MRTA-Sim让多机器人任务分配更贴近真实环境，兼顾导航与避障。
MRTA-Sim: A Modular Simulator for Multi-Robot Allocation, Planning, and Control in Open-World Environments
- 模块化设计，连接任务分配与真实机器人规划控制栈
- 集成NAV2与CBF-QP实现多机协同避障与路径优化
- 适合研究真实场景下多机器人系统协同的学者使用
本文提出MRTA-Sim，一个基于Python/ROS2/Gazebo的模块化仿真平台，用于在复杂室内环境中测试多机器人任务分配（MRTA）方法。传统网格法在动态环境如仓库、商场、医院中过于受限；而自由空间方法常在抽象层面运行，忽略狭小空间和多机交互对解质量的影响。本工具将MRTA求解器输出接入NAV2导航栈，并通过基于控制屏障函数-二次规划（CBF-QPs）的集中式多机去冲突机制，实现更贴近真实运行的测试。系统架构模块化，支持各层级方法替换。我们以基于可满足性模理论（SMT）的动态MRTA方法为例，在一组室内配送机器人上验证了该平台的有效性。
原文摘要 · Abstract (English)
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.
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