Here we are at the business end of the schools.
We are planning now what is part planning?
Part planning in simpler terms is estimating apart from a start to a goal go in robot navigation.
This amounts to the set of states that a robot needs to achieve to reach its goal.
These states can be the position and orientation in the respective animation space.
For example, in our case, there are several roles that the robot would take, but path planning algorithm
will explicitly only find the path that leads to the maze exit.
Now let us talk about the different types of path planning algorithms.
So there are two types of path planning algorithms.
Starting with the first type, there is the grid based path, then it's the grid based partners finds
a path based on the minimum travel goals of a grid map.
Now because a proper grade is maintained for any case.
So this makes it impractical for higher dimensional cases.
Also because a grid is maintained.
This means that we are most more than likely to find a solution.
About the example, algorithms that are grid based path planners are DFS and DFS and shortest path finding
algorithms like Dijkstra and stuff.
The second type of planning algorithms are sampling based venues, unlike the grid based path that is,
these two cells by randomly sampling points.
So no grade is maintained and the traversal is not based on coordinates but rather on the basis of randomly
sampled point that satisfy a given criteria.
That criteria is that this shouldn't be on the obstacle and are within the reasonable distance.
So because nobody's maintained this makes it suitable for both low and high dimensional cases.
That's providing us an impression, or should I say, a rather more practical solution?
Here I mentioned that we've got an efficient solution, but this came at a cost.
That is, that we may fail to find a solution if it only ran for a finite amount of time.
The example algorithms that are sampling based partners are already and are already starting now.
Which type of algorithms are we going?
All depends on our case.
So the case that we have is that we have performed to degrade mapping of Mars in the previous step for
the occupancy grid, as that was a low dimensional case.
So the great miss mapping is the go to choice here.
For the goals that you want to achieve.
There are two goals that you want to achieve in this particular module, starting with the first goal.
That is, we want to find the first available path from the start to the main exit.
This is suitable for the case when we are only looking to find a path and not the most optimal one.
Also we have an unweighted graph meaning traversal from any node.
All different nodes carry equal weight in the example algorithms.
For the fourth finding algorithms are DFS and BFS and will be implementing the DFS algorithm and BFS
algorithm will be left for the students to implement.
The second and the last thing that you want to achieve are the shortest path that both planners.
These, as the name suggests, are used to retrieve the shortest part from the given start to the means
exit.
These are suitable.
Finding the shortest path is a requirement.
Also we have the vertical graph available, meaning traversing from any node towards distant nodes carry
different British, so we would know which reversal is the most optimal choice.
The example algorithm for shortest path path planners are distraught.
And a star.
So let's implement them.