Voice robot control method based on particle filtering algorithm

Abstract

The invention relates to a voice robot control method based on a particle filter algorithm, and belongs to the field of computers. The method comprises the following steps: step 1, speech recognition:designing a speech recognition algorithm based on a deep convolutional neural network algorithm, and obtaining a final recognition result through training and testing; 2, performing target detectionand identification, and designing a target detection algorithm based on a fast convolutional neural network; 3, tracking the target object through an improved particle filter algorithm; and 4, obtaining a mechanical arm control algorithm based on deep reinforcement learning. A traditional particle filtering algorithm is improved; a novel particle filtering algorithm of grouping optimization weightand variation is provided, the number of sampling particles is reduced, the problem of particle degradation is delayed, the loss of sample diversity and effectiveness caused in the particle resampling stage is reduced, the resampling process is optimized, the problem of particle shortage is solved, and the detection precision is higher.

Description

technical field [0001] The invention belongs to the field of computers and relates to a voice robot control method based on a particle filter algorithm. Background technique [0002] At present, most service robots are inseparable from human control and lack intelligence. And in the traditional target detection method, due to the complex external environment (illumination, occlusion, etc.), the camera will shake and other factors, only relying on the detection result, the detection frame is very unstable, and it is easy to cause wrong detection and omission. detection. In recent years, Montemerlo, Doucet and other scholars put forward the particle filter algorithm based on particle filter has been widely used in various target tracking fields, and achieved good results. Compared with the Kalman filter, the particle filter has the advantage that it is not constrained by nonlinear non-Gaussian problems and system noise problems, and can be widely applied to any form of syste...

AI Technical Summary

Problems solved by technology

Solve the problem that the service robot needs to be operated remotely or the sensor cannot be tracked and detected, and the problem that it cannot learn and control autonomously

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