Eight months ago he built a quadrupedal robot that could step sideways using three of them per leg. I’m not going to link that, you’ll have to find it from his YouTube page because you should look around.
Same issue covered on HN a few weeks ago.[1] This one has more motor theory but less machine learning theory.
Too much gear reduction, and you can't back-drive or sense forces from the motor end. Too little gear reduction, and your motors are too bulky or too weak. Reflected inertia goes up as the square of the gear ratio, as the article points out, because the gear ratio gets you both coming and going. So high gear ratios really hurt.
Robots, like drones, need custom motors sized for the specific requirements of the joint. For a long time, the robotics industry was too tiny to get such custom motors engineered, and had to use motors designed for other purposes. This will become a non-problem as volume increases. Especially since 3-phase servomotor controllers, which drones need, are now small and cheap. They used to be the size of a paperback book or larger.
(I've been out of this for years. I've used hydraulic robots and R/C servo powered robots.
The newer machinery sucks a lot less.)
I'm used to approaching these problems from a slightly different angle. There are two simple cases that can be used to establish guide points.
If you have a load that is purely inertial, the optimum gear ratio (to minimize I-squared-R motor loss) is found by picking a gear ratio which matches the reflected inertia of load and motor. At this point, on every acceleration you put just as much energy into the rotor inertia as into the load inertia.
In contrast, for a steady-state load which is all friction (e.g. a mixer such as for paint or food), a gear ratio which balances friction loss in the motor with the load friction will minimize the armature loss.
Most applications have live between these points, and these optimizations ignore gearing losses and expense and noise, but they can serve as guide posts.
There's also the issue of separating winding choice from gearing choice. For each candidate motor there exists an optimum gear ratio which will minimize the heat produced when driving a given load (friction and inertia) over a given velocity profile. That gearing can be found by trial and error in a simulation. These aren't crazy difficult simulations (can be done in a spreadsheet) but do need to take temperature dissipation and change of motor performance with temperature into account. Once that gearing is found, the V-I requirements of the motor at that gearing will be known and then winding adjusted to fit requirements of driver circuitry (i.e. trade current for voltage).
So is the takeaway here that motors should be individually sized for the specific torque application to minimize the size of the motor - within thermal constraints? Larger motors direct drive or smaller motors with geartrains will perform similarly?
I wonder if robots could be made to work better at cryogenic temperature, so superconductors could be used. The figure of merit would be much higher if resistance was zero. Or maybe this is another reason to want room temperature superconductors.
Why can't we just dump massive currents into spring returning solenoids with ~5mm or ~1/4" range of motion, and amplify that motion through tendon systems for whole joint motion ranges?
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https://youtube.com/watch?v=MwIBTbumd1Q
Eight months ago he built a quadrupedal robot that could step sideways using three of them per leg. I’m not going to link that, you’ll have to find it from his YouTube page because you should look around.
Too much gear reduction, and you can't back-drive or sense forces from the motor end. Too little gear reduction, and your motors are too bulky or too weak. Reflected inertia goes up as the square of the gear ratio, as the article points out, because the gear ratio gets you both coming and going. So high gear ratios really hurt.
Robots, like drones, need custom motors sized for the specific requirements of the joint. For a long time, the robotics industry was too tiny to get such custom motors engineered, and had to use motors designed for other purposes. This will become a non-problem as volume increases. Especially since 3-phase servomotor controllers, which drones need, are now small and cheap. They used to be the size of a paperback book or larger.
(I've been out of this for years. I've used hydraulic robots and R/C servo powered robots. The newer machinery sucks a lot less.)
[1] https://news.ycombinator.com/item?id=47184744
If you have a load that is purely inertial, the optimum gear ratio (to minimize I-squared-R motor loss) is found by picking a gear ratio which matches the reflected inertia of load and motor. At this point, on every acceleration you put just as much energy into the rotor inertia as into the load inertia.
In contrast, for a steady-state load which is all friction (e.g. a mixer such as for paint or food), a gear ratio which balances friction loss in the motor with the load friction will minimize the armature loss.
Most applications have live between these points, and these optimizations ignore gearing losses and expense and noise, but they can serve as guide posts.
There's also the issue of separating winding choice from gearing choice. For each candidate motor there exists an optimum gear ratio which will minimize the heat produced when driving a given load (friction and inertia) over a given velocity profile. That gearing can be found by trial and error in a simulation. These aren't crazy difficult simulations (can be done in a spreadsheet) but do need to take temperature dissipation and change of motor performance with temperature into account. Once that gearing is found, the V-I requirements of the motor at that gearing will be known and then winding adjusted to fit requirements of driver circuitry (i.e. trade current for voltage).