  Real-time and efficient path planning is critical for all robotic systems. In
particular, it is of greater importance for industrial robots since the overall
planning and execution time directly impact the cycle time and automation
economics in production lines. While the problem may not be complex in static
environments, classical approaches are inefficient in high-dimensional
environments in terms of planning time and optimality. Collision checking poses
another challenge in obtaining a real-time solution for path planning in
complex environments. To address these issues, we propose an end-to-end
learning-based framework viz., Path Planning and Collision checking Network
(PPCNet). The PPCNet generates the path by computing waypoints sequentially
using two networks: the first network generates a waypoint, and the second one
determines whether the waypoint is on a collision-free segment of the path. The
end-to-end training process is based on imitation learning that uses data
aggregation from the experience of an expert planner to train the two networks,
simultaneously. We utilize two approaches for training a network that
efficiently approximates the exact geometrical collision checking function.
Finally, the PPCNet is evaluated in two different simulation environments and a
practical implementation on a robotic arm for a bin-picking application.
Compared to the state-of-the-art path planning methods, our results show
significant improvement in performance by greatly reducing the planning time
with comparable success rates and path lengths.
