Field hexapod robot control training method based on double operators

Abstract

The invention belongs to the technical field of teleoperation of foot-type robots. The invention discloses a field hexapod robot control training method based on double operators. The problem of control weight distribution of a trainer to a trainee in hexapod robot control training is solved. The invention discloses a field hexapod robot control training method based on double operators, is applied to a double-master single-slave structure system, and provides a system transparency analysis and application method, a sharing factor calculation method and the like. The system transparency analysis and application method comprises the following steps: calculating transparency deviation ratios of a master end and a slave end, analyzing system transparency, and calculating a sharing factor value range based on the transparency deviation ratios. According to the sharing factor calculation method, a new calculation method is used, and the value range of the sharing factor is combined to calculate the sharing factor. The control training efficiency of the hexapod robot can be effectively improved, and the safety of the system can be improved while the transparency is ensured.

Description

technical field [0001] The invention belongs to the technical field of remote operation of legged robots, and in particular relates to a shared control method capable of safely and efficiently realizing control training of a hexapod robot in the field. Background technique [0002] Shared control is a semi-autonomous teleoperation control strategy between two-way control and supervisory control. According to the complementarity between direct control and supervisory control, the method of maximizing strengths and avoiding weaknesses is adopted, which allows the operator to directly exert his judgment and decision-making ability , and ensure that the robot has a certain local autonomy. It is generally believed that intelligent control systems are good at the execution of fine tasks, while humans are good at upper-level planning. At the same time, humans can monitor the operating status of the robot system in real time and correct some errors. This control method can greatly ...

AI Technical Summary

Problems solved by technology

But what needs to be recognized is that although legged robots can replace humans in dangerous working environments, due to the unpredictable complexity of the work site, it is difficult to ensure that the robot can adapt to the complex and changeable by relying solely on the robot's own intelligent control algorithm. Task requirements under working conditions

And it is not difficult to find that the current motion control of legged robots is mainly based on manual manipulation, and it is necessary to train drivers who lack control experience in the control of legged robots.

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