Abstract
We propose HyDICE, Hybrid DIscrete Continuous Exploration, a multi-layered approach for hybrid-system testing that integrates continuous sampling-based robot motion planning with discrete searching. The discrete search uses the discrete transitions of the hybrid system and coarse-grained decompositions of the continuous state spaces or related projections to guide the motion planner during the search for witness trajectories. Experiments presented in this paper, using a hybrid system inspired by robot motion planning and with nonlinear dynamics associated with each of several thousand modes, provide an initial validation of HyDICE and demonstrate its promise as a hybrid-system testing method. Comparisons to related work show computational speedups of up to two orders of magnitude.
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