20190930 - 02 - ETHZ - Project Kickoff...bronze expectations: tile-level localization and GOTO...
Transcript of 20190930 - 02 - ETHZ - Project Kickoff...bronze expectations: tile-level localization and GOTO...
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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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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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!