A combined stochastic and greedy hybrid estimation capability for concurrent hybrid models with autonomous mode transitions

Probabilistic hybrid discrete/continuous models, such as Concurrent Probabilistic Hybrid Automata (CPHA) are convenient tools for modeling complex robotic systems. In this paper, we present a novel method for estimating the hybrid state of CPHA that achieves robustness by balancing greedy and stochastic search. To accomplish this, we 1) develop an efficient stochastic sampling approach for CPHA based on Rao-Blackwellised Particle Filtering, 2) perform an empirical comparison of the greedy and stochastic approaches to hybrid estimation and 3) propose a strategy for mixing stochastic and greedy search. The resulting method handles nonlinear dynamics, concurrently operating components and autonomous mode transitions. We demonstrate the robustness of the mixed method empirically.