Obstacle Avoidance by a Mobile Robot Using a Nuro-Genetic Controller System
DOI:
https://doi.org/10.59992/IJCI.2026.v5n8p2Keywords:
Neuro -Genetic Controller, Mobile Robots, Obstacle AvoidanceAbstract
By applying Darwinian principles like natural selection and survival of the fittest, neural controllers can be developed that enable robots to act autonomously in unpredictable or unknown environments. This study examines the Genetic Algorithm (GA) as an optimization tool for mobile robot navigation. The controller's performance was evaluated on a standard obstacle-avoidance task, as well as a multi-task setup combining obstacle avoidance with homing. Experimental results confirm that neuro-genetic controller optimization significantly enhances both average population fitness and individual peak fitness, while demonstrating the feasibility of integrating cooperative functional modules to accomplish complex robotic goals.
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