gazebo-classic VS argos3

it is this one: https://github.com/gazebosim/gazebo-classic

Then I find this page which presents the migrations required for each new Gazebo verison.

Check out the paper titled "Simulation Studies on Vision based Humanoid Arm Operations" , they have used Gazebo environment ( https://gazebosim.org/ ). However making it online, I don't think so they will.

Hi guys, Have been anyone able to run gazebo on linux/macos arm64 machine? is there any upcomming support for m1 apple chip if not? what is your solution to run a simulation on m1 do you have any advices? I have tried to build gazebo on my own but it seems it won't build whatever I try.

If you dont wanna spend any extra cash, there is the gazebo simulator (http://gazebosim.org/), which supports ROS (https://www.ros.org/).

Simulation is a strong choice too. ARGoS, Actin, Webots, Gazebo and V-Rep are some examples. A simulation will give you the opportunity to use some robots in different environments (with a lot of configurations). Gazebo has a big community and documentation, V-Rep is the more powerful one (and my pick).

I've used Gazebo; it's very well-integrated into the ROS universe. Lots of open-source sample 'worlds' and robot models out there: http://gazebosim.org/. There is also https://cyberbotics.com/, which looks neat, but I haven't tried it.

Gazebo provides a set of build-in meshes that are listed in this source code file. Apply them be adding an tags inside the tag as follows.

Do check out these options (personally I’ve used these) 1. Buzz: https://the.swarming.buzz 2. Argos: https://github.com/ilpincy/argos3

You can do AI by relying on a library that is effectively a black box and yet make the result useful to other users. Recently I made a web interface for Argos3 (swarm robotics framework https://www.argos-sim.info ) and I do not know how it works. I understand in principle (write the code for a robot, define how many such robots run, how many ticks, start the simulation, study the output) but that is it, a very high level (some would say superficial) level. Yet, by providing a web interface (using Docker and Observable) I facilitated the research process. Did I do any AI? To somebody familiar with Argos3, no, to pretty much anyone else, yes.

Simulation is a strong choice too. ARGoS, Actin, Webots, Gazebo and V-Rep are some examples. A simulation will give you the opportunity to use some robots in different environments (with a lot of configurations). Gazebo has a big community and documentation, V-Rep is the more powerful one (and my pick).

How? Encapsulating the "core" part in an HTTP API. For example I don't write C++ and I don't even know much about swarm robotics. Yet to help a group of researchers I wrote a short NodeJS server for Argos3 https://github.com/ilpincy/argos3 that allowed few functions, in particular starting a simulation and get the results back. I did so naively at first and then in order to get faster feedback tinkered with server-sent events. This allowed me to connect an interface like ObservableHQ in JavaScript or Jupyter Notebooks in Python (but can also be JS, Julia, etc) by providing that API and results via HTTPS.