Will it possible to make ML-based LSTM or RNN into the navigation system of the BlueROV2 ? with same specs
Hi @surya65321, welcome to the forum
You’ll need to provide some more information for us to be able to give any meaningful advice. LSTMs and RNNs are just types of neural networks, so whether and how you could use them for vehicle navigation depends very much on how you structure and train the network(s) you create, including what kind of inputs you allow and what kind of outputs it provides.
From a technological standpoint, the flight controller hardware is not designed to efficiently run a neural network, so if you’re intending to use a network of any significant size or complexity it likely makes sense to run the network on your own hardware (potentially on the topside computer), to generate control inputs that get sent to the autopilot via MAVLink messages.
Pymavlink may be of interest as a starting point for communicating with the autopilot.
Hi @EliotBR, Thank you for your quick reply
I want to create an autonomous navigation system for BlueRov2 vehicle using LSTM or RNN neural networks.
Collect data from the embedded IMUs, magnetic compasses, and gyroscopes on the BlueROV2’s flight navigation controller, and preprocess the data to prepare it for use as input to the neural network.
planning to design a neural network architecture that can take in the preprocessed sensor data and generate appropriate control signals to navigate the vehicle example by use of RNN or LSTM network to model the sensor data, while convolutional layers extract features and fully connected layers map them to output control signals. Train the network on a topside computer with supervised learning using labeled data or reinforcement learning through trial-and-error interactions.
As you said , I will train the neural network on a topside computer using a known environment.
I will implement the neural network-based navigation system on the BlueROV2, using MAVLink messages to communicate with the autopilot and send control signals to the vehicle.
5.Test the navigation system in a variety of conditions, and refine the system as needed.