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VMAS is a vectorized differentiable simulator designed for efficient Multi-Agent Reinforcement Learning benchmarking. It is comprised of a vectorized 2D physics engine written in PyTorch and a set of challenging multi-robot scenarios. Additional scenarios can be implemented through a simple and modular interface.
Multirobot system consisting of UR10s that allows simultaneous execution of tasks. It enables the control of different cobots with different controllers. || Sistema multirobot compuesto por UR10s que permite la ejecución simultánea de tareas. Permite el control de diferentes cobots con distintos tipos de controladores.
An ROS implementation of optimal Multi-Agent Pathfinding algorithm ICTS (Increasing Cost Tree Search) , and a simple multi-agent navigation with ridgeback-robot simulated in Gazebo
A ROS package that implements a multi-robot RRT-based map exploration algorithm. It also has the image-based frontier detection that uses image processing to extract frontier points.
Framework that allows the design, execution and analysis of generalized stochastic Petri nets (GSPN). It allows capturing multi-robot problems as a GSPN and simulate the model.