  One of the most difficult parts of motion planning in configuration space is
ensuring a trajectory does not collide with task-space obstacles in the
environment. Generating regions that are convex and collision free in
configuration space can separate the computational burden of collision checking
from motion planning. To that end, we propose an extension to IRIS (Iterative
Regional Inflation by Semidefinite programming) [5] that allows it to operate
in configuration space. Our algorithm, IRIS-NP (Iterative Regional Inflation by
Semidefinite & Nonlinear Programming), uses nonlinear optimization to add the
separating hyperplanes, enabling support for more general nonlinear
constraints. Developed in parallel to Amice et al. [1], IRIS-NP trades rigorous
certification that regions are collision free for probabilistic certification
and the benefit of faster region generation in the configuration-space
coordinates. IRIS-NP also provides a solid initialization to C-IRIS to reduce
the number of iterations required for certification. We demonstrate that
IRIS-NP can scale to a dual-arm manipulator and can handle additional nonlinear
constraints using the same machinery. Finally, we show ablations of elements of
our implementation to demonstrate their importance.
