This paper introduces MRTA-Sim, a Python/ROS2/Gazebo simulator for testing
approaches to Multi-Robot Task Allocation (MRTA) problems on simulated robots
in complex, indoor environments. Grid-based approaches to MRTA problems can be
too restrictive for use in complex, dynamic environments such in warehouses,
department stores, hospitals, etc. However, approaches that operate in
free-space often operate at a layer of abstraction above the control and
planning layers of a robot and make an assumption on approximate travel time
between points of interest in the system. These abstractions can neglect the
impact of the tight space and multi-agent interactions on the quality of the
solution. Therefore, MRTA solutions should be tested with the navigation stacks
of the robots in mind, taking into account robot planning, conflict avoidance
between robots, and human interaction and avoidance. This tool connects the
allocation output of MRTA solvers to individual robot planning using the NAV2
stack and local, centralized multi-robot deconfliction using Control Barrier
Function-Quadrtic Programs (CBF-QPs), creating a platform closer to real-world
operation for more comprehensive testing of these approaches. The simulation
architecture is modular so that users can swap out methods at different levels
of the stack. We show the use of our system with a Satisfiability Modulo
Theories (SMT)-based approach to dynamic MRTA on a fleet of indoor delivery
robots.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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