  Trajectory optimization is a widely used technique in robot motion planning
for letting the dynamics and constraints on the system shape and synthesize
complex behaviors. Several previous works have shown its benefits in
high-dimensional continuous state spaces and under differential constraints.
However, long time horizons and planning around obstacles in non-convex spaces
pose challenges in guaranteeing convergence or finding optimal solutions. As a
result, discrete graph search planners and sampling-based planers are preferred
when facing obstacle-cluttered environments. A recently developed algorithm
called INSAT effectively combines graph search in the low-dimensional subspace
and trajectory optimization in the full-dimensional space for global
kinodynamic planning over long horizons. Although INSAT successfully reasoned
about and solved complex planning problems, the numerous expensive calls to an
optimizer resulted in large planning times, thereby limiting its practical use.
Inspired by the recent work on edge-based parallel graph search, we present
PINSAT, which introduces systematic parallelization in INSAT to achieve lower
planning times and higher success rates, while maintaining significantly lower
costs over relevant baselines. We demonstrate PINSAT by evaluating it on 6 DoF
kinodynamic manipulation planning with obstacles.
