Kinematic models for robots rarely match their implementations--axes may be slightly misallgned, part dimensions may differ from nominal, etc. To handle real world conditions, kinematic models are often extended to include error parameters. Unfortunately, the resulting kinematic models are often cumbersome and unwieldy to work with.
Kinann lets you create an artificial neural network (ANN) that bridges the gap between a simple, ideal kinematic model and any given implementation of that kinematic model. Kinann will handle all the error corrections automatically after proper calibration and training. Kinaan doesn't actually need to know the precise kinematics of your model--all it does is model the mismatch between ideal and actual coordinates. As long as your robot is precise, Kinann will make sure that your robot moves accurately to application coordinates:
Kinann kinematic error regression ANNs can be linear or even polynomial. Linear Kinann networks are often sufficient for Cartesian kinematics. However, you will need polynomial Kinann networks to deal with non-linear kinematics. For example, rotary delta kinematic errors often manifest as "bowl-shaped Z-plane errors".
npm to install kinann.
npm install kinann