Better autonomous drone flying with Neural-Fly

Read an article the other day on Neural-Fly (see: Rapid adaptation of deep learning teaches drones to survive any weather) based on research out of CalTech documented in a paper is ScienceRobotics (see: Neural-Fly enables rapid learning for agile flight in strong winds, behind paywall).

Essentially they have trained two neural networks (NN) at the same time and computed an adaptation coefficient matrix (with linear multipliers to compensate for wind speed). The first NN is trained to understand the wind invariant flight characteristics of a drone in wind and the second is trained to the predict the class of wind the drone is flying in (or wind index). These two plus the adaptation control matrix coefficients are used to predict the resultant force on drone flight in wind.

In a CalTech article on the research (see: Rapid Adaptation of Deep Learning Teaches Drones to Survive Any Weather) at the bottom is a YouTube video that shows how well the drone can fly in various wind conditions (up to 27mph).

The data to train the two NNs and compute the adaptation matrix coefficients come from CalTech wind tunnel results with their custom built drone (essentially an RPi4 added to a pretty standard drone) doing random trajectories under different static wind conditions.

The two NNs and the adaptation control matrix functionality run on a Raspberry Pi 4 (RPi4) that’s added to a drone they custom built for the test vehicle. The 2 NNs and the adaptation control tracking are used in the P-I-D (proportional-integral-derivative) controller for drone path prediction. The Neural-Fly 2 NNs plus the adaptation functionality effectively replaces the residual force prediction portion of Integral section of the P-I-D controller.

The wind invariant neural net has 5 layers with relatively few parameters per layer. The wind class prediction neural network has 3 layers and even fewer parameters. Together these two NNs plus the adaptation coefficient provides real time resultant force predictions on the drone which can be fed into the drone controller to control drone flight. All being accomplished, in real time, on an RPi4.

The adaption factor matrix is also learned during 2 NN training. And this is what’s used in the NF-Constant results below. But the key is that the linear factors (adaptation matrix) are updated (periodically) during actual drone flight by sampling the measured actual force and predicated force on the drone. The adaption matrix coefficients are updated using a least squares estimation fit.

In the reports supplemental information, the team showed a couple of state of the art adaptation approaches to problem of drone flight in wind. In the above chart the upper section is the x-axis wind effect and the lower portion is the z-axis wind effect and f (grey) is the actual force in that direction and f-hat (red) is the predicted force. The first column represents results from a normal integral controller. The next two columns are state of the art approaches (INDI and L1, see paper references) to the force prediction using adaptive control. If you look closely at these two columns, and compare the force prediction (in red) and the actual force (in grey), the force prediction always lags behind the actual force.

The next three columns show Neural-Fly constant (Neural-Fly with a constant adaptive control matrix, not being updated during flight), Neural-Fly-transfer (Using the NN trained on one drone and applying it’s adaptation to another drone in flight) and Neural-Fly. Neural-Fly constant also shows a lag between the predicted force and the actual force. But the Neural-Fly Transfer and Neural-Fly reduce this lag considerably.

The measurement for drone flight accuracy is tracking positional error. That is the difference between the desired position and its actual position over a number of trajectories. As shown in the chart tracking error decreased from 5.6cm to ~4 cm at a wind speed of 4.2m/s (15.1km/h or 9.3mph). Tracking error increases for wind speeds that were not used in training and for NF-transfer but in all wind speeds the tracking error is better with Neural-Fly than with any other approach.

Pretty impressive results from just using an RPi4.

[The Eds. would like to thank the CalTech team and especially Mike O’Connell for kindly answering our many questions on Neural-Fly.]

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