  PDDL+ planning has its semantics rooted in hybrid automata (HA) and recent
work has shown that it can be modeled as a network of HAs. Addressing the
complexity of nonlinear PDDL+ planning as HAs requires both space and time
efficient reasoning. Unfortunately, existing solvers either do not address
nonlinear dynamics or do not natively support networks of automata.
  We present a new algorithm, called HNSolve, which guides the variable
selection of the dReal Satisfiability Modulo Theories (SMT) solver while
reasoning about network encodings of nonlinear PDDL+ planning as HAs. HNSolve
tightly integrates with dReal by solving a discrete abstraction of the HA
network. HNSolve finds composite runs on the HA network that ignore continuous
variables, but respect mode jumps and synchronization labels. HNSolve
admissibly detects dead-ends in the discrete abstraction, and posts conflict
clauses that prune the SMT solver's search. We evaluate the benefits of our
HNSolve algorithm on PDDL+ benchmark problems and demonstrate its performance
with respect to prior work.
