摘要
Gazebo作为机器人通用仿真平台,能够在复杂的室内和室外环境中准确模拟机器人行为,并在单节点上支持多机器人的协同仿真。但执行仿真任务中如果启动成百上千台机器人,通常会发现Gazebo性能参考值(RTF)仿真实时比会降低两个数量级,甚至出现仿真错误的情况,仿真性能会成为其主要制约因素。为了实现机器人集群的高性能仿真,探索了基于MPI的跨节点ROS+Gazebo仿真平台搭建方法,核心过程是针对确定的仿真任务进行并行划分,可采用编号划分或区域划分,将划分好的各个子任务部署到各计算节点的Gazebo上进行仿真,最后通过Gazebo之间的MPI进程通信保证仿真的同步和一致性,以此实现机器人分布在不同计算节点上的协同仿真。同时编写了固定翼和四旋翼同构和异构的仿真测试案例,通过脚本程序读入world配置文件和roslaunch文件来实现,设计了对用户友好的与ROS类似的启动方式,进行了单节点和跨节点的性能测试,验证了分布式并行仿真的优越性。
Gazebo,as a general robot simulation platform,can simulate robot behavior accurately in the complex environment of indoor or outdoor,and support multi-robot collaborative simulation on single computer node.But when the simulation task contains hundreds of robots,it is usually found that the RTF(Gazebo simulation real-time performance)will reduce two orders of magnitude,some errors even appear in the simulation.The simulation performance will become the critical limiting factor.In order to realize high-performance simulation,the across node simulation platform based on MPI and ROS+Gazebo is explored.The core process is to divide the simulation tasks in parallel,which can be divided by number or region.The divided sub tasks are deployed to the Gazebo of each computing node for simulation.Finally,the MPI process communication between the Gazebo ensures the synchronization and consistency of the simulation,so as to realize the collaborative simulation of robots distributed on different computing nodes.At the same time,two types of cases including homogeneity and heterogeneity about fixed wing and quadrotor are writed,which are realized by reading the world configuration file and roslaunch file through the script program.The user-friendly starting mode similar to ROS was designed,and the single-node and cross-node performance tests are carried out to verify the advantage of distributed parallelism simulation.
作者
蒋化南
张帅
林宇斐
李豪
JIANG Hua-nan;ZHANG Shuai;LIN Yu-fei;LI Hao(Tianjin Artificial Intelligence Innovation Center,Tianjin 300280,China;The Academy of Military Science,Beijing 100000,China)
出处
《计算机科学》
CSCD
北大核心
2021年第S02期672-677,692,共7页
Computer Science
基金
国家自然科学基金青年基金(61902425)。
