Representation and Generalization Method of Discrete Trajectory of Robot Based on Probabilistic Model

Abstract

The invention relates to a probabilistic model-based characterization and generalization method for discrete trajectory of a robot, including the teaching of discrete trajectory: splitting the trajectory into multiple segments, teaching each trajectory separately, and obtaining the discrete trajectory Characterized data sources, characterizing discrete trajectories: modeling robot trajectories based on multiple GMMs, extracting correlations between multi-segment trajectories, characterizing teaching trajectories, and generalizing trajectory output: performing multi-segment trajectories through GMR Splicing realizes the generalized output of the trajectory, making the output trajectory smooth. The teaching process of the present invention is simple and operable; smooth splicing of various trajectories can be performed based on time information; and multi-task constraint relations of multi-robot arms can be learned so that the multi-robot arms of the robot can cooperate to complete multi-tasks.

Description

technical field [0001] The invention relates to a method for characterizing and generalizing the discrete trajectory of a robot based on a probability model. Background technique [0002] Teaching and learning can allow robots to learn how humans perform dexterous manipulation tasks in unknown environments, and generate robot trajectories that meet the needs in new environments and task objectives. The trajectory generation strategy based on teaching and learning can fully extract the characteristics of the teaching trajectory and generate robot trajectories with certain generalization. [0003] Discrete trajectories are more common in human life, such as writing Chinese characters. However, in the teaching learning field, there are few studies on feature extraction of discrete trajectories. The current research on the representation and generalization of discrete robot trajectories mainly has the following deficiencies: [0004] (1) The teaching trajectory is complex and...

AI Technical Summary

Problems solved by technology

When the learning trajectory is relatively complex, this teaching strategy is obviously cumbersome, and the teaching process requires a lot of time and cost, and it also requires certain skills to continuously design the trajectory

[0005] (2), lack of corresponding trajectory splicing strategy

Different from continuous trajectories, discrete trajectories are composed of multiple trajectories, and there are certain space constraints and time constraints between each segment of trajectories. The current research on the representation and generalization of discrete trajectories lacks corresponding strategies to integrate discrete The splicing of multi-segment trajectories cannot satisfy the relevant constraints of the original trajectories while making the final output trajectories have better smoothness.

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