Add curvature limit to planner

[gerth2]

Currently, while navigating down local minima "troughs", the path planner will zig-zag across the gradient line.

It would be better if we smoothed these out. I propose to do that by imposing a maximum curvature - basically, given a certain direction of travel (from the current command), there is a maximum change in the angle of that direction of travel - the next-step logic needs to enforce this by not picking a next goal location which is at too great of an angle.

[gerth2]

Note: If this doesn't pan out... there is another more computationally-intensive option - basically, detect the rapid change in angle, and then take smaller steps until the oscillation subsides.

Detecting this is hard (maybe need to look for a zero crossing in the derivative of the force 😬 ?). Taking the smaller steps to find the real minima is potentially unbounded... and fraught, depending on how small our local minima end up being.

[gerth2]

From a coding perspective, and from an algorithm perspective (might be a good @schmile question), I think the smaller steps is probably the way to go.

Basically: 1) At each step, use a for loop (with max iter) to take a fraction of a step 2) Check if the result of taking that step has moved us at least one full step size away from the starting point 3) repeat with another fraction of a step 4) Report "stuck" if after max iterations we're done.

The big thing is that this will get kinda computationally intensive with lots of obstacles on the field

[schmile]

I think that is always the compromise and we would have to see if there is a way to plan "as much as possible"

On Wed, Oct 2, 2024 at 2:47 PM Chris Gerth @.***> wrote:

From a coding perspective, and from an algorithm perspective (might be a good @schmile https://github.com/schmile question), I think the smaller steps is probably the way to go.

Basically:

1. At each step, use a for loop (with max iter) to take a fraction of a step
2. Check if the result of taking that step has moved us at least one full step size away from the starting point
3. repeat with another fraction of a step
4. Report "stuck" if after max iterations we're done.

The big thing is that this will get kinda computationally intensive with lots of obstacles on the field

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