Genetic Algorithm-Based Optimization in Sorting Recyclable Waste using Robotic Arm

Amid escalating environmental concerns and the imperative for sustainable resource management, optimizing recycling processes has become a paramount challenge. This research proposes an advanced methodology for sorting recyclable waste by integrating a robotic arm with four degrees of freedom and a conveyor system, guided by a genetic algorithm (GA). The system processes waste represented as color-coded cubes with assigned values, categorizing them based on predefined metrics to maximize efficiency.To address the constraints of the robotic arm's limited operational range and the dynamic nature of the conveyor belt, a genetic algorithm with variable-length chromosomes was employed. This approach optimizes the sorting process by prioritizing high-value items while adhering to stringent temporal and spatial constraints. The methodology was simulated and validated using RoboDK software, with Python utilized for algorithm implementation.The findings demonstrate substantial improvements in sorting efficiency and cumulative value compared to traditional sequential methods. This study underscores the potential of integrating robotic systems with intelligent optimization algorithms to advance industrial recycling operations, enhancing automation efficiency and sustainable recycling practices at an industrial scale.