Duckietown

Course slides

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• You can find these slides in: https://tinyurl.com/y3kwbr9v

Duckietown

Calendar

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Date # Lecture09-18 1 Course Introduction

09-23 2 Box ceremony / Project Presentation

09-25 3 Intro to Autonomy

09-30 4 Projects Kickoff

10-02 5 Software architectures

10-07 6 Software development

10-09 7 Camera modeling and calibration

10-14 8 Perception / CV basic algorithms

10-16 9 Machine learning basics

10-21 10 Machine learning - perception

10-23 11 Projects status updates

10-28 12 Localization (bayes filtering)

10-30 13 Lane filter / notebook exercises

Date # Lecture11-04 14 Dynamics modeling

11-06 15 Control

11-11 16 Control / Notebook exercises

11-13 17 Planning I

11-18 18 Planning II

11-20 19 Imitation / Reinforcement Learning

11-25 20 Projects status updates

11-27 21 (buffer)

12-02 22 (buffer)

12-04 23 (buffer)

12-09 24 Autonomous Mobility on Demand

12-11 25 TA office hours

12-16 26 TA office hours

12-18 27 Final project presentation

Projects

Duckietown

Infrastructure and Tools

• Google Drive: Group folder on google drive - mentor will create during first meeting and give you access.

• Github: github.com/duckietown-ethz

• Dockerhub: cloud.docker.com/u/duckietownethz/

• JIRA and Confluence (optional): you received an invitation to the email provided in the application form

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• Lab / Room access: You will have 24/7 access to ML K 31 - you will have a working space, connection, Duckietown (city) and Robotarium infrastructure.

• Please be mindful of the IDSC scientists working in the adjacent rooms.

Duckietown

1. GOTO-N: Fleet autopilot

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Jacopo

Amaury, Tomaszproj-goto-n

Objective: Procedure to drive a fleet of Duckiebots autonomously to specified "starting" positions, for repeatability in AIDO submission evaluations

Reset demo run initial conditions (permutation accepted, "tile" precision)

Drive n Duckiebots to n initial positions (permutation accepted, up to half-tile precision)

Drive n Duckiebots to and from n parking positions, not necessarily "in town"

' Demetris ChrysostomouMarc-Philippe Frey Alexander Hatteland

Duckietown

3. LF-P: Static Obstacle Avoidance

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Objective: identify static obstacles (pedestrians) on the lane and plan around them in a computationally efficient way. You can assume no other vehicle is present.

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Andrea

Gianmarco, Rohitproj-lfp

bronze expectations: reliably stop

silver expectations: pass the obstacle if possible

gold expectations: reliable demo becomes part of baseline

' Lison AbecassisEnnio Filcicchia Johannes Lienhart

Duckietown

5. City Rescue

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JacopoGioele, Amaury, Benproj-cityrescue

Objective: Provide a solution for putting Duckiebots operating in a Robotarium back on track in case of "accidents"

Detect a Duckiebot in distress and open loop rescue it using watchtowers

Detect a Duckiebot in distress and open loop rescue it using watchtowers and emergency manoeuvres

Detect a Duckiebot in distress and rescue it in close loop

' Carl BiagoschShengje Hu Martin Ziran Xu

Duckietown

6. GOTO-1 / Global localization

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Objective: allow the Duckiebot to continuously localize in a global map of the city and navigate to a certain point. The map is assumed known.

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Andrea

Gioele, Amauryproj-goto-1

bronze expectations: tile-level localization and GOTO performance.

silver expectations: reliable sub-tile localization

gold expectations: map enrichment with additional features

' Johannes BoghaertWencan Huang Xiao'ao Song

Duckietown

7. LF-IVOP: Robust object detection

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Julian

Aleksandar, Gianmarcoproj-lfivop-ml

Objective: Leverage ML and computer vision insights to detect objects independent of lighting conditions.

Show improvements to previous OD baseline in difficult lighting conditions

Additionally, make it run reliably on different Duckiebots

Finally get it to work reliably within intersections

' Harleen HanspalStefan Lionar Maximilian Stölzle

Duckietown

8. Parking

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Objective: the name says it all. This project covers the planning, implementation and parking lot / spaces hardware design to implement parallel parking in Duckietown.

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Andrea

Tomasz, Gioeleproj-parking

bronze expectations : demonstrate kinodynamic planning on the platform

silver expectations: implementation becomes part of Duckietown demos

gold expectations: parking lot design becomes part of Duckietown

' Linus LinggTrevor Philips Vincenzo Polizzi

Duckietown

9. LF-I-ML : ML-based lane following

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Objective: Leverage ML and computer vision insights to create a faster, more reliable lane following behavior. Special focus is on navigating intersections in a reliable way.

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Julian

Aleksandar, Rohitproj-lfi-ml

Run lane following, faster and more reliable for different lighting conditions

Additionally, make it run reliably on different Duckiebots

Finally get it to work reliably within intersections

' Etienne WaltherElias Wicki Oliver Widler

Duckietown

10. LF-VOP: Dynamic Obstacle Avoidance

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Objective: Traffic flow in both directions, pedestrians crossing the road - driving safely but confidently in Duckietown starts to become challenging. This project will address free form solutions for the LF-VOP challenge.

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Andrea

Rohit, Gianmarcoproj-lfvop

bronze expectations: overtake duckie (25% lane occupancy)

silver expectations: overtake parked Duckiebot (50% lane occupancy)

gold expectations: pass safely a broken Duckiebot (100% lane occupancy)

' Fidel Esquivel EstayNikolaj Witting Paula Wulkop

Duckietown

11. LF: Adaptive Lane Following

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Objective: Calibrate a Duckiebot while driving down the lane. The challenge is to do in an efficient and optimized way so that it can run on the bot itself.

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Jacopo

Rohit, Aleksandarproj-lf-adaptive

Calibrate gain / trim parameters

Calibrate Duckiebot kinematic model parameters on the fly

Calibrate Duckiebot and camera model parameters on the fly

istockphoto.com

' Simone ArrighiniPietro Griffa Yannick Strümpler

Duckietown

12. LF-I: Improve intersection navigation

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Objective: Improve intersection navigation, any way you want!

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Andrea

Merlin, Amauryproj-lfi

bronze expectations: improvement on baseline

silver expectations: significant improvement on baseline

gold expectations: integration with controlled intersections

' Sebastian Nicolas GilesChristian Leopoldseder Matthias Wieland

Duckietown

Reminder: Project Timeline and Deliverables• First Meeting Documents — Deadline: October 4th (Friday 23:59)

Team Overview: group members, logistics, meetings, taskPreliminary Design Document: mission & scope, problem definition, approach, project planning We will give you templates for this and other deliverables.

• First Status Update — Class presentation: October 23rd

• Second Status Update — Class presentation: November 25th

• Final Presentation and deliverables — Live Demo: December 18th

• Duckumentation: docs.duckietown.org/daffy/<stay-tuned>

• Commented and documented code; unit tests, etc.

• 2 min. high quality video (“trailer”).

• Challenges related projects are impartially evaluated in the Robotarium.

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Duckietown

Homework

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• [RH1] Assemble, connect and operate a Duckiebot

• [RH2] Basic development in Duckietown

• Hands-on Robotics Development using Duckietown

Deadline: Tuesday Oct. 1st, 23:59.

• First Meeting Documents — Deadline: Friday Oct. 4th, 23:59. Team Overview: group members, logistics, meetings, taskPreliminary Design Document: mission & scope, problem definition, approach, project planning

Make a copy of these documents and place them in your Google Drive folder before filling in

Duckietown

Getting projects started

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• First meeting documents review

• Get access to:

• Google drive project folder

• Github project repository

• JIRA project (optional)

• Slack project channel

• Duckietown Private Calendar • add your meeting to it

• persistent zoom link (ask Jacopo) • add to your meeting on calendar

• Meet your group (today!) and start filling up the docs and the above list!