I've seen in the past a different trick that is adding an IMU[1] to the robot arm. When combining two different types of sensors, it's called Sensor Fusion[2], and it's really common to put together a IMU with GPS and slap a Kalman Filter[3] for very accurate position reading.
The particularly cool thing of this video though is that they could mount the new sensor within the motor itself, making it all a lot more compact.
If anyone wants to build this sort of thing the new Raspberry Pie Pico 2 is both orders of magnitude more capable than the chip used here and also around half the price.
It's by far the best value for money for an introductory 32bit ARM/Risk embedded device right now.
You can’t judge backlash by how the robot repeats the exact same set of movements over and over. That removes hysteresis from the problem definitionally.
Question for anyone who has used one of these analog measuring devices: the indicator seems to go all the way around before the camera zooms in to read the indicated value. Is this video actually showing the accuracy it is claiming?
I haven’t watched the whole video, but I’m assuming what they were showing was ‘move x from 0.00 to 10.00’ with the gauge showing the final move was to (actual) 10.05.
Which with how floppy that rig is, is pretty impressive.
Notably though, those gauges do need to be ‘preloaded’ (compressed into their ‘positive’ range) to be able to measure negative direction shifts, and while it looks like that was done, I can’t be 100% sure without analyzing it far more than I want to do right now.
Also, those gauges provide a degree of preload (not much, but some), which might be taking a bunch of slop out of the system and giving overly rosy accuracy numbers.
I'm not a fan of the youtube link trend on HN, as cool as the latest robots are. I know they're encroaching on territory previously held by much heavier additive and subtractive machines.
And I am okay with YouTube when a video makes sense, but in this case they have basically crammed a short article into a video, making it more awkward to read: slides with texts and diagrams, with some background music, and only a video demonstration in the end.
Are you saying you don't like video and would prefer text, or is it something specific to YouTube that you object to? For many topics, video is really helpful in understanding stuff.
It's a mix of both, I guess. I don't like youtube taking over for text based sources. It's less accessible, way less efficient, and feeds into the google surveillance machine.
I sort of struggle to see how getting good positioning accuracy from a high backlash system under zero load can have a useful application.
Maybe just lack of imagination on my part.
There is this trend that says make and buy bad hardware, the software will solve it. I haven't noticed that paying off. Tesla using webcams for self driving is an example. Boeing designing their planes and then using faulty attitude sensors is another.
I would be way more impressed if the robot did something useful. My suspicion is that its real world application capabilities are rather limited.
You have oversimplified the Boeing one: their goal was to create an efficient plane to compete with Airbus without needing the expense and delays of a new type certification.
To do this they needed bigger engines on the same frame, which in turn needed to be mounted further forward affecting flight characteristics and requiring retraining. Retraining would be a sales killer so they hacked on some software systems to attempt to make the plane fly like an older 737.
Then they can just use an iPad training course for pilots to upgrade. The augmentation had to avoid the pilot knowing about (I think) the plane getting stuck in a stall at a too high AoA (this is where my memory might be off...) so the MCAS software uses AoA sensors to nose down based on the detected AoA.
The AoA sensors were never designed to be used for a direct life and death critical use case and sometimes they got stuck or failed. MCAS only used one as an input. If MCAS incorrectly asseses a nose down is required and the pilot follows their 737 training they are having their last day. That plane is going down.
Bascially people were murdered by Boeing so at every stage of this wretched plan they can make more money.
I think you are right but Boeing was more of perhaps the worst possible asshole design, and deserves it's own league.
There’s some really negligent stuff, like changing how to disable auto pilot (ie, MCAS) — as the pilots of both crashed planes attempted actions that would have disabled the autopilot on previous models.
Wasn’t the Boeing issue completely preventable with an inconsequential extra part that cost nothing? Like the short cuts actually worked but they literally went all the way to almost succeeding and snatched defeat from the jaws of victory. (Aside from all the other things they did that also contributed to disaster situations going worse)
I don't know. Maybe an expert can chime in but I think it is a hard problem because of ice etc. I think the 737Max has the problem where AoA matters more because you can get into a stall you can't get out of.
Whereas maybe before on older planes you get in a stall and you nose down to reduce AoA. You don't need a sensor look at altitude etc.
So now you need perfect ten nines of reliability sensors.
What their video demonstrates is mostly same-direction repeatability, not absolute static accuracy. They can correct for with backlash at individual motors, but not slop or bend in the linkages.
This uses DC motors. If you use modern 3-phase servomotors, you know more of what the motor is doing.
I've seen in the past a different trick that is adding an IMU[1] to the robot arm. When combining two different types of sensors, it's called Sensor Fusion[2], and it's really common to put together a IMU with GPS and slap a Kalman Filter[3] for very accurate position reading.
The particularly cool thing of this video though is that they could mount the new sensor within the motor itself, making it all a lot more compact.
[1] https://en.wikipedia.org/wiki/Inertial_measurement_unit
[2] https://en.wikipedia.org/wiki/Sensor_fusion
[3] https://en.wikipedia.org/wiki/Kalman_filter
He goes into detail on the control algorithm of the project on the github. It's rather complicated... https://github.com/adamb314/ServoProject/blob/main/Doc/Theor...
If anyone wants to build this sort of thing the new Raspberry Pie Pico 2 is both orders of magnitude more capable than the chip used here and also around half the price.
It's by far the best value for money for an introductory 32bit ARM/Risk embedded device right now.
You can’t judge backlash by how the robot repeats the exact same set of movements over and over. That removes hysteresis from the problem definitionally.
But they're not the same motions? The second move is to the other side.
Question for anyone who has used one of these analog measuring devices: the indicator seems to go all the way around before the camera zooms in to read the indicated value. Is this video actually showing the accuracy it is claiming?
I haven’t watched the whole video, but I’m assuming what they were showing was ‘move x from 0.00 to 10.00’ with the gauge showing the final move was to (actual) 10.05.
Which with how floppy that rig is, is pretty impressive.
Notably though, those gauges do need to be ‘preloaded’ (compressed into their ‘positive’ range) to be able to measure negative direction shifts, and while it looks like that was done, I can’t be 100% sure without analyzing it far more than I want to do right now.
Also, those gauges provide a degree of preload (not much, but some), which might be taking a bunch of slop out of the system and giving overly rosy accuracy numbers.
How do 3 axis robots you can buy for 100$ (3D printers) have a static accuracy of 0.05mm?
It's not control theory, but mechanics and steppers.
It’s the compromise between a gantry vs arm design
I'm not a fan of the youtube link trend on HN, as cool as the latest robots are. I know they're encroaching on territory previously held by much heavier additive and subtractive machines.
And I am okay with YouTube when a video makes sense, but in this case they have basically crammed a short article into a video, making it more awkward to read: slides with texts and diagrams, with some background music, and only a video demonstration in the end.
Are you saying you don't like video and would prefer text, or is it something specific to YouTube that you object to? For many topics, video is really helpful in understanding stuff.
It's a mix of both, I guess. I don't like youtube taking over for text based sources. It's less accessible, way less efficient, and feeds into the google surveillance machine.
Unlike the web, youtube has a functional search ;)
Great, but that robot isn't doing an actual task?
I sort of struggle to see how getting good positioning accuracy from a high backlash system under zero load can have a useful application.
Maybe just lack of imagination on my part.
There is this trend that says make and buy bad hardware, the software will solve it. I haven't noticed that paying off. Tesla using webcams for self driving is an example. Boeing designing their planes and then using faulty attitude sensors is another.
I would be way more impressed if the robot did something useful. My suspicion is that its real world application capabilities are rather limited.
You have oversimplified the Boeing one: their goal was to create an efficient plane to compete with Airbus without needing the expense and delays of a new type certification.
To do this they needed bigger engines on the same frame, which in turn needed to be mounted further forward affecting flight characteristics and requiring retraining. Retraining would be a sales killer so they hacked on some software systems to attempt to make the plane fly like an older 737.
Then they can just use an iPad training course for pilots to upgrade. The augmentation had to avoid the pilot knowing about (I think) the plane getting stuck in a stall at a too high AoA (this is where my memory might be off...) so the MCAS software uses AoA sensors to nose down based on the detected AoA.
The AoA sensors were never designed to be used for a direct life and death critical use case and sometimes they got stuck or failed. MCAS only used one as an input. If MCAS incorrectly asseses a nose down is required and the pilot follows their 737 training they are having their last day. That plane is going down.
Bascially people were murdered by Boeing so at every stage of this wretched plan they can make more money.
I think you are right but Boeing was more of perhaps the worst possible asshole design, and deserves it's own league.
There’s some really negligent stuff, like changing how to disable auto pilot (ie, MCAS) — as the pilots of both crashed planes attempted actions that would have disabled the autopilot on previous models.
Wasn’t the Boeing issue completely preventable with an inconsequential extra part that cost nothing? Like the short cuts actually worked but they literally went all the way to almost succeeding and snatched defeat from the jaws of victory. (Aside from all the other things they did that also contributed to disaster situations going worse)
I don't know. Maybe an expert can chime in but I think it is a hard problem because of ice etc. I think the 737Max has the problem where AoA matters more because you can get into a stall you can't get out of.
Whereas maybe before on older planes you get in a stall and you nose down to reduce AoA. You don't need a sensor look at altitude etc.
So now you need perfect ten nines of reliability sensors.
Look at this later videos where he has the servo lift a weight on a long arm.
that is a really great encoder trick. I wouldn't have thought it was that good, but it clearly is.
What their video demonstrates is mostly same-direction repeatability, not absolute static accuracy. They can correct for with backlash at individual motors, but not slop or bend in the linkages.
This uses DC motors. If you use modern 3-phase servomotors, you know more of what the motor is doing.
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