Scalable Interactive Autonomous Navigation Simulations on HPC

We present our work of enabling HPC in an interactive real-time autonomy loop. The workflow consists of many different software components deployed within Singularity containers and communicating using both the Robotic Operating System's (ROS) publish-subscribe system and the Message Passing Interface (MPI). We use Singularity's container networking interface (CNI) to enable virtual networking within the containers, so that multiple containers can run the various components using different IP addresses on the same compute node. The Virtual Autonomous Navigation Environment Environmental Sensor Engine (VANE:ESE) is used for physically-realistic simulation of LIDAR along with the Autonomous Navigation Virtual Environment Laboratory (ANVEL) for vehicle simulation. VANE:ESE sends Velodyne UDP LIDAR packets directly to the Robotic Technology Kernel (RTK) and is distributed across multiple compute nodes via MPI along with OpenMP for shared memory parallelism within each compute node. The user interfaces with the navigation environment using an XFCE desktop with virtual workspaces running over a VNC containerized deployment through a double-hop ssh tunnel, which uses noVNC (a JavaScript-based VNC client) to provide a browser-based client interface. We automate the complete launch process using a custom iLauncher plugin. We benchmark scalable performance with multiple vehicle simulations on four different HPC systems and discuss our findings.

Automated summary

This article presents the work of enabling HPC in an interactive real-time autonomy loop. It discusses the workflow using Singularity containers, ROS, and MPI, as well as the specific software components and tools used. Scalable performance on multiple HPC systems is also benchmarked and analyzed.