AWS RoboMaker is a service for robotic developers provided by Amazon Web Services (AWS). This service helps its users to build robot applications easily. The structure of the RoboMaker is like a big toolbox filled with everything a user needs to create, test, and launch their robotic systems. It also provides a simulation environment for the robotics application development which is powered by Gazebo.
A gazebo is a type of virtual playground where the developers can create digital versions of the robotic systems and test them out in different situations without the need of any physical hardware. It also helps the user to try out different ideas and make changes easily and in a faster way. Gazebo helps the developer to build actual prototypes in a cheaper way because AWS RoboMaker also supports the Robot Operating System (ROS), which is a very popular software used by a lot of robotics developers.
What is the Robotics Development Environment with AWS RoboMaker?
The Robotics Development Environment is a setup of AWS RoboMaker which helps users to build and use robot applications. This environment gives a set of tools and resources that make it easy to create, test, and launch the robotic systems. It gives access to familiar tools like the Robot Operating System (ROS), the Gazebo environment, and different software development kits (SDKs). These tools allow developers to build, debug, and deploy their robotic applications easily.
With the help of the AWS RoboMaker’s Robotics Development Environment, developers can use the available cloud resources to enhance their development efforts and make their applications available faster. It also gives support with other AWS services, so developers can easily other features like machine learning, and data analytics, and monitor their robotic applications in a nutshell. It can also provide the user with robust tools combined with cloud resources like a human having a super-charged workshop for bringing robot ideas to life.
Features of Robotics Development Environment with AWS RoboMaker
- It supports Virtual Testing where users can test their robot’s behavior in a virtual environment before deploying it into the real world. This allows the developers to identify and fix issues or bugs more easily.
- The AWS RoboMaker allows the users to easily connect the robot with other AWS services like Amazon SageMaker for machine learning. This can make the robot more advanced with the help of machine learning models and design robots to perform complex tasks.
- It allows the developer to manage and monitor robotic performance through AWS RoboMaker console. This also enables user to receive real-time updates, and make necessary adjustments according to them in a easy way.
- The developer can change the robot’s software onto physical hardware using AWS RoboMaker. for example, once developer has completed the designing and testing part, he or she can use AWS RoboMaker to deploy it into physical hardware. This saves the user time and effort.
- It allows Customization Tailor to complete the developer particular need into the robot. It can provide any customization like configuring software tools or adding special hardware components, developer have the flexibility to create an environment that suits their requirements.
- AWS RoboMaker allows Collaborative Development in which robot can be build with a team. The developer share resources and code with his team members and they can collab to work on their robot together. This enhances the productivity speed and promotes knowledge sharing among team members.
- It allows developers to scale up and down their infrastructure effort because they don’t have to worry to manage the complex hardware or software setups and directly focus on the robot’s development and performance.
Setting Up Your First Robotics Development Environment with AWS RoboMaker
Step 1: Go to the AWS website & Create an AWS Account. User must have a valid credit card, although many services, including AWS RoboMaker, offer a free tier. Then open the AWS Management Console and search for AWS Robomaker.
Step 2: Now create an AWS Robomaker development environment by following the general instructions.
Step 3: Now we must Install Docker to run simulations and deploy applications on the local machine by following the instructions on the Docker website using the AWS CLI.
Step 4: After deploying the environment and deploying applications on docker, user must create a robot application and assign the properties to his robot using the application. Or he can use the below code in the CLI to perform the same.
pip install aws-robomaker-cdk-type
Step 5: Now the user must configure the AWS credentials on their local machine using the AWS CLI. Copy the IAM Role and simulation job and paste it into the CLI and run the following command.
aws configure
Step 6: Now user needs to create a ROS Workspace. He can either use the AWS to create a robot or use below code and command to configure and create it on the local machine.
mkdir -p ~/ros-workspace/src
cd ~/ros-workspace/src
catkin_init_workspace
Step 7: After creating and setting environment and applications of our robot, we Now we have to install AWS RoboMaker ROS Packages by cloning the AWS RoboMaker sample applications repository into your ROS workspace’s src directory –
The user can also check his working of the simulation tools on the configure on his local system using the simulation tools and monitor them. He can click on below and view their status as well as make the changes.
git clone https://github.com/aws-robotics/aws-robomaker-sample-application.git
Step8: Now the developer can click on the terminal or use the following code to start creating a physical robot according to him. The developer must build a ROS Workspace to store the development which can navigate to the ROS workspace directory.
cd ~/ros-workspace
catkin_make
Step 9: Developers already used the AWS RoboMaker CLI and created a simulation application. This application will get attached to our robot simulation environment. for more help, find documentation on how to create a simulation application in the AWS RoboMaker documentation.
aws robomaker describe-robot-application –application YOUR-ROBOT-APP-ARN
Step 10: Now we need to launch the Simulation Job and for that we can use the AWS RoboMaker console or CLI to launch a simulation job. This will spin up a simulation environment based on the application you created.
aws robomaker describe-simulation-application –application YOUR-SIM-APP-ARN
Step 11: You can now develop Robot Applications With your simulation environment set up after developing the robot applications using ROS and other tools:
{
“maxJobDurationInSeconds”: 3600,
“iamRole”: “IAM-ROLE-ARN”,
“robotApplications”: [
{
“application”: “YOUR-ROBOT-APP-ARN”,
“applicationVersion”: “$LATEST”,
“launchConfig”: {
“environmentVariables”: {
“ROS_IP”: “ROBOMAKER_ROBOT_APP_IP”,
“ROS_MASTER_URI”: “http://ROBOMAKER_ROBOT_APP_IP:11311”,
“GAZEBO_MASTER_URI”: “http://ROBOMAKER_SIM_APP_IP:11345”
},
“streamUI”: false,
“command”: [
“/bin/bash”, “-c”, “roslaunch hello_world_robot rotate.launch”
]
},
“tools”: [
{
“streamUI”: true,
“name”: “robot-terminal”,
“command”: “/entrypoint.sh && xfce4-terminal”,
“streamOutputToCloudWatch”: true,
“exitBehavior”: “RESTART”
}
]
}
],
“simulationApplications”: [
{
“application”: “YOUR-SIM-APP-ARN”,
“launchConfig”: {
“environmentVariables”: {
“ROS_IP”: “ROBOMAKER_SIM_APP_IP”,
“ROS_MASTER_URI”: “http://ROBOMAKER_ROBOT_APP_IP:11311”,
“GAZEBO_MASTER_URI”: “http://ROBOMAKER_SIM_APP_IP:11345”,
“TURTLEBOT3_MODEL”:”waffle_pi”
},
“streamUI”: true,
“command”: [
“/bin/bash”, “-c”, “roslaunch hello_world_simulation empty_world.launch –wait”
]
},
“tools”: [
{
“streamUI”: true,
“name”: “gzclient”,
“command”: “/entrypoint.sh && gzclient”,
“streamOutputToCloudWatch”: true,
“exitBehavior”: “RESTART”
}
]
}
]
}
Step 12: Deploy robot Applications into physical robots or run them in simulation using AWS RoboMaker.
aws robomaker create-simulation-job –cli-input-json file://create_simulation_job.json
Step 13: Use AWS RoboMaker’s monitoring and debugging tools to monitor your robot applications and troubleshoot to solve the issues.
Advantages of Robotics Development Environment with AWS RoboMaker
- It is highly flexible because it can handle the change workloads and demands easily by allowing the developer to enhance without compromising performance.
- It is cost-effective solution for improving robotic performance because it reduces the need to invest in expensive hardware upfront. This helps the user to manage costs e minimize unnecessary expenses.
- It allows Integrated Tools AWS RoboMaker provides a comprehensive set of development tools, including simulation environments, debugging capabilities, and visualization tools, streamlining the development process and enabling rapid iteration.
- This help developer to perform and test the robotic applications in virtual environments before deploying them to physical robots, reducing risks and provides safety.
- AWS RoboMaker allows users to easily connect the robot with other AWS services easily. Like user can use AWS’s machine learning services and models advance features into it.
- This works on the fleet management concept where users are offered with different types of management capabilities in which user can easily deploy, monitor, and update the robots to ensure consistent performance.
- It has high-built Security frameworks which ensures the protection of sensitive data and other important data.
- Their is an active community of developers to provide the Community and Support to new developers. It gives them the technical support, help in the training programs, and other services.
Disadvantages of Robotics Development Environment with AWS RoboMaker
- It is costly particularly for long or resource-heavy simulations, deployments, and storage because it works on the principle of “pay-as-you-go” pricing model.
- It is complex to set up & manage because it can be tricky to configure, especially if developer is new to cloud development or robotics.
- This environment offers simulation environments for testing which can look real but might not be as realistic as the real world.
- It can might store sensitive data and running simulations on remote servers which might have security risks. The developers needs to ensure strong security measures to protect sensitive data and prevent unnecessary access to the robotics applications and data.
Conclusion
The use of AWS RoboMaker provides us a Robotics Development Environment which helps the its users to build robot applications easily. The structure of the RoboMaker is like a big toolbox filled with everything a user needs to create, test, and launch their robotic systems. But there are some things to think about. They can get costly if they get long resource and heavy simulations that leads to deployments into storage. It is complex to set up & manage because it can be tricky to configure, especially if developer is new to cloud development or robotics so, we need to have knowledge about the technical setup, scaling, monitoring, and troubleshooting.
However, we also can’t ignore that the Robotics Development Environment is a unified platform which brings the data engineers, scientists, and ML experts together on one platform for seamless collaboration are worth these potential drawbacks. We can integrates with it seamlessly with other AWS services and we don’t have to worry about Security frameworks because it ensures the protection of sensitive data. The developer can change the robot’s software onto physical hardware using AWS RoboMaker. for example, once developer has completed the designing and testing part, he or she can use AWS RoboMaker to deploy it into physical hardware. So we can conclude that it saves, both the time and effort of the user.
Robotics Development Environment – FAQ’s
What Do You Mean By Robotics Development Environment?
The Robotics Development Environment is a setup of AWS RoboMaker which helps users to build and use robot applications.
Why Is There A Need Of The Robotics Development Environment?
This environment is needed because it gives a set of tools and resources which makes it easy to create, test, and launch the robotic systems.
What Do You Mean By Gazebo?
Gazebo is a type of virtual playground where the developers can create the digital versions of the robotic systems and test them out in different situations without the need of any physical hardware.
Explain Any Two Features Of Robotics Development Environment.
- It supports Virtual Testing where users can test their robot’s behavior in a virtual environment before deploying it into the real world. This allows the developers to identify and fix issues or bugs more easily.
- It allows the developer to manage and monitor robotic performance through AWS RoboMaker console.
What Are The Limitations Of The Robotics Development Environment?
- It is costly particularly for long or resource-heavy simulations, deployments, and storage because it works on the principle of “pay-as-you-go” pricing model.
- It is complex to set up & manage because it can be tricky to configure, especially if developer is new to cloud development or robotics