12 | | * **MIMIC (Miniature Intersection Motion Imitation Car)** - Develop a remote-controllable car which can be used to mimic the path taken by a car in the real intersection. For this platform, omni-wheels will be used to allow for real-time corrections to the path. The platform will be developed using Robot Operating System (ROS). Students will need to prototype hardware and develop software to extract the path from video of the real intersection, translate that into a path for the miniature intersection, and produce a set of commands for the car. |
| 12 | ||= RASCAL \\ (Robotic Autonomous Scale Car for Adaptive Learning) =||= MIMIC \\ (Miniature Intersection Motion Imitation Car) =|| |
| 13 | || Continue work on the miniature autonomous car hardware project developed last summer (see the project page here: [https://www.orbit-lab.org/wiki/Other/Summer/2022/Hardware]). \\\\ The goal for this summer is to finish prototyping the hardware and develop drivers to control the car with ROS. \\ If time allows, once the ROS integration is complete, we will be able to develop and test machine learning models to control the car. || Develop a remote-controllable car which can be used to mimic the path taken by a car in the real intersection. \\\\ For this platform, omni-wheels will be used to allow for real-time corrections to the path.\\ The platform will be developed using Robot Operating System (ROS). \\ Students will need to prototype hardware and develop software to extract the path from video of the real intersection, translate that into a path for the miniature intersection, and produce a set of commands for the car. || |
14 | | * **RASCAL (Robotic Autonomous Scale Car for Advanced Learning)** - Continue work on the miniature autonomous car hardware project developed last summer (see the project page here: [https://www.orbit-lab.org/wiki/Other/Summer/2022/Hardware]). The goal for this summer is to finish prototyping the hardware and develop drivers to control the car with ROS. If time allows, once the ROS integration is complete, we will be able to develop and test machine learning models to control the car. |
| 15 | === Week 1 === |
| 16 | [https://docs.google.com/presentation/d/1xI1j6bT0DHHZn49lkX46HIa0S8HK6EFORhHdp0s7ubw/edit?usp=sharing Week 1 Slides] |
| 17 | |
| 18 | === Week 2 === |
| 19 | [https://docs.google.com/presentation/d/1iGiPmPWDiUlBCvQfNxJomSGOvTsIeXy3BlOZD4x3UdU/edit?usp=drive_link Week 2 Slides] |
| 20 | |
| 21 | === Week 3 === |
| 22 | [https://docs.google.com/presentation/d/1P8vwjlIrdJqn0JxDZvIFFVKU4G3govuB-HsCFOkXvmc/edit?usp=drive_link Week 3 Slides] |
| 23 | |
| 24 | === Week 4 === |
| 25 | [https://docs.google.com/presentation/d/1e9mE7dmLPfX8NrHO0R2h8viQOAZ2b9qCVxmgUfG48ig/edit?usp=drive_link Week 4 Slides] |
| 26 | |
| 27 | === Week 5 === |
| 28 | [https://docs.google.com/presentation/d/1YZyKdFTEkPWX_MtU9aXOE59pvN-EeOz8yq32NEeIPb8/edit?usp=drive_link Week 5 Slides] |
| 29 | |
| 30 | === Week 6 === |
| 31 | [ Week 6 Slides] |
| 32 | |
| 33 | === Week 7 === |
| 34 | [ Week 7 Slides] |
| 35 | |
| 36 | === Week 8 === |
| 37 | [ Week 8 Slides] |
| 38 | |
| 39 | === Week 9 === |
| 40 | [ Week 9 Slides] |