Research On Robot Shared Control Based On Sensor Information And Operator Status

In the new era of rapid development of science and technology,in order to meet the ever-increasing needs of users,robotics technology continues to develop and become more mature.However,due to the limited technical level of intelligent control and sensors at this stage,it is currently not possible to directly control robots by humans or rely on robots to operate completely autonomously to deal with relatively complex tasks.Therefore,in improving the robot's autonomous control and control capabilities,the will and decision-making of human operators are combined with the robot's autonomous control system to achieve a shared control strategy.However,there is still a lot of room for improvement in the combination of robots and operators in the current shared control system,and the control loops of the two are relatively independent.Therefore,the key to the study of shared control is to design reasonable control weights to achieve human-machine fusion and efficient control systems.In this paper,the shared control system is studied for the above problems,including the use of adaptive fuzzy neural network system to realize the shared control system based on sensor information;the use of TGAM-based EEG acquisition equipment to collect EEG signals,and propose feature extraction algorithms,To achieve the task of classifying the EEG signals of the operator in different mental states,so as to identify the state of the operator;propose a robot shared control system that integrates the state of the operator and sensor information,and the specific content is introduced as follows:First,the research background and significance of the robot shared control system are investigated and studied,and a shared controller based on sensor information is designed.In this paper,the environment information collected by sensors is used to obtain the distance between the robot and surrounding obstacles and the movement state of the robot.Based on this information,the shared weight regulator is designed to dynamically adjust the direct control commands of remote operation and the autonomous control commands of the robot.The weight value ensures that the robot responds to the external environment in time while correctly executing the operator's intention.At present,it is common to use fuzzy inference to realize shared control method,but fuzzy control has problems such as lack of design method and time-consuming adjustment of function parameters.In view of the above problems,this paper uses Adaptive Network-based Fuzzy Inference System(ANFIS)to design the shared weight adjustment regulator,combining the advantages of fuzzy logic and neural network,can adaptively adjust the shared control weight,and achieve Efficiently execute the operator's control instructions while being able to cope with the external environment.And through a series of comparative experiments,the feasibility and superiority of this algorithm model are verified.Secondly,this article studies the status of the operator.The state of the operator refers to the state of movement of the limbs,physical condition and mental state of the body when performing a certain task,which will have a relatively large impact on work efficiency.Common methods for detecting state features include facial features,physiological features,behavioral features,and EEG features.This paper designs an operator state recognition algorithm based on EEG signals,and designs an EEG signal acquisition system based on TGAM to collect EEG signals,concentration and relaxation data,and then de-artifact and extract the EEG signals.The fusion characteristics of frequency,concentration and relaxation will finally realize the recognition task of the operator's state,which is the basis for the subsequent integration of the operator's state into the shared control strategy.Finally,this article adds operator status information and sensor information to the shared control strategy,strengthens the connection between the operator and the robot,considers the robot environment information while fully considering the impact of the operator's status on the operation,and improves the performance of the robot system.effectiveness.This article builds a robot shared control system,including the robot motion control module,the operator status recognition module,the weight regulator module and the network communication module.Then the algorithm is verified through simulation experiments.From the experimental results,it can be seen that the robot shared control algorithm based on operator status and sensor information proposed in this paper can safely and effectively control the operation of the robot.At the end of the article,it summarizes the content and innovations of the full text,and looks forward to the future.