Updated 03 Apr 2019
This is a fully configurable MATLAB project that implements and provides simulation for vehicle self-learning of collision avoidance and navigation with a rangefinder sensor using an evolutionary artificial neural network. The neural network guides the vehicle around the environment and a genetic algorithm is used to pick and breed generations of more intelligent vehicles.
The vehicle uses a rangefinder sensor that calculates N intersections depths with the environment and then feeds these N values as inputs to the neural network. The inputs are then passed through a neural network and finally to an output layer of 2 neurons: a left and right steering force. These forces are used to turn the vehicle by deciding the vehicle steering angle.
Each vehicle represents a different chromosome in a generation (or a unique set weight for the neural net) which are evaluated and potentially carried through to the next generation by a fitness score. The fitness score has different definition in each of my three experiments for collision avoidance and navigation self-learning.
Hesham Eraqi (2021). GA-NN-Car (https://github.com/heshameraqi/GA-NN-Car), GitHub. Retrieved .
Eraqi, Hesham M., et al. “Reactive Collision Avoidance Using Evolutionary Neural Networks.” Proceedings of the 8th International Joint Conference on Computational Intelligence, SCITEPRESS - Science and Technology Publications, 2016, doi:10.5220/0006084902510257.
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