Is there a mathematical solution for preventing fly-aways?
My understanding is that the following is what causes them:
- Accel z goes negative due to ship vibration versus an actual drop in altitude
- The ship corrects by adding power
- The added power causes a larger vibration-induced negative z
- The ship corrects the pseudo altitude drop by adding more power
- A fly away occurs
My understanding of mathematical solutions are:
- A filtered or weighted z only diminishes (does not solve) the effect.
Has anyone tried a "significant z", z / s, where s is the moving average of the variation of z?
- when the IMU isn't vibrating, s is low so the magnitude of z / s is high
... z is significant
... z can be trusted for use in altitude control
- when the IMU is vibrating, s is high so the magnitude of z / s is low
... z / s has less effect
... and will not caused a flyaway
"specific emergency algorithms" ?? when you lose a motor in a quad its game over. LAW of physics ..
... so detouring the main thread here ..
just ordered those parts. will test them for the response rate. if you friend me, i'll pass along the results to you.
just to confirm ... these props?
+1. This is also my intuition that visual camùera input is the only way out via the top.
Currently, the quad can land safely even one or two motors/prop out. However, it requires supporting components to monitor the motor/esc/prop conditions.
ok fair enough. a HW failure. I wish you all the best in improving EKF. I will continue to fly my drone until a HW failure happens then i will be ready to switch to stabilize.
can you show me that. like proof. never seen that before.
I have been playing with kinect/real sense image system for two years, the problems are the payload, distance and computational resource. I also tried the multi-view algorithm. I got it working on MATLAB
land safely does not mean straight down.. like a controlled descent DOWN does not mean safe landing. to land safely you have to be able to steer away from danger. Have lateral control...
If I am not wrong, this is what EKF is doing: takes into account various sensors measurements, evaluate them against a predicted evolution, and if there is a too big deviation versus forecast, then ignores them or diminishes their weight in the PID correction.