Validation of NoMaD as a Global Planner for Mobile Robots

Abstract

As autonomous mobile robots are becoming increasingly common in real-world applications, like warehouses and self-driving cars, so is the need for robust navigation methods growing. NoMaD, a vision-based navigation architecture, was recently presented and showed very good metrics in obstacle avoidance tested in ”challenging environments” but the exact level of challenge was unclear. This thesis aimed to measure that performance in a standardized way and implement an improvement by augmenting it with a LiDAR sensor. This new solution is based on ROS Navigation, where NoMaD acts as the global planner guiding the robot, leaving the obstacle avoidance to the local planner. Both NoMaD and the new solution were tested in environments inspired by the standardized navigation environment dataset BARN. While NoMaD reached the proclaimed success rate (90%) in simple baseline tests, it failed to do so in more complex environments, even when the hardware limitations of the setup were compensated for, with success rates ranging from 3.3% to 53.3%. The new solution, however, achieved all-around good results (83%) with no collisions. While it has its own drawbacks the new approach shows some merit.