  We describe and evaluate a novel optimization-based off-line path planning
algorithm for mobile robots based on the Counterexample-Guided Inductive
Optimization (CEGIO) technique. CEGIO iteratively employs counterexamples
generated from Boolean Satisfiability (SAT) and Satisfiability Modulo Theories
(SMT) solvers, in order to guide the optimization process and to ensure global
optimization. This paper marks the first application of CEGIO for planning
mobile robot path. In particular, CEGIO has been successfully applied to obtain
optimal two-dimensional paths for autonomous mobile robots using off-the-shelf
SAT and SMT solvers.
