Research on Genetic Algorithm Optimization of Agricultural Robot Picking Task Planning

This study focuses on the genetic algorithm (GA) optimization of agricultural robot picking task planning. With the rapid development of agricultural science and technology, agricultural robots play an important role in improving picking efficiency and reducing labor costs. However, the complicated and changeable orchard environment poses a challenge to the picking task planning. As a heuristic search algorithm simulating natural selection and genetic mechanism, GA can effectively solve this complex optimization problem. In this study, a GA optimization model was constructed. By coding the picking points and sequence, designing a fitness function, taking into account the path length, picking efficiency, energy consumption and operating costs, and adopting roulette wheel selection, partial mapping crossover and reverse mutation, the intelligent planning and optimization of picking tasks were realized. The experiment was carried out in a simulated orchard environment. The results show that the path optimized by GA significantly reduces the walking distance of the robot, improves the working efficiency, and reduces the energy consumption and operating costs. Compared with traditional methods, GA shows higher automation level, stronger adaptability and better economic benefits, which provides strong support for intelligent operation of agricultural robots.