  Autonomous agents face the challenge of coordinating multiple tasks
(perception, motion planning, controller) which are computationally expensive
on a single onboard computer. To utilize the onboard processing capacity
optimally, it is imperative to arrive at computationally efficient algorithms
for global path planning. In this work, it is attempted to reduce the
processing time for global path planning in dynamically evolving polygonal
maps. In dynamic environments, maps may not remain valid for long. Hence it is
of utmost importance to obtain the shortest path quickly in an ever-changing
environment. To address this, an existing rapid path-finding algorithm, the
Minimal Construct was used. This algorithm discovers only a necessary portion
of the Visibility Graph around obstacles and computes collision tests only for
lines that seem heuristically promising. Simulations show that this algorithm
finds shortest paths faster than traditional grid-based A* searches in most
cases, resulting in smoother and shorter paths even in dynamic environments.
