Rendezvous iterative learning control method of under-actuated multi-mobile robot

Abstract

The invention provides a rendezvous iterative learning method of an under-actuated multi-mobile robot. The rendezvous iterative learning method of the under-actuated multi-mobile robot comprises the following steps that the linear velocity and angular velocity of the individual mobile robot at the current moment, the position and azimuth angle of the individual mobile robot at the next moment, andthe position and azimuth angle of a neighbor robot at the next moment in the current iteration are acquired; identification parameters are determined according to a judgment result of whether the individual mobile robot has predetermined desired position information or not; a weighted adjacency matrix and a gain matrix of the under-actuated multi-mobile robot are acquired; the individual mobile robot is controlled according to the position and azimuth angle of the individual mobile robot at the next moment, the linear velocity and angular velocity of the individual mobile robot at the currentmoment, the position and azimuth angle of the neighbor robot at the next moment, the expected trajectory position, the azimuth angle, the weighted adjacency matrix, an identification parameter matrixand the gain matrix. It is guaranteed that the under-actuated multi-mobile robot can complete the tracking task of separate expected trajectories after a certain number of iterations, the rendezvousand docking problem is solved, and higher control precision is achieved.

Description

technical field [0001] The invention relates to the technical field of robot control, in particular to a rendezvous iterative learning control method for an underactuated multi-mobile robot. Background technique [0002] At present, with the rapid development of robot technology and the continuous expansion of application fields, the coordinated control method of multiple mobile robots has been more and more widely used in practical problems. However, it is often difficult for a single mobile robot to achieve the desired results in performing complex tasks, information acquisition and processing, and multiple mobile robots are not just a simple combination of multiple individuals. They coordinate and interact with each other to complete practical applications. Complex problems, improve system performance and work efficiency, and achieve effects that cannot be achieved by a single robot. However, there is a class of problems in practical applications: the controlled multi-mo...

AI Technical Summary

Problems solved by technology

However, there is a class of problems in practical applications: the controlled multi-mobile robot system is required to achieve high-precision, error-free and complete tracking of the respective desired trajectories from beginning to end.

For such requirements, traditional control methods often have problems such as low control accuracy, slow convergence speed, and even difficulty in tracking from beginning to end. In order to achieve this goal, multiple learning and corrections are often required, and multiple executions can achieve the best control. Effect

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