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In my ambition to have some small influence over the matter, I took a course in autonomous robot control theory last year, which culminated in my building a Python-based robotic simulator that allowed me to practice control theory on a simple, mobile, programmable robot.
The simulator I built is written in Python and very cleverly dubbed Sobot Rimulator. You can find v1.0.0 on GitHub. It does not have a lot of bells and whistles but it is built to do one thing very well: provide an accurate simulation of a mobile robot and give an aspiring roboticist a simple framework for practicing robot software programming. While it is always better to have a real robot to play with, a good Python robot simulator is much more accessible and is a great place to start.
In other words, programming a simulated robot is analogous to programming a real robot. This is critical if the simulator is to be of any use to develop and evaluate different control software approaches.
In this tutorial, I will be describing the robot control software architecture that comes with v1.0.0 of Sobot Rimulator, and providing snippets from the Python source (with slight modifications for clarity). However, I encourage you to dive into the source and mess around. The simulator has been forked and used to control different mobile robots, including a Roomba2 from iRobot. Likewise, please feel free to fork the project and improve it.
Robots, like people, need a purpose in life. The goal of our software controlling this robot will be very simple: It will attempt to make its way to a predetermined goal point. This is usually the basic feature that any mobile robot should have, from autonomous cars to robotic vacuum cleaners. The coordinates of the goal are programmed into the control software before the robot is activated but could be generated from an additional Python application that oversees the robot movements. For example, think of it driving through multiple waypoints.
The first thing to note is that, in this guide, our robot will be an autonomous mobile robot. This means that it will move around in space freely and that it will do so under its own control. This is in contrast to, say, a remote-control robot (which is not autonomous) or a factory robot arm (which is not mobile). Our robot must figure out for itself how to achieve its goals and survive in its environment. This proves to be a surprisingly difficult challenge for novice robotics programmers.
This interface internally uses a robot object that provides the data from sensors and the possibility to move motors or wheels. If you want to create a different robot, you simply have to provide a different Python robot class that can be used by the same interface, and the rest of the code (controllers, supervisor, and simulator) will work out of the box!
Valetudo is the de-facto-standard open-source software solution for cloud-free vacuum robots empowering thousands of users.Since it was started in 2018, it has matured to a reliable fire-and-forget solution that just works.
*Note: Amazon Future Engineer offers free online virtual robotics & coding classes for any student or teacher affected by COVID-19 in the US, Canada, Argentina, Colombia and Chile. For more information, visit:
QEV3Bot Simulator is a free Windows-based LEGO EV3 simulator. QEV3BotSim allows the user to configure the connection of up to four sensors chosen from the set of eight available sensors mounted on the robot.
To download Ansys HFSS, you must be an Ansys customer and have access to the Customer Portal. Ansys HFSS is included in the Electronics software bundle and is also included in the free Ansys Student bundle.
Users can take advantage of the seamless workflow in Electronics Desktop, which includes advanced electromagnetic field solvers, and dynamically link them to power circuit simulators to predict EMI/EMC performance of electrical devices. These integrated workflows avoid repetitive design iterations and costly recurrent EMC certification tests. Multiple EM solvers intended to address diverse electromagnetic problems, as well as the circuit simulators in Electronics Desktop, help engineers assess the overall performance of their electrical devices and create interference-free designs. These diverse problems range from radiated and conducted emissions, susceptibility, crosstalk, RF desense, RF coexistence, cosite, electrostatic discharge, electric fast transients (EFT), burst, lightning strike effects, high intensity fields (HIRF), radiation hazards (RADHAZ), electromagnetic environmental effects (EEE), electromagnetic pulse (EMP) to shielding effectiveness and other EMC applications.
View Machining simulationGet started with Robot MachiningRobot Machining with external axesExternal axes can be modelled and synchronized with a robot arm for robot Machining purposes. RoboDK optimizes the robot axes to generate optimal and error-free robot programs.
View Robot Painting SimulationRobot Painting DocumentationRobot Spot WeldingRobotic Spot Welding is a type of resistance welding. Spot Welding is the most common type of robot welding. RoboDK Software can easily generate error-free robot programs for spot welding applications.
Link to Documentation 3D Printing with a robot3D printing with robots (or additive manufacturing) allows making large three dimensional objects from a digital object file. Industrial robot arms can be used as a 3-axis or 5-axis printer with RoboDK Software to 3D print large objects. RoboDK will generate error-free robot programs.
This Duckiebot is the fourth generation self-driving car of the Duckietown project, initiated at MIT in 2016 and now used for education and research in 58 countries. Equipped with a front facing camera, time of flight sensor, IMU and wheel encoders, in addition to a screen, four addressable RGB LEDs and a custom designed smart battery providing live diagnostics, this differential drive vehicle can be powered by NVIDIA's Jetson Nano 2GB or 4GB models. Within the fully programmable smart Duckietown urban ecosystem, featuring extensive online documentation, a simulator, logs, an international scientific competition (AI Driving Olympics) and a dedicated free massive online open course (MOOC), Duckiebots make learning state-of-the-art single and multi-robot autonomy accessible and fun. (Available worldwide) 041b061a72