Robot system-oriented human-in-loop decision modeling and control method

Abstract

The invention provides a robot system-oriented human-in-the-loop decision modeling and control method. The method comprises the steps of robot task design, human decision information selection, human decision behavior modeling, human decision task design and fixed time sliding mode adaptive behavior controller design. The method comprises the steps: firstly acquiring an output information value after a robot executes a task, then selecting robot position deviation information and speed deviation information as decision information of a person, using a human decision drift diffusion model as a modeling method, modeling decision behaviors of the person according to the decision information of the person, after a decision threshold value is reached, selecting to execute a human intervention behavior, designing a human decision task, finally designing an adaptive behavior controller based on a fixed time sliding mode control method, and when the robot cannot autonomously control to complete the task, executing the human decision task to complete the work task in finite time, According to the method, the man-machine composite task can be completed within limited time, and man-machine task co-fusion is realized.

Description

technical field [0001] The invention relates to the technical field of human-computer interaction, in particular to a human-in-the-loop decision-making modeling and control method for robot systems. Background technique [0002] Behavior control, as one of the formation control technologies, can realize distributed control of multi-robot systems and has the advantages of flexible obstacle avoidance. However, traditional behavior control methods cannot guarantee the stability of formation control. Therefore, a behavior control method based on zero space is adopted. , the mathematical model of this method can achieve formation stability, but due to the lack of human participation, the behavior control method cannot eliminate the conflict between tasks in some cases, resulting in the failure to complete the task smoothly. [0003] Therefore, in order to better realize formation control and improve formation stability, it is necessary to introduce human-computer interaction. Cu...

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Problems solved by technology

There are currently some human model modeling methods, including the Markov model proposed by the literature (Pentland A, Liu A.Modeling and prediction of human behavior[J].Neural computation,1999,11(1):229-242.) , Extending the decision field theory to model operators' reliance on automation in supervisory control situations[J].IEEE Transactions on Systems,2006,36(5):943-959. Compared with these two methods, the literature (Ratcliff R, McKoon G. The diffusion decision model: theory and data for two-choice decision tasks[J]. Neural computation, 2008, 20 (4): 873-922.) The drift diffusion model proposed is based on the actual sensory information of people to model human decision-making, which is mainly used in the modeling of human decision-making in the field of neurology. Application in the context of robot interaction remains an open problem

In addition, there is a time requirement for the execution of man-machine collaborative tasks in practical applications. The traditional design of controllers for man-machine collaborative task execution cannot guarantee that man-machine composite tasks can be completed within a limited time. Therefore, it is necessary to design a human-machine collaborative controller It is still an open problem to realize the execution of man-machine composite tasks in a limited time (Zuo Z.Non-singular fixed-timeterminal sliding mode control of non-linear systems[J].IET control theory&applications,2014,9(4):545- 552.)

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