This invention belongs to the field of
robotic arm motion planning and control technology, and discloses a control method for a harvesting
robotic arm based on density clustering and agent integration. First, the
DBSCAN algorithm is used to simplify the harvesting
scenario, determining the objective function, constraints, input
state space, action space, reward function, and termination condition of the agent in the harvesting
scenario model. Based on the clustering results of the
DBSCAN algorithm, the agent is prepared for training. Then, the selected agent
algorithm is trained. Finally, an error reciprocal weighted
combination method based on agent reward is used to integrate the
motion control decisions of each trained agent algorithm for the same
robotic arm and accumulate the total
reward value after integration
processing. This invention is applicable to the control of harvesting robotic arms, realizes multi-angle decision-making, effectively compensates for the shortcomings of single algorithms, improves the accuracy of robotic arm motion, and thus provides reliable support for modern
agricultural automation technology.