Genetic Algorithm Based Pick and Place Sequence Optimization for a Color and Size Sorting Delta Robot

Abstract

Delta Robots are used in industry for light weight material handling and sorting. This paper presents a sequence optimizing methodology for a color and size sorting delta robot. It finds the optimum path in the task space to perform an industry emulated scenario. An OpenCV-Python program was developed to sort objects according to their colors and sizes. The static positional coordinates of the objects in the robot workspace are obtained using the program. Genetic algorithm is used for pick-and-place sequence optimization to ensure that the sorting process is performed in the shortest possible path. The static positional coordinates are used to calculate the fitness. Single point crossover and mutation are applied with elitist selection when the current generation evolves to the next generation. The genetic algorithm ensures that the sequence of pick-and-place converges to the highest fitness in minimum number of generations reducing the computational time.