The military is constantly expanding the use of unmanned ground vehicles in warfighting applications that often involve complex environments. Part of the focus of military research is to improve or validate existing routing algorithms which are used to predict vehicle mobility. Routing algorithms are based on the time required for vehicle movement through a series of obstacles such as trees or fences, thus requiring an assessment of the ability to override such obstacles as compared to finding an alternate maneuver path. The required overriding force can be computed and compared to a vehicle's tractive force to determine the best viable option. If overriding the obstacle is an option (tractive force exceeds the required overriding force), the delay in overriding can be assessed as compared to the delay in maneuvering around the obstacle. This study provides a quick and reasonable calculation of the force required to override specific types of vertically embedded obstacles to support the determination of movement capabilities for unmanned ground vehicles on the battlefield. Published by Elsevier Ltd. on behalf of ISTVS.

Future Combat Systems will include Small Unmanned Ground Vehicles (SUGV). Several have already been deployed, including the TALON ® , an 80-lb SUGV; PackBot ® at 50 lb, and Gator TM at 2500 lb. As doctrine, tactics, techniques, and procedures continue to evolve, there exists a need to represent the performance of these vehicles in Army models and simulations. Army simulations such as COMBAT XXI and OneSAF will use the Standard Mobility Application Programmers Interface (STNDMob API) for estimating vehicle performance. Currently, only the Gator can be represented by a STNDMob vehicle class, and it is modeled as a manned vehicle.This report describes the results of a study undertaken to identify and discuss mobility performance algorithms applicable to SUGV in the weight range of 10 to 5000 lb. Algorithms used by the NATO Reference Mobility Model and the STNDMob were examined. Most of the algorithms currently used in STNDMob were found to be applicable to SUGV. However, it was found that vehicle performance data and algorithms for additional material surfaces and obstacles need to be developed; specifically, models and data for wheeled vehicle skid steering, interior building floor and roof surfaces, and stair climbing are needed. Previously unreported SUGV test results for TALON, MATILDA, and PackBot are presented, and performance estimates described herein compared well using currently available algorithms in STNDMob. Recommendations for new algorithms and improvements to current mobility algorithms are presented.