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Minimum time angular waypoint trajectory #190

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rraetz opened this issue Nov 10, 2023 · 1 comment
Open

Minimum time angular waypoint trajectory #190

rraetz opened this issue Nov 10, 2023 · 1 comment

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@rraetz
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rraetz commented Nov 10, 2023

We have a Gen3 and compute waypoints in joint space that we want to execute as fast as possible. I adopted the waypoint example of the python API.

From the manual I understand that I have three options:

Users have the option to apply constraints to trajectories. There are three options available for
constraints:
Duration - the time period (in seconds) in which the trajectory is to be carried out
Speed - the maximum speed (meters/second for Cartesian, degrees/second for angular) of the
motion while carrying out the trajectory
No constraint - the robot will go to the endpoint without the time or speed being

However, when I do not set the duration and run the validation, I get a message that the time-optimal spline interpolation is not implemented yet.

How can I obtain a minimum-time execution of the waypoints? Is there a way of pre-computing the minimum duration manually and then set the duration accordingly? Maybe you can specify what kind of motion profile is used to compute the angular waypoint trajectory?

Many thanks for your advise!

@martinleroux
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Hi @rraetz .

Apologies for the very delayed response, as I have been saying in multiple other threads, I am in the process of cleaning up our list of issues to breathe some life into our online presence.

Regarding your question, there is currently no way to do this with the functions available from the API. You could either move to cartesian waypoint trajectories - optimal blending does optimize for time. Alternatively, you can compute a trajectory that does run, but define all of your durations as a duration * scaling factor and then reduce the scaling factor until the robot can't make it anymore. This is admittedly not a time-optimal solution, but is the best solution I can think of to reduce the duration of the trajectory as much as possible.

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