RBF network based motion trajectory optimization for robot used in agricultural activities

Abstract

At present, the efficiency of the method to track and predict motion trajectory of fruit and vegetable picking robot was low and the realization process was complex. Therefore, a research on motion trajectory optimization of fruit and vegetable picking robot based on RBF network was proposed. After analyzing the reason for data class imbalance of fruit and vegetable picking robot, this paper introduced the processing technology MWMO in RBF network. Then, the MWMO technology was embedded in the tracking and prediction research of motion trajectory optimization of fruit and vegetable picking robot. Moreover, the semi-supervised learning algorithm was used as the framework and integrated the processing technology of data class imbalance of motion trajectory to improve the efficiency of tracking and prediction of fruit and vegetable picking robot. According to the integration result, combined with the idea about the calculation of spatial function and the tracking and prediction of motion trajectory in RBF network, we designed the matching principle of trajectory similarity of time and space and realized the matching between the predicted position and the actual position, so that the tracking and prediction of fruit and vegetable picking robot could be completed. Experimental results show that the average calculation time of proposed method is 2.0S, which is only half of average time of traditional tracking and prediction method. It fully proves that the proposed optimization method can accurately track and predict the motion trajectory of fruit and vegetable picking robot. The prediction efficiency is higher and the time consumptionis shorter.

Actuators and Sensors for Application in Agricultural Robots: A Review

“…Automatic navigation technology mainly includes environmental perception, path planning, vehicle model establishment, and steering control. The current agricultural vehicle models include kinematics and vehicle dynamics [76][77][78]. The major control methodologies adopted in agricultural robots consist of classical control, intelligent control, including fuzzy control, and nonlinear control, as shown in Table 1.…”