  Time-optimal obstacle avoidance is a prevalent problem encountered in various
fields, including robotics and autonomous vehicles, where the task involves
determining a path for a moving vehicle to reach its goal while navigating
around obstacles within its environment. This problem becomes increasingly
challenging as the number of obstacles in the environment rises. We propose an
iterative active-inactive obstacle approach, which involves identifying a
subset of the obstacles as "active", that considers solely the effect of the
"active" obstacles on the path of the moving vehicle. The remaining obstacles
are considered "inactive" and are not considered in the path planning process.
The obstacles are classified as 'active' on the basis of previous findings
derived from prior iterations. This approach allows for a more efficient
calculation of the optimal path by reducing the number of obstacles that need
to be considered. The effectiveness of the proposed method is demonstrated with
two different dynamic models using the various number of obstacles. The results
show that the proposed method is able to find the optimal path in a timely
manner, while also being able to handle a large number of obstacles in the
environment and the constraints on the motion of the object.
