  In this work we tackle the path planning problem for a 21-dimensional snake
robot-like manipulator, navigating a cluttered gas turbine for the purposes of
inspection. Heuristic search based approaches are effective planning strategies
for common manipulation domains. However, their performance on high dimensional
systems is heavily reliant on the effectiveness of the action space and the
heuristics chosen. The complex nature of our system, reachability constraints,
and highly cluttered turbine environment renders naive choices of action spaces
and heuristics ineffective. To this extent we have developed i) a methodology
for dynamically generating actions based on online optimization that help the
robot navigate narrow spaces, ii) a technique for lazily generating these
computationally expensive optimization actions to effectively utilize
resources, and iii) heuristics that reason about the homotopy classes induced
by the blades of the turbine in the robot workspace and a Multi-Heuristic
framework which guides the search along the relevant classes. The impact of our
contributions is presented through an experimental study in simulation, where
the 21 DOF manipulator navigates towards regions of inspection within a
turbine.
