Difference between revisions of "Jessi"

From RoboJackets Wiki
Jump to navigation Jump to search
Line 122: Line 122:
Image:jessiifordtour.jpg | Jessii team on a Ford tour
Image:jessiifordtour.jpg | Jessii team on a Ford tour
Image:jessiidesignpresentation.jpg | Jessii team giving design presentation
Image:jessiidesignpresentation.jpg | Jessii team giving design presentation
Image:jessii-backwards-run.png | Jessii's route and map generated during the backwards run

Latest revision as of 22:04, 9 September 2019

Jessii alt text
Year Of Creation 2017-2018
Latest Revision Jessii
Revision Years 2018-2019
Information and Statistics
Farthest Distance 16 ft
Fastest Time n/a
Highest Finish AutoNav n/a
Highest Finish Design 1st


IGVC 2018

  • Results:
    • Distance: 5 ft
    • Design Competition Placement: 2nd (401.33 / 480 points in finalist round; 1299/1400 points in group stages)
    • AutoNav Competition Placement:

IGVC 2019

  • Results:
    • Distance: 16 ft
    • Design Competition Placement: 1st
    • AutoNav Competition Placement:



At Competition

Mechanical Design & Issues

Electrical Design & Issues

Jessi Electrical Overview

Software Design & Issues


At Competition

Though the team was well-prepared going into 2019 IGVC, several issues came up during competition. On the first day, the mbed was experiencing issues and causing the computer to brownout. The mbed was fried while attempts were being made to fix the problem. Eventually this was solved with a new mbed and a large capacitor, which prevented brownouts from happening, though the whole process of fixing the robot took up the first day of the competition. During the second day, major progress was made; the team was 4th to qualify, which was the earliest Georgia Tech has ever qualified in IGVC. Additionally, the robot had a few successful runs on the course. Most notably, the great backwards run tragedy of 2019 happened. During this run, the robot turned itself around and went BACKWARDS on the course, though it went extremely far. This ended up being (absolute-value) the second longest run achieved by any robot in the competition this year, though it didn't count towards our score since it was backwards.

Mechanical Design & Issues

We tried to make Jessii a more modular and accessible version of Jessi.

Electrical Design & Issues

For Jessii, the electrical team focused on iterating on several components of Jessi's system to improve weaknesses seen at the prior competition.

  • Custom Computer and Improved Sensors
    • To improve computational capabilities and performance we upgraded our computer and other sensors
      • Intel i7-8700 3.2GHz 6 Core Processor, Nvidia GTX 1060 GPU, 32GB RAM
        • GPU was chosen for CUDA support
        • Hotswap capability was brought using a PCB though not implemented before competition
      • Velodyne Puck VLP-16 3D LiDAR
      • YostLabs 3-Space Micro USB IMU
  • E-Stop
    • E-Stop circuitry was consolidated onto E-Stop PCB
    • Relay was used instead of a transistor used in version 1.0
  • Ethernet Communication Protocol
    • The logic board, the custom PCB which handles motor control switched from a virtual serial interface from MBed to a custom Ethernet communication protocol for robustness

Software Design & Issues

Design & Improvements

The software team performed a large overhaul of the codebase, focusing primarily on increasing the robustness of various algorithms used.

  • Perception
    • We used the VLP-16 3D Lidar this year. We tried both RANSAC plane detection and Progressive Morphological Filters, but ended up using RANSAC for competition as it was more tested.
    • We switched from the FCN-8 architcture to the U-net architecture for the neural net for image segmentation.
  • Localization
  • Mapping
    • We kept with the same occupancy grid mapping strategy. However, we revamped the actual algorithm to be a lot more mathematically sound by using a binary bayes filter instead of simply incrementing a counter. In addition, we included a proper sensor model for our various sensors, so that knowledge of both free space and occupied space are used when mapping.
  • Global Path Planning
    • The global path planning algorithm was changed from a janky A* implementation to a janky Field D* algorithm, which yeilded smoother paths.
  • Local Path Planning
    • The local path planning algorithm still used the same smooth control law as before. However, instead of using a pure-pursuit style algorithm to follow the path, the robot paths directly to waypoints on the path and performs motion profiling, so that the robot is able to stay on the path.


We faced a few issues this year:

  • Robustness of line detection
    • We moved from a pretrained FCN8 to a U-net architecture that didn't have pretraining. As a result, on the final day, our neural net had a decent amount of false negatives.
  • Computational efficiency of global path planning
    • We used the Field D* for global path planning. However, it wasn't computationally efficient, and failed to find a path in a reasonable amount of time at the beginning of the run, when it saw lines that extended a fair distance in front.
  • Because we were seeing lines in front and not behind us and the first waypoint was located to the side of the starting location, the global path planner planned a path that went behind us, leading to the famous reverse run.

Additional Information

Team Members