Copyright © 2011 Phillip J. Durst et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Simulations provide a safe, controlled setting for testing and are therefore ideal for rapidly developing and testing autonomous mobile robot behaviors. However, algorithms for mobile robots are notorious for transitioning poorly from simulations to fielded platforms. The difficulty can in part be attributed to the use of simplistic sensor models that do not recreate important phenomena that affect autonomous navigation. The differences between the output of simple sensor models and true sensors are highlighted using results from a field test exercise with the National Robotics Engineering Center's Crusher vehicle. The Crusher was manually driven through an area consisting of a mix of small vegetation, rocks, and hay bales. LIDAR sensor data was collected along the path traveled and used to construct a model of the area. LIDAR data were simulated using a simple point-intersection model for a second, independent path. Cost maps were generated by the Crusher autonomy system using both the real-world and simulated sensor data. The comparison of these cost maps shows consistencies on most solid, large geometry surfaces such as the ground, but discrepancies around vegetation indicate that higher fidelity models are required to truly capture the complex interactions of the sensors with complex objects.