Mobile robots have important applications in high speed, rough-terrain scenarios. In these scenarios, unexpected and hazardous situations can occur that require rapid hazard avoidance maneuvers. At high speeds, there is limited time to perform re-planning based on detailed vehicle and terrain models. Furthermore, detailed models often do not accurately predict the robot"s performance due to model parameter and sensor uncertainty. This paper presents a method for high speed hazard avoidance. The method is based on the concept of the trajectory space, which is a compact model-based representation of a robot"s dynamic performance limits in uneven, natural terrain. A Monte Carlo method for analyzing system performance despite model parameter uncertainty is briefly presented, and its integration with the trajectory space is discussed. Simulation results for the hazard avoidance algorithm are presented and demonstrate the effectiveness of the method.© (2004) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.