Creating Engaging and SEO Optimized Headless Robotic Simulations using AWS Batch
Introduction:
AWS Batch is a powerful service that allows robot developers to run headless robotic simulations at a massive scale. By using AWS Batch, developers can iterate and test their code through thousands of scenarios before moving on to physical device testing. This saves time and cost as deploying and testing every scenario on physical robots can be time consuming and expensive. In this blog, we will guide you through the process of creating an AWS Batch compute environment and job for running your containerized robot and simulation applications. We will cover steps such as preparing robot and simulation containers, creating a Dockerfile, building and pushing the container image to Amazon Elastic Container Registry (Amazon ECR), setting up permissions, creating an AWS Batch compute environment and job, and viewing the logs in Amazon CloudWatch.
Full Article: Creating Engaging and SEO Optimized Headless Robotic Simulations using AWS Batch
Headless Robotic Simulations with AWS Batch for Increased Velocity in Robot Development
Introduction
Running code through thousands of scenarios and iterating them before moving onto physical device testing allows robot developers to increase their velocity. However, deploying and testing every scenario on physical robots can be time-consuming and costly. AWS Batch provides an easy way for robot developers to run batch robotics simulations at a massive scale with custom control over compute types. This article will explore how to create an AWS Batch compute environment and job for running containerized robot and simulation applications.
Overview
This article will update the Amazon CloudWatch robot monitoring sample, originally built with AWS RoboMaker, to demonstrate how the same task can be achieved with AWS Batch. The sample involves running a robot navigation test and sending data to Amazon CloudWatch to monitor the robot’s position and speed. The following steps will be covered:
1. Prepare robot and simulation containers.
2. Create a Dockerfile to install docker-compose and the AWS CLI.
3. Build and push the container image to Amazon Elastic Container Registry (Amazon ECR).
4. Create a docker-compose.yaml file and upload it to Amazon Simple Storage Service (Amazon S3).
5. Set up permissions for AWS Batch jobs, robot, and simulation applications.
6. Create an AWS Batch compute environment, job queue, job definition, and job using the AWS Batch Wizard.
7. View the logs in Amazon CloudWatch.
Prerequisites
Before proceeding, ensure the following requirements are met:
1. An AWS account with permissions to AWS Identity and Access Management (AWS IAM), Amazon S3, Amazon ECR, Amazon CloudWatch, and AWS Batch.
2. Basic understanding of Docker containers.
3. Docker, AWS CLI, and VCS Tool installed on your machine.
Prepare Robot and Simulation Containers
To update the CloudWatch robot monitoring sample for use with AWS Batch, perform the following steps:
1. Clone the Amazon CloudWatch robot monitoring sample repository.
Note: The code in the AWS Robotics sample applications is already structured with ROS workspace directories, so there’s no need to create a workspace and source code directory.
2. Containerize the robot and simulation applications by following the steps described in Preparing ROS application and simulation containers for AWS RoboMaker, with some minimal name changes.
– Start from the “Build a docker image from a ROS workspace for AWS RoboMaker” step 2.
– Place the Dockerfile in the cloudwatchsample directory in step 3.
– Optionally, rename your applications to cloudwatch-robot-app and cloudwatch-sim-app in steps 6 and 7.
– Optionally, rename your application tags as batch-cloudwatch-robot-app and batch-cloudwatch-sim-app, respectively.
– In step 1 of “Publish docker images to Amazon ECR,” ensure you use the same robotapp and simapp variables as your application tag names from steps 6 and 7.
– Stop when you have pushed your ROS-based robot and simulation Docker images to Amazon ECR.
Create a Dockerfile to Install docker-compose and the AWS CLI
To run both the robot and simulation containers in AWS Batch and allow them to communicate with each other, follow these steps:
1. Inside the cloudwatchsample folder, create a new folder for storing AWS Batch Docker files.
– Command: mkdir batch-docker && cd batch-docker
2. Create a new file named Dockerfile and copy the provided contents into the file.
– This Dockerfile installs Docker Compose and the AWS CLI, enabling AWS Batch to copy the docker-compose.yaml object from Amazon S3.
– Dockerfile contents:
“`
FROM ubuntu:focal
ARG DEBIAN_FRONTEND=noninteractive
# Install prerequisites
RUN apt-get update && apt-get install -y curl && apt-get install wget
RUN apt-get update && apt-get -y install awscli
RUN apt -y install amazon-ecr-credential-helper
RUN apt-get -y install jq
RUN mkdir ~/.docker && echo “{“credsStore”: “ecr-login”}” | jq > ~/.docker/config.json
RUN curl -L “https://github.com/docker/compose/releases/download/$(curl -s https://api.github.com/repos/docker/compose/releases/latest | grep -oP ‘”tag_name”: “K(.*)(?=”)’)/docker-compose-$(uname -s)-$(uname -m)” -o /usr/local/bin/docker-compose
RUN chmod +x /usr/local/bin/docker-compose
RUN ln -s /usr/local/bin/docker-compose /usr/bin/docker-compose
“`
Build and Push the Container Image to Amazon ECR
Once the Dockerfile is created, follow these steps to build and push the container image to Amazon ECR:
1. Create a repository in Amazon ECR:
– In the Amazon ECR console, navigate to Repositories and choose “Create repository.”
– Keep the repository private and give it a name like “batch-dockercompose-awscli.” Choose “Create repository.”
– Select your new repository and choose “View push commands.” This will provide you with the AWS CLI commands to build and push your container image to Amazon ECR.
2. Execute the provided AWS CLI command to build and push your container image to Amazon ECR.
– Note: If you’re running the push commands from a non-Linux platform, remember to update the docker build command to include “–platform linux/amd64” at the end.
– Full command: docker build -t batch-dockercompose-awscli . –platform linux/amd64
Create a docker-compose.yaml File and Upload it to Amazon S3
Perform the following steps to create a docker-compose.yaml file and upload it to Amazon S3:
1. Create a docker-compose.yaml file and copy the provided contents into the file.
– This file runs both the robot and simulation applications and passes the necessary permissions.
– docker-compose.yaml contents:
“`
version: “3”
services:
robot:
image:
network_mode: host
command: bash -c “sudo apt-get -y upgrade && roslaunch cloudwatch_robot rotate.launch”
environment:
– AWS_CONTAINER_CREDENTIALS_RELATIVE_URI
– TURTLEBOT3_MODEL
sim:
image:
network_mode: host
command: roslaunch cloudwatch_simulation bookstore_turtlebot_navigation.launch
environment:
– AWS_CONTAINER_CREDENTIALS_RELATIVE_URI
– TURTLEBOT3_MODEL
“`
2. Update the image entries in the docker-compose.yaml with the appropriate image URI for both the robot and simulation applications from Amazon ECR.
– These image URIs can be found in the Amazon ECR console next to your repository names.
3. In the Amazon S3 console, choose an existing bucket or create a new one to upload your docker-compose.yaml file.
– Upload the file to the selected bucket.
Set Up Permissions for AWS Batch Jobs and Robot/Simulation Applications
To set up permissions for AWS Batch jobs, robot, and simulation applications, follow these steps:
1. Create an Amazon Elastic Container Service (Amazon ECS) Task Execution role:
– Navigate to the AWS IAM Console and choose “Roles.”
– Create a new role, selecting the AWS service Trusted entity type.
– For the Use case, choose “Elastic Container Service” from the dropdown, then select “Elastic Container Service Task.” Click “Next.”
– On the Add Permissions step, grant Amazon ECS agents permission to call AWS APIs on your behalf. An AWS managed policy for this task already exists.
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Summary: Creating Engaging and SEO Optimized Headless Robotic Simulations using AWS Batch
Summary: Headless robotic simulations with AWS Batch offer robot developers a cost-effective and time-saving way to test scenarios before physical device testing. AWS Batch enables developers to run batch robotics simulations at scale with customizable control over compute types. This blog provides a step-by-step guide on creating an AWS Batch compute environment and job to run containerized robot and simulation applications. It also covers prerequisites and instructions on preparing the containers, creating a Dockerfile, and setting up permissions for AWS Batch jobs. By following this guide, developers can streamline their robotics simulations and improve their development velocity.
Frequently Asked Questions:
Q1: What is robotics?
A1: Robotics is a multidisciplinary field that combines engineering, computer science, and various sciences to design, construct, program, and operate machines (robots) that can perform tasks autonomously or with minimal human intervention.
Q2: What are the different types of robots?
A2: There are various types of robots designed to serve different purposes. Some common types include industrial robots used in manufacturing processes, humanoid robots that mimic human movements, medical robots used in surgeries and patient care, autonomous vehicles, drones, and even toy robots for entertainment.
Q3: How are robots programmed to perform tasks?
A3: Robots are typically programmed using programming languages and algorithms suited to their specific tasks. Robot programmers write code to control the robot’s movements, actions, and responses to various stimuli. Programming can range from simple instructions to complex artificial intelligence algorithms that allow robots to learn and adapt.
Q4: What are the applications of robotics?
A4: Robotics has a wide range of applications across different industries and sectors. Some notable applications include automation in manufacturing, warehouse management, healthcare assistance, agriculture, space exploration, search and rescue operations, underwater exploration, and even entertainment.
Q5: Are robots going to replace humans in the workforce?
A5: While robots can automate repetitive and dangerous tasks, their integration into the workforce does not necessarily mean replacing humans. Instead, robots could augment human capabilities and support them in tasks, improving efficiency and productivity. The collaboration between humans and robots is often referred to as “collaborative robotics” or “cobots” and is expected to be the future of many industries.
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