ROS-EDGE-ORCHESTRATION

A ROS-based package for utility-aware dynamic task offloading framework based on a multi-edge-robot system that takes into account computation, communication, and task execution load at the edge devices to minimize the overall service time for delay-sensitive applications.

Requirement:

This package is distributed on following nodes:

• 3 Edge devices(SBCs) - Nvidia Jetson Nanos
• 3 Turtlebot3s spawned on Gazebo Simulator - Local Machine

Overview

Continuous device, network and task profilers are continuously running on edge devices. For each task assigned, an edge with maximum utility is derived using a utility maximization technique with weights variation and a system reward assignment for task connectivity. A scheduler is in charge of task assignment, whereas an executor is responsible for task offloading on edge devices.

• Credit for spawning multiple turtlebot3 in Gazebo ROS Noetic goes to hikashi
• Credit for the use of ROS-Yolov5 package goes to mats-robotics

Installation Prerequisites

This package is built and tested on Ubuntu 20.04 LTS and ROS Noetic with Python 3.8. The following libraries are required to install before proceeding to run the code

$ sudo apt-get install ros-noetic-gmapping
$ sudo apt-get install ros-noetic-navigation
$ sudo apt-get install python-numpy
$ sudo apt-get install python-scikits-learn
$ sudo apt-get install ros-noetic-teb-local-planner
$ sudo apt-get install ros-noetic-multirobot-map-merge
$ sudo apt-get install ros-noetic-yolov5-ros

Installation Process

For interoperability, all the edge devices and the robots should have the same operating system. The following package should be installed on all four machines in the system.

$ sudo mkdir -p ~/catkin_ws/src
$ cd ~/catkin_ws/src/
$ git clone https://github.com/herolab-uga/ros-edge-orchestration.git
$ cd ~/catkin_ws
$ catkin_make

Execution for Collaborative Multirobots Object Detection and Pose Estimation

Launching robots in Gazebo - Local Machine

 $ source ~/catkin_explore/devel/setup.bash 
 # export TURTLEBOT3_MODEL=waffle_pi
 $ roscore 

The following launch file would launch three turtlebot3s in Gazebo world with RViz for visualization.

roslaunch edge_robot test_setup.launch

For pose publishing and way points for navigation

roslaunch edge_robot all_pose_publishers.launch
roslaunch edge_robot all_wps.launch

Task profilers - Edge Devices

To be launched on each edge device.

e.g. on edge 1, use the following launch file:

roslaunch edge_robot e1_profilers.launch

on edge 2:

roslaunch edge_robot e2_profilers.launch

on edge 3:

roslaunch edge_robot e3_profilers.launch

Preinitialized tasks on edge devices

on edge 1:

roslaunch edge_robot tb3_0_slam.launch
roslaunch edge_robot tb3_0_nav.launch
roslaunch edge_robot tb3_0_yolov5.launch

on edge 2:

roslaunch edge_robot tb3_1_slam.launch
roslaunch edge_robot tb3_1_nav.launch
roslaunch edge_robot tb3_1_yolov5.launch

on edge 3:

roslaunch edge_robot tb3_2_slam.launch
roslaunch edge_robot tb3_2_nav.launch
roslaunch edge_robot tb3_2_yolov5.launch

Scheduler on local Machine

Set the mode of the scheduler variant from CPU, Network, Memory, all, fixed within the following launch file. It takes in the parameters from the config/task_req_yolo_obj.yaml which are predefined for the task tested and launches the Gateway, Scheduler and the Executor nodes.

roslaunch edge_robot scheduler.launch

Contributors

• Nazish Tahir - PhD Candidate
• Dr. Ramviyas Parasuraman - Lab Director

Heterogeneous Robotics (HeRoLab)

Heterogeneous Robotics Lab (HeRoLab), School of Computing, University of Georgia.

For further information, contact Nazish Tahir [email protected] or Dr. Ramviyas Parasuraman [email protected]

https://hero.uga.edu/