Chance-Constrained Planning for Dynamically Stable Motion of
Reconfigurable Vehicles
Abstract
In this paper, a computationally efficient chance-constrained
rollover-free motion planning method is presented. Specifically, the
method is developed to plan motions for reconfigurable vehicles with the
knowledge of a 3-D terrain model that has limited accuracy. The overall
motion planning problem is formulated as a nonlinear optimal control
problem (NOCP) that employs a constraint in the form of a bound on the
probability of rollover under terrain-induced vehicle orientation
uncertainty. To increase the computational efficiency of the NOCP, a
geometric interpretation of the chance constraint is derived based on
the characteristics of SO(3), the 3-D rotation group. Monte Carlo
simulations are provided to demonstrate the usefulness of the geometric
interpretation through comparisons with other methods. Experimental data
gathered from driving a mobile robot through real forests are also used
to validate the proposed model. Finally, path and trajectory generation
results obtained with the proposed planning method for a feller-buncher
machine traversing through uncertain 3-D terrain are presented to
showcase the method’s overall performance and efficiency.