Improved Energy Predictions for High-Confidence Trajectory Planning of Automated Off-Road Vehicles

Abstract

Generally, large discrepancies exist between predicted and realized energy consumption with energy-aware trajectory planning algorithms for off-road vehicles. Conservative planners typically pre-compensate for the expected discrepancy by demanding high confidence thresholds. Global path planners often ignore the substantial energy needed for turning on off-road deformable terrains, contributing to this mismatch. In this paper, we improve energy predictions by adding an additional energy cost for turning maneuvers in the global path planner and reformulate the high-confidence global planner's cost function to reduce conservatism. We couple the proposed global planner with a nominal local planner to show the robustness and improved performance compared to existing energy-aware motion planners for off-road vehicles.