Difference between revisions of "2021FallTeam6"

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Our goal was to develop a system of nodes in ROS to enable vehicle to vehicle communication between 2 cars. We took inspiration for our project from the "Black Ice" phenomenon, whereby cars in icy conditions slide out and crash due to difficult-to-see ice on the road. By allowing cars to communicate with one another, a car involved in a crash could tell other cars that there is an issue on the road ahead, allowing the cars to slow down or stop to avoid the road hazard.  
Our goal was to develop a system of nodes in ROS to enable vehicle to vehicle communication between 2 cars. We took inspiration for our project from the "Black Ice" phenomenon, whereby cars in icy conditions slide out and crash due to difficult-to-see ice on the road. By allowing cars to communicate with one another, a car involved in a crash could tell other cars that there is an issue on the road ahead, allowing the cars to slow down or stop to avoid the road hazard.  


Our project consists of two cars: the lead car and the follower car. If the lead car should crash, it shall publish a message to the follower car, which causes it to stop, thus preventing that the follower car from crashing into the same obstacle. For sensing the crash, we use the accelerometer sensor on an Openlog Artemis IMU in the lead car. If a specific deceleration threshold  is met (i.e. 10 milli g), the car publishes a message to a connected following car. If the follower car receives this specific message, it should cause an immediate stop.
Our project consists of two cars: the lead car and the follower car. If the lead car should crash, it shall publish a message to the follower car, which causes it to stop, thus preventing that the follower car from crashing into the same obstacle. For sensing the crash, we use the accelerometer sensor on an Openlog Artemis IMU in the lead car, which we initialized by adding the device when creating the Docker Container with the robot image. If a specific deceleration threshold  is met (i.e. 10 milli g), the car publishes a message to a connected following car. If the follower car receives this specific message, it should cause an immediate stop.


In our case, we set the lead car to be the "Master" roscore, to which we connected the follower car.
In order to prevent hackers from slowing down or stopping the follower car without a road hazard being present, we also include a unique security string published by the lead car and subscribed to by the follower car. If the security string being sent to the follower car before a stop command does not match the expected security string stored on the follower car, then the follower car will not accept a message to stop, and will continue to move around the track.
 
In our case, we set the lead car to be the "Master" roscore, to which we connected the follower car (so that both cars use the same roscore). Both cars run many of the same nodes, clients, and servers as described in [https://gitlab.com/djnighti/ucsd_robo_car_simple_ros Dominic Nightingale's Simple ROS framework]. However, in order to ensure the topics being published and subscribed do not cross cars (i.e. camera on one car is used by the lane guidance on the other car), we gave the each topic with the potential to cross over a new unique identifier.


== Gantt Chart ==
== Gantt Chart ==

Revision as of 11:51, 10 December 2021

Robot.jpg


Team Members

Team 5.5 consists of the former two-person teams 5 and 6. We've built everything separately except for the final project where we collaborated to make v2v communication work.

Team 5:

Zoey Huang

Jason Almeda

Team 6:

Liam Fowler - Mechanical Engineering

Johannes Diehm - UCSD Extension

Liam (left) and Johannes (right)












Mechanical

Circuit Design

Donkey Car Laps

Donkey Car Laps, Twisty Track

Donkey Car Laps, Classroom Track

ROS2 Laps

ROS Laps

Our Project

Our goal was to develop a system of nodes in ROS to enable vehicle to vehicle communication between 2 cars. We took inspiration for our project from the "Black Ice" phenomenon, whereby cars in icy conditions slide out and crash due to difficult-to-see ice on the road. By allowing cars to communicate with one another, a car involved in a crash could tell other cars that there is an issue on the road ahead, allowing the cars to slow down or stop to avoid the road hazard.

Our project consists of two cars: the lead car and the follower car. If the lead car should crash, it shall publish a message to the follower car, which causes it to stop, thus preventing that the follower car from crashing into the same obstacle. For sensing the crash, we use the accelerometer sensor on an Openlog Artemis IMU in the lead car, which we initialized by adding the device when creating the Docker Container with the robot image. If a specific deceleration threshold is met (i.e. 10 milli g), the car publishes a message to a connected following car. If the follower car receives this specific message, it should cause an immediate stop.

In order to prevent hackers from slowing down or stopping the follower car without a road hazard being present, we also include a unique security string published by the lead car and subscribed to by the follower car. If the security string being sent to the follower car before a stop command does not match the expected security string stored on the follower car, then the follower car will not accept a message to stop, and will continue to move around the track.

In our case, we set the lead car to be the "Master" roscore, to which we connected the follower car (so that both cars use the same roscore). Both cars run many of the same nodes, clients, and servers as described in Dominic Nightingale's Simple ROS framework. However, in order to ensure the topics being published and subscribed do not cross cars (i.e. camera on one car is used by the lane guidance on the other car), we gave the each topic with the potential to cross over a new unique identifier.

Gantt Chart

Our Code

You can find the GitHub repository to our code here: https://github.com/LiamEFowler/ECE-MAE-148-Group5.5-Black-Ice

In summary, we added a talker node on the lead car, a listener node on the follower car and the according launch files. In the talker node, we implemented a module to read the output of the IMU, filter it for the axis of acceleration that was interesting for us (frontal deceleration) and publish a message if the threshold was met. In the listener node we implemented a module that, if a specific message was received, called a function in the lane_guidance_node. In the Lane_guidance_node, we had to implement the function that could be called from the listener node and which stopped the car.

Weekly Presentations

Final Proposal Slides

11/23 Status Update

12/2 Status Update

Final Presentation

Final Presentation Slides:

Summary and Learnings