Quantifying the Effects of Environmental Conditions on Autonomy Algorithms for Unmanned Ground Vehicles.

Autonomy for commercial applications is developing at a rapid pace; however, autonomous navigation of unmanned ground vehicles (UGVs) for military applications has been deployed to a limited extent. Delaying the use of autonomy for military applications is the environment in which military UGVs must operate. Military operations take place in unstructured environments under adverse environmental conditions. Military UGVs are infrequently tested harsh conditions; therefore, there exists a lack of understanding in how autonomy reacts to challenging environmental conditions. Using high-fidelity modeling and simulation (M&S), autonomy algorithms can be exercised quickly and inexpensively in realistic operational conditions. The presented research introduces the M&S tools available for simulating adverse environmental conditions. Simulated camera images generated using these M&S tools are run through two typical autonomy algorithms, road lane detection and object classification, to assess the impact environmental conditions have on autonomous operations. Furthermore, the presented research proposes a methodology for quantifying these environmental effects.