint Engineering Laboratory for Renewable Energy Access to Grid Technology, Hefei University of Technology, Hefei, China, Hefei University, Hefei 230601, China.

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Duckietown is a platform for creating and disseminating robotics and AI learning experiences.

Extended Kalman Filter (EKF) SLAM for Duckiebots

Posted on April 26, 2025 | by Duckietown Admin

Extended Kalman Filter (EKF) SLAM for Duckiebots

Project Resources

Objective: Implement an Extended Kalman Filter (EKF) SLAM system to estimate accurate Duckiebot pose and landmark positions in Duckietown environments.
Approach: Fuse odometry and AprilTag landmark detections using EKF to achieve robust localization and mapping for small-scale autonomous Duckiebots.
Authors: Amir Hossein Zamani, Léonard Oest O'Leary, Kevin Lessard from the University of Montreal.

Project highlights

In SLAM, everything that can drift will drift, and the role of the filter is to drift more slowly than entropy.

Extended Kalman Filter (EKF) SLAM system architecture overview — Figure 1. EKF-SLAM Architecture Overview

Extended Kalman Filter (EKF) SLAM input and output fusion diagram — Figure 2. Extended Kalman Filter (EKF) SLAM input and output flow diagram

Extended Kalman Filter (EKF) SLAM odometry before incorporating landmarks — Figure 5. Odometry before incorporating landmarks

Extended Kalman Filter (EKF) SLAM improved versus simple interpolation for trajectory estimation — Figure 6. EKF-SLAM Trajectory Interpolation

The equations describe how the Duckiebot's position and orientation (x,y,θ) evolve over time based on its linear velocity (v) and angular velocity (ω). — Figure 7. EKF's prediction-correction workflow

The diagrams illustrates the coordinate transformations and the pose estimation workflow. — Figure 9. EKF-SLAM AprilTags Pose Estimation

The visual illustrates the EKF's prediction-correction workflow, showing how odometry data predicts the next state and April tag measurements refine it. By iteratively fusing these data sources, the EKF achieves accurate localization and mapping, even under noisy or incomplete sensor inputs. — Figure 10. EKF-SLAM Motion Model Equations

Figure 11. EKF-SLAM full version (motion+measurement model)

Figure 12. EKF-SLAM measurement model (without motion model)

Figure 13. EKF-SLAM motion model (without measurement model)

Figure 14. Pose estimation using a circular interpolation

Figure 15. Pose estimation using a circular interpolation_DELTA_TIME=2

Figure 16. Pose estimation using a linear interpolation

Figure 17. Pose estimation using a linear interpolation_DELTA_TIME=2

Extended Kalman Filter (EKF) SLAM for Duckiebots - the objectives

This SLAM-Duckietown project addresses a famous challenge in robotics: concurrently estimating the agent’s pose and mapping the environment under uncertainty.

This project implements an Extended Kalman Filter (EKF) SLAM algorithm on Duckiebots (DB21-J4), combining odometry from wheel encoders and landmark observations from April tags.

The objective is to maintain an evolving posterior over the Duckiebot’s pose (x,y,θ) and landmark positions by recursively integrating noisy control inputs and observations.

This upgrade shifts Duckiebots from open-loop dead reckoning units into closed-loop, state-estimating agents. For Duckietown, it reinforces its use as an experimental ground for real-world robotics challenges, including data association, observability, filter consistency, and multi-sensor fusion.

The challenges and approach

The system applies the EKF-SLAM pipeline in two stages: motion prediction and measurement correction.

Prediction propagates the robot’s belief through a non-holonomic kinematic model under process noise, using arc-based interpolation to reduce discretization error.

Correction incorporates April tag detections via a Perspective-n-Point (PnP) solution, updating the state with landmark-relative observations under observation noise. The state vector grows dynamically as new landmarks are observed, and the covariance matrix tracks both robot and landmark uncertainty.

The technical challenges include maintaining filter consistency under linearization errors, ensuring landmark observability despite partial fields of view, and synchronizing asynchronous data from wheel encoders, camera frames, and Vicon ground-truth captures.

Moreover, AprilTag detection is constrained by lighting artifacts and pose ambiguity at shallow viewing angles, introducing non-Gaussian errors that the EKF must approximate linearly.

Moreover, tuning noise parameters presents the classical tradeoff: too little noise leads to overconfidence and divergence; too much noise leads to filter paralysis. Deployment exposes the systemic difference between simulation and physical experiments: real Duckiebots do not move with perfect kinematics, cameras suffer from radial distortion, and computation suffers from non-deterministic latency.

In SLAM, everything that can drift will drift, and the role of the filter is to drift more slowly than entropy.

Did this work spark your curiosity?

Check out the follow works on path planning with Duckietown:

Extended Kalman Filter (EKF) SLAM for Duckiebots: Authors

AmirHossein Zamani was a former Duckietown student, and currently, he is pursuing his Ph.D. in Computer Science at Mila (Quebec AI Institute) and Concordia University, Canada. He is also working as an AI Research Scientist Intern at Autodesk in Montreal, Canada.

Léonard Oest O’Leary was a former Duckietown student, and currently, he is pursuing his Master of Science in Computer Science at the University of Montreal, Canada.

Kevin Lessard was a former Duckietown student, and currently, he is pursuing his Master of Science in Machine Learning at Mila – Quebec AI Institute in Montreal, Canada.

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.

VAE-Based Out-of-Distribution Detectors for Embedded Deployment

VAE-Based Out-of-Distribution Detectors for Embedded Systems

Posted on April 11, 2025 | by Duckietown Admin

General Information

Title: Compressing VAE-Based Out-of-Distribution Detectors for Embedded Deployment
Authors: Aditya Bansal, Michael Yuhas, Arvind Easwaran
Institution: Nanyang Technological University, SIngapore
Citation: Bansal, A., Yuhas, M., & Easwaran, A. (2024). Compressing VAE-Based Out-of-Distribution Detectors for Embedded Deployment. In Proceedings - 2024 IEEE 30th International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2024 (pp. 37-42).

VAE-Based Out-of-Distribution Detectors for Embedded Systems

Out-of-distribution (OOD) detection is essential for maintaining safety in machine learning systems, especially those operating in the real world. It helps identify inputs that differ significantly from the training data, which could lead to unexpected or unsafe behavior.

Variational Autoencoders (VAEs) are neural networks that compress input data into a smaller latent space (a compact set of features) and reconstructs the input from this compressed version.

In OOD detection, if the reconstruction fails or doesn’t fit the expected latent space, the input is flagged as unfamiliar, i.e., out-of-distribution. While VAEs are effective, they are computationally expensive, making them hard to deploy on small, embedded devices like Duckiebots.

To solve this challenge, building upon previous work (Embedded Out-of-Distribution Detection on an Autonomous Robot Platform), the researchers applied three model compression techniques:

Pruning: Removes low-importance weights or neurons to shrink and speed up the model.
Knowledge distillation: Trains a smaller “student” model to mimic a larger “teacher” model.
Quantization: Lowers numerical precision (e.g., from 32-bit to 8-bit) to save memory and improve speed.

Two VAE-based OOD detectors were evaluated:

β-VAE: A variant of VAE that learns more interpretable latent features (controlled by a parameter called β).
Optical Flow Detector: Analyzes how pixels move across video frames to detect unusual motion.

Both models were trained and tested using data collected in Duckietown, and the models were measured using Area under the Receiver Operating Characteristic Curve (AUROC), which shows how well the model separates known from unknown inputs, memory footprint, and execution latency. The compressed models achieved faster inference times, smaller memory usage, and only minor drops in detection accuracy.

Highlights - VAE-Based Out-of-Distribution Detectors for Embedded Systems

Here is a visual tour of the work of the authors. For all the details, check out the full paper.

Visual control architecture in Duckietown showing VAE OOD compression methodology — Figure 1. Design Methodology for VAE Compression

Visual control models in Duckietown showing β-VAE and Optical Flow OOD detectors — Figure 2. OOD Detector Block Diagrams

Visual control metrics in Duckietown with β-VAE under varying sparsity — Figure 4. β-VAE AUROC vs. Sparsity

Visual control performance in Duckietown using various compression methods — Figure 5. Compression Impact on β-VAE

Visual control optimization in Duckietown using compressed optical flow models — Figure 6. Knowledge Distillation Effects on Optical Flow Model

: Figure 1. Design Methodology for VAE Compression

: Figure 2. OOD Detector Block Diagrams

: Figure 3. Reconstructed Images from β-VAE

: Figure 4. β-VAE AUROC vs. Sparsity

: Figure 5. Compression Impact on β-VAE

: Figure 6. Knowledge Distillation Effects on Optical Flow Model

: Figure 7. Sparsity Impact on Optical Flow Models

Abstract

In the author’s words:

Out-of-distribution (OOD) detectors can act as safety monitors in embedded cyber-physical systems by identifying samples outside a machine learning model’s training distribution to prevent potentially unsafe actions. However, OOD detectors are often implemented using deep neural networks, which makes it difficult to meet real-time deadlines on embedded systems with memory and power constraints. We consider the class of variational autoencoder (VAE) based OOD detectors where OOD detection is performed in latent space, and apply quantization, pruning, and knowledge distillation.

These techniques have been explored for other deep models, but no work has considered their combined effect on latent space OOD detection. While these techniques increase the VAE’s test loss, this does not correspond to a proportional decrease in OOD detection performance and we leverage this to develop lean OOD detectors capable of real-time inference on embedded CPUs and GPUs. We propose a design methodology that combines all three compression techniques and yields a significant decrease in memory and execution time while maintaining AUROC for a given OOD detector.

We demonstrate this methodology with two existing OOD detectors on a Jetson Nano and reduce GPU and CPU inference time by 20% and 28% respectively while keeping AUROC within 5% of the baseline.

Conclusion - VAE-Based Out-of-Distribution Detectors for Embedded Systems

Here are the conclusions from the author of this paper:

We explored different neural network compression techniques on β-VAE and optical flow OOD detectors using a mobile robot powered by a Jetson Nano. Based on our analysis of results for quantization, knowledge distillation, and pruning, we proposed a design strategy to find the model with the best execution time and memory usage while maintaining some accuracy metric for a given VAE-based OOD detector. We successfully demonstrated this methodology on an optical flow OOD detector and showed that our methodology’s ability to aggressively prune and compress a model is due to the unique attributes of VAE-based OOD detection.

Despite our methodology’s good performance, it requires access to OOD samples at design time to act as a crossvalidation set. In our case study, we assume OOD samples arise from a particular generating distribution, but this may not be the case in general. Furthermore, it only guides the search for a faster architecture, but does not guarantee the optimum result. Nevertheless, we believe having a design methodology that combines quantization, knowledge distillation, and pruning allows engineers to exploit the combined powers of these techniques instead of considering them individually.

Project Authors

Aditya Bansal is currently working as a Machine Learning Engineer at Adobe, United States.

Michael Yuhas is currenly working as a Research Assistant at Nanyang Technological University, Singapore.

Arvind Easwaran is an Associate Professor at Nanyang Technological University, Singapore.

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Duckietown is a platform for creating and disseminating robotics and AI learning experiences.

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.