  Latest research in industrial robotics is aimed at making human robot
collaboration possible seamlessly. For this purpose, industrial robots are
expected to work on the fly in unstructured and cluttered environments and
hence the subject of perception driven motion planning plays a vital role.
Sampling based motion planners are proven to be the most effective for such
high dimensional planning problems with real time constraints. Unluckily random
stochastic samplers suffer from the phenomenon of 'narrow passages' or
bottleneck regions which need targeted sampling to improve their convergence
rate. Also identifying these bottleneck regions in a diverse set of planning
problems is a challenge. In this paper an attempt has been made to address
these two problems by designing an intelligent 'bottleneck guided' heuristic
for a Rapidly Exploring Random Tree Star (RRT*) planner which is based on
relevant context extracted from the planning scenario using a 3D Convolutional
Neural Network and it is also proven that the proposed technique generalises to
unseen problem instances. This paper benchmarks the technique (bottleneck
guided RRT*) against a 10% Goal biased RRT star planner, shows significant
improvement in planning time and memory requirement and uses ABB 1410
industrial manipulator as a platform for implantation and validation of the
results.
