Mobility and dynamics modeling for unmanned ground vehicle motion planning

This paper presents an approach to modeling unmanned ground vehicle (UGV) mobility performance and vehicle dynamics for evaluating the feasibility and cost of alternative motion plans. Feasibility constraints include power, traction, and roll stability limits. Sensor stabilization performance is considered in a system-level constraint requiring that the obstacle detection distance exceed the stopping distance. Mission time and power requirements are inputs to a multi- attribute cost function for planning under uncertainty. The modeling approach combines a theoretical first-principles mathematical model with an empirical knowledge-based model. The first-principles model predicts performance in an idealized deterministic environment. On-board vehicle dynamics control, for dynamic load balancing and traction management, legitimize some of the simplifying assumptions. The knowledge- based model uses historical relationships to predict the mean and variance of total system performance accounting for the contributions of unplanned reactive behaviors, local terrain variations, and vehicle response transients.