Abstract: Traditional image-based visual servo control needs to calculate Jacobian matrix and inverse of Jacobian
matrix，which is complex in structure，large amount calculation and unsatisfactory real-time performance． The
BP neural network optimized by PSO(Particle Swarm Optimization) genetic algorithm realizes the vision servo
control of“eye on hand”robot by learning the mapping relationship between image feature space and robot
motion space． By optimizing the weight and threshold of BP neural network，the disadvantages of long training
time and slow convergence speed are prevented． The experimental results show that the optimized algorithm has
high efficiency． The designed controller can make the robot end actuator reach the expected position in a shorter
time． The average error between the actual value and the expected value of the motion position of the image
feature points is about two pixels，which has good convergence speed and control accuracy． The relevant
conclusions can provide the basis for the optimization of robot visual servo control and improve the application
performance of the algorithm．

Key words:  , particle swarm optimization-genetic algorithm-BP (PSO-GA-BP) neural network, visual servo,
particle swarm optimization (PSO), genetic algorithm (GA)