Modeling And Optimization Of Multi-species Single Site Exoskeleton Human-machine Production Line System

In real industrial production and processing scenarios,there is a production and processing model in which workers use the power assistance of exoskeleton robot to carry and classify and store the workpiece on the conveyor belt of production line,which is called the exoskeleton man-machine production line system.As a representative intelligent production and processing model in the fourth industrial revolution,it is of great practical significance to study the modeling and optimization of the exoskeleton man-machine production line system.This thesis mainly considers the modeling and optimal control of human-computer assist in a single site exoskeleton human-computer production line system for the production and processing of multiple kinds of workpiece.In this system,many kinds of artifacts at random to carry on the conveyor belt site,workers at the site using the exoskeleton robot dynamic auxiliary production line conveyor belt workpiece handling and classified storage,at the same time to protect the production line workers due to accumulated fatigue or even the body injury problems,the introduction of mandatory rest time after excessive fatigue protection mechanism.The optimization goal of the system is to coordinate and optimize the level of intelligent integration between the exoskeleton robot and the physiological fatigue of the workers and the relative productivity of the production line by selecting the optimal power weighting strategy of the exoskeleton robot.Thesis first consider the exoskeleton robot machine production line system driven by an external power source,outside the skeleton robot handling phase type,production line workers of real-time fatigue level and the variety types of the artifacts as a system of the United States,in the aid of exoskeleton robot weight coefficient to control the decision variables,the infinite time expected cost minimum as the optimization goal,its weight distribution coefficient of power control problem is modeled as a markov decision process(SMDP)model.The mathematical model of muscle fatigue evaluation of production line workers in each handling stage of the system is presented.The establishment of the model provides a mathematical basis for the actual operation of the simulation system based on the Q-learning algorithm based on simulated annealing.In the simulation,the optimization curve of the Q-learning algorithm is given,and the influence of different strategies and different kinds of workpiece arrival rate parameters on the system performance is analyzed and discussed.The simulation results verify the rationality of the model and the effectiveness of the optimization learning algorithm.In order to meet the practical needs of the exoskeleton robot to move flexibly in the system,the man-machine production line system driven by the exoskeleton robot battery is further considered.The system comes with its own battery for flexible mobility,but the limited power capacity makes battery replacement frequency another factor affecting system performance.Therefore,in this thesis,the residual capacity of battery is further added as the joint state of system optimization control,and the battery consumption model and the muscle fatigue evaluation model of battery replacement process are presented.After the complete reinforcement learning model was established,the optimal power contribution coefficient of the system under the average criterion was obtained by using the Q-learning algorithm based on simulated annealing.The experimental results show that the optimal or sub-optimal power assist control strategy can be obtained by the model and optimization algorithm.