Hi!
We are getting back to posting about Stanley here. We’ll start by getting up to speed with some milestones we achieved a while back and we’ll get to present ones very soon so stay tuned!
First, take a look at Stanley Standing up for the first time and having a good stretch here:
You can see here the wide range of motion Stanley is capable of. Achieving that was the next step after solving inverse kinematics for a single leg. Body kinematics is a layer on top of leg kinematics that calculates the Cartesian position of each leg tip needed to achieve the commanded position of Stanley’s body. That leg tip position is then fed into the algorithm for leg inverse kinematics and that’s how we get positions for each of the 12 motors.
After that it came time for Stanley's first steps:
The walk here is still pretty clumsy - slow and jittery. Stanley is walking tethered and the lab PSU is a limiting factor (although the PSU could easily handle average power consumption, the high momentary peaks were overwhelming it). PD parameters at the time were way too stiff (P was too high) and that is what made the robot so bouncy.
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I love this project and want to see it advance so, I suggest using a simulation of the robot to train a neural network how to move efficiently. A proper simulation could evolve countless generations of neural networks without human interaction. However, trying to write a program to compensate for every changing condition (climbing/slipping/partial failures/etc.) is an inefficient use of time (unless you are glutton for mathematics of ever-increasing complexity).
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