This is the ros package for the ODHE Grant Funded Project Robot-Assisted Feeding (RAF) for Individuals with Spinal Cord Injury

This ros workspace has been used to develop the whole project, so different iterations of the project require different program files.

This repo is not intended to be cloned into a ros workspace and be useful right away. In the future, I will upload a cleaned and maintained repo for this purpose.

The first iteration of the project used the iSCAN etl-600 wearable eye tracker. We used a faster-RCNN object detection network to detect cups, bowls, plates, forks, and spoons. The network used the iSCAN's head-mounted camera as the input. To demo the system, we placed a cup in the scene. The user then directs their gaze to the cup. When they are ready, the user issues a voice command such as "grab the cup". Baxter then picks up the cup that the user was looking at and delivers it to in front of the user's mouth. They then move their body to sip through a straw in the cup.

I actually don't think this demonstration will work in this workspace because the launch file is in a different workspace. Originally, we were using Ubuntu 18.04 because we couldn't get Baxter to work with ROS Noetic and Python3. Then, I put in the effort to convert Baxter's code to Python3 and I switched to Ubuntu 20.04. The workspace did not get transferred over, but some of the files did. Below are some of the files associated with this project iteration.

cup_demo.py

The main file for the cup demo. Handles all code logic, particularly the robot's motion.

cup_demo_short.py   

I think this version was supposed to only perform one reach, or some other shortened version for demonstration purposes. It looks very similar to the code above, however.

record_data.py

Records the iSCAN information and robot data.

serial_read.py  

Reads the serial data from the iSCAN.

The second iteration of this project got rid of the head-mounted eye tracker. Also, we decided to interact with different food items on a plate, instead of dishes and utensils. For the interface, we used a tablet monitor with a Tobii Eye tracker 4 mounted on the bottom. We detected objects using a mask-RCNN object detection network from detectron2. The network used an Intel L515 LIDAR depth camera as the input. The plane of the table was defined using an AprilTag fiducial marker. We used Talon to interface with the eyetracker. Talon also has the ability to allow the user to issue commands. The depth camera is mounted to Baxter's wrist. The camera videos food items on a plate, and the image is sent to the tablet with food item outlines drawn. The person uses their eye movements to direct a cursor to a food item. After a short dwell time, the food item is selected. Baxter picks up the food item and then turns to video the user's face. We used the face-alignment ROS package for facial keypoint detection. When the user open's their mouth, Baxter approaches the user's face and releases the food item.

Check out a demo video here!

Below are the files associated with this project iteration.

raf_study.launch
    - camera_multiple.launch
    - tag_detection.py
    - arm_camera_network.py
    - dlt.py

raf.launch 

Top-level launch file for the project.

camera_multiple.launch     

Launches both the LIDAR and STEREO depth cameras. Requires serial numbers.

tag_detection.py         

Detects the AprilTag Fiducial markers and draws the tag coordinate frame.

arm_camera_network_run.py   

Detects food items on the plate.

dlt.py    

Defines the plane of the table and publishes DLT parameters.

calibrate_camera.py     

Runs the logic for performing the calibration which defines the camera in robot coordinates.

raf.py            

Main project script file. Handles all experiment logic and robot motion.

realtime_ros.py  

Currently not in the project workspace. Located in face-alignment. This code detects the person's face.

raf_grasp_demo.py  

Demonstrates picking up food items anywhere on the plate.

raf_setPos_demo.py  

Demonstrates picking up a food item in a set position and orientation.

raf_visualization.py  

Test code that draws GUI info for food item detection. This got integrated into raf.py now.

raf_visualize_grasp.py   

Nice visualization for the position and orientation of food item.