CN111368762A - Robot gesture recognition method based on improved K-means clustering algorithm - Google Patents
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Abstract
A robot gesture recognition method based on an improved K-means clustering algorithm. Step 1, collecting data of hand movement by using a glove embedded with a micro-nano optical fiber sensor, wherein the dimension of the data collected by the sensor is 6 dimensions; step 2, uploading data acquired by the micro-nano optical fiber sensor to a robot through a WIFI module on the glove; step 3, the robot determines clustering centers corresponding to different gestures in advance by combining an improved K-means clustering algorithm; step 4, calculating Euclidean distances from data acquired by the current micro-nano sensor to predetermined clustering centers of different gestures; step 5, comparing each calculated Euclidean distance with a threshold value of a corresponding category, if the Euclidean distance is lower than the threshold value of the category, judging the Euclidean distance as the category, and if not, retraining the model; and 6, completing corresponding actions by the robot according to the judgment result, and finishing a complete closed loop. The invention effectively realizes the accurate recognition of the robot to various gestures through the improved K-means clustering algorithm.
Description
Technical Field
The invention relates to the field of robot gesture recognition, in particular to a robot gesture recognition method based on an improved K-means clustering algorithm.
Background
With the continuous development of artificial intelligence and virtual reality technology, a human-computer interaction system has become a current research hotspot. Nowadays, as an emerging human-computer interaction mode, gesture recognition is valued by many researchers, and produces a series of effective results, and is widely applied to devices such as intelligent robots, intelligent driving and the like. Gesture recognition simply means that a machine understands ideas which people want to express with the assistance of a vision or sensor acquisition system, namely, an interaction process is completed in a non-contact mode, so that corresponding actions are completed through the robot, and intellectualization is realized in a true sense.
Aiming at the problem of gesture recognition of a robot, a domestic patent related to a solution of the problem is 'a cooperative robot gesture recognition method and device based on depth vision' (201910176271.5), a gesture template set is obtained in advance, and a plurality of depth maps of a gesture to be recognized are obtained at the same time; and aiming at each gesture template, obtaining the distance between the gesture to be recognized and the gesture template, taking the gesture template with the minimum distance between the gesture to be recognized and the gesture template as the recognition result of the gesture to be recognized, and further controlling the cooperative robot according to the control parameters corresponding to the recognition result. The invention discloses a gesture recognition method based on an intelligent robot (201910118356.8), which is characterized in that a camera carried by the robot is called to obtain a gesture image, and a gesture template is established; segmenting the gesture by detecting based on skin color and based on maximum between-class variance; denoising the segmented gesture image by using a median filtering algorithm, and extracting a gesture edge contour; and then, obtaining a recognition result by adopting a Euclidean distance template matching method based on the gesture template and the gesture edge outline. The above two invention patents identify the gesture picture, and the picture data with overlarge dimensionality increases the training difficulty of the model on one hand, and increases the time for judging the model in actual application on the other hand.
Disclosure of Invention
In order to solve the problems, the invention provides a robot gesture recognition method based on an improved K-means clustering algorithm on the basis of a micro-nano optical fiber sensor and the K-means clustering algorithm. Firstly, collecting corresponding data without gestures by using a micro-nano optical fiber sensor; then sequentially determining clustering centers and category thresholds corresponding to different gestures by utilizing an improved K-means clustering algorithm; meanwhile, the model supports online updating optimization, and the generalization of the model is greatly improved; and finally, the method is successfully applied to practice, and the robot can accurately recognize different gestures. To achieve the purpose, the invention provides a robot gesture recognition method based on an improved K-means clustering algorithm, which comprises the following specific steps:
step 1, collecting data of hand movement by using a glove embedded with a micro-nano optical fiber sensor, wherein the dimension of the data collected by the sensor is 6 dimensions;
step 2, uploading data acquired by the micro-nano optical fiber sensor to a robot through a WIFI module on the glove;
step 3, the robot determines clustering centers corresponding to different gestures in advance by combining an improved K-means clustering algorithm;
step 4, calculating Euclidean distances from data acquired by the current micro-nano sensor to predetermined clustering centers of different gestures;
step 5, comparing each calculated Euclidean distance with a threshold value of a corresponding category, if the Euclidean distance is lower than the threshold value of the category, judging the Euclidean distance as the category, and if not, retraining the model;
and 6, completing corresponding actions by the robot according to the judgment result, and finishing a complete closed loop.
Further, the specific step of using the improved K-means clustering algorithm to predetermine the clustering centers corresponding to different gestures in step 3 is as follows:
step 3.1, arbitrarily selecting one sample point from all sample points as the initial clustering center c of the first category1；
Step 3.2, X ═ X for the entire training sample setjI j 1,2,.., n, each sample is calculatedx to the clustering center, and taking the position of the sample corresponding to the maximum distance as a new clustering center;
step 3.3, repeating step 3.2 until k clustering centers c are determinedi(1≤i≤k)；
Step 3.4, for the whole training sample set X ═ XjI j 1,2,.. n, and calculating each sample point x respectivelyjGo to step 3.3 to determine k cluster centers ci(1 ≦ i ≦ k), sample x for the s-dimensional samplejTo class i centre ciThe Euclidean distance of (A) is:
step 3.5, classifying the samples into the class where the nearest Euclidean distance is located, and traversing the whole sample space to complete the construction of k class clusters;
step 3.6, for each class cluster, taking the mean vector of all sample points in the cluster as a new class cluster center, namely the update criterion of the class cluster is as follows:
in the formula, ciTo center the updated cluster, miIndicates the total number of samples in the ith class cluster,
representing the sum of the dimensions of all sample vectors in the class cluster.
Step 3.7, repeating the steps 3.4-3.6 until the square error function converges or the iteration number reaches the set number, wherein the expression of the square error function is as follows:
further, if the euclidean distance from the data acquired in real time to each cluster center in step 5 is greater than any category threshold, the specific description of the retraining model is as follows:
labeling a sample acquired in real time through prior knowledge, and then bringing the data into a trained model to update and correct the model: and updating the clustering centers of all the categories and the corresponding category threshold values. The model supports updating optimization, and the generalization of the model is greatly improved.
The robot gesture recognition method based on the improved K-means clustering algorithm has the beneficial effects that: the invention has the technical effects that:
1. according to the invention, the six-dimensional micro-nano optical fiber sensor is used for acquiring data under the current gesture in real time, and compared with the traditional data acquisition method for gesture recognition in a picture form, the method has lower sample dimension, shortens the training time of the model, improves the distinguishing speed of the model, and simultaneously ensures high precision;
2. the improved K-means clustering algorithm is used for clustering analysis of different gestures, so that the method is better improved in the aspect of avoiding the occurrence of wrong initial clustering centers compared with the traditional K-means clustering method, and can accurately realize classification of different gestures and determination of category thresholds;
3. the robot gesture recognition model of the invention supports optimization updating, namely: when the trained model cannot classify and judge the data collected in real time, the data at the moment is taken as training data and substituted into the model for retraining, so that the updating of the class center and the class threshold corresponding to each gesture class is realized, and the generalization of the model is greatly improved.
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FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic diagram of the recognition of clustering centers and classification thresholds of different gestures using an improved K-means clustering algorithm in the present invention;
Detailed Description
The invention is described in further detail below with reference to the following detailed description and accompanying drawings:
the invention provides a robot gesture recognition method based on an improved K-means clustering algorithm, and aims to simply and efficiently realize accurate recognition of different gestures by a robot.
FIG. 1 is a flow chart of the present invention. The steps of the present invention will be described in detail with reference to the flow chart.
Step 1, collecting data of hand movement by using a glove embedded with a micro-nano optical fiber sensor, wherein the dimension of the data collected by the sensor is 6 dimensions;
step 2, uploading data acquired by the micro-nano optical fiber sensor to a robot through a WIFI module on the glove;
step 3, the robot determines clustering centers corresponding to different gestures in advance by combining an improved K-means clustering algorithm;
step 3.1, arbitrarily selecting one sample point from all sample points as the initial clustering center c of the first category1；
Step 3.2, X ═ X for the entire training sample setjCalculating the distance from each sample x to a clustering center, and taking the position of the sample corresponding to the maximum distance as a new clustering center;
step 3.3, repeating step 3.2 until k clustering centers c are determinedi(1≤i≤k)；
Step 3.4, for the whole training sample set X ═ XjI j 1,2,.. n, and calculating each sample point x respectivelyjGo to step 3.3 to determine k cluster centers ci(1 ≦ i ≦ k), sample x for the s-dimensional samplejTo class i centre ciThe Euclidean distance of (A) is:
step 3.5, classifying the samples into the class where the nearest Euclidean distance is located, and traversing the whole sample space to complete the construction of k class clusters;
step 3.6, for each class cluster, taking the mean vector of all sample points in the cluster as a new class cluster center, namely the update criterion of the class cluster is as follows:
in the formula, ciTo center the updated cluster, miIndicates the total number of samples in the ith class cluster,
representing the sum of the dimensions of all sample vectors in the class cluster.
Step 3.7, repeating the steps 3.4-3.6 until the square error function converges or the iteration number reaches the set number, wherein the expression of the square error function is as follows:
step 4, calculating Euclidean distances from data acquired by the current micro-nano sensor to predetermined clustering centers of different gestures;
and 5, comparing each calculated Euclidean distance with a threshold value of a corresponding category, judging the Euclidean distance as the category if the Euclidean distance is lower than the threshold value of the category, and retraining the model if not. The specific description of the retraining model is as follows: labeling a sample acquired in real time through prior knowledge, and then bringing the data into a trained model to update and correct the model: and updating the clustering centers of all the categories and the corresponding category threshold values. The model supports updating optimization, and the generalization of the model is greatly improved.
And 6, completing corresponding actions by the robot according to the judgment result, and finishing a complete closed loop.
FIG. 2 is a schematic diagram of the present invention for identifying cluster centers and class thresholds of different classes by using an improved K-means clustering algorithm. As can be seen from the figure, the improved K-means clustering algorithm can be used for simply and effectively determining the clustering centers and the boundaries of different classes so as to obtain the corresponding class thresholds.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, but any modifications or equivalent variations made according to the technical spirit of the present invention are within the scope of the present invention as claimed.
Claims (3)
1. The robot gesture recognition method based on the improved K-means clustering algorithm comprises the following specific steps:
step 1, collecting data of hand movement by using a glove embedded with a micro-nano optical fiber sensor, wherein the dimension of the data collected by the sensor is 6 dimensions;
step 2, uploading data acquired by the micro-nano optical fiber sensor to a robot through a WIFI module on the glove;
step 3, the robot determines clustering centers corresponding to different gestures in advance by combining an improved K-means clustering algorithm;
step 4, calculating Euclidean distances from data acquired by the current micro-nano sensor to predetermined clustering centers of different gestures;
step 5, comparing each calculated Euclidean distance with a threshold value of a corresponding category, if the Euclidean distance is lower than the threshold value of the category, judging the Euclidean distance as the category, and if not, retraining the model;
and 6, completing corresponding actions by the robot according to the judgment result, and finishing a complete closed loop.
2. The robot gesture recognition method based on the improved K-means clustering algorithm of claim 1, characterized in that: the specific steps of utilizing the improved K-means clustering algorithm to predetermine the clustering centers corresponding to different gestures in the step 3 are as follows:
step 3.1, arbitrarily selecting one sample point from all sample points as the initial clustering center c of the first category1；
Step 3.2, X ═ X for the entire training sample setjCalculating the distance from each sample x to a clustering center, and taking the position of the sample corresponding to the maximum distance as a new clustering center;
step 3.3, repeating step 3.2 until k clustering centers c are determinedi(1≤i≤k)；
Step 3.4, for the whole training sample set X ═ XjI j 1,2,.. n, and calculating each sample point x respectivelyjGo to step 3.3 to determine k cluster centers ci(1 ≦ i ≦ k), sample x for the s-dimensional samplejTo class i centre ciThe Euclidean distance of (A) is:
step 3.5, classifying the samples into the class where the nearest Euclidean distance is located, and traversing the whole sample space to complete the construction of k class clusters;
step 3.6, for each class cluster, taking the mean vector of all sample points in the cluster as a new class cluster center, namely the update criterion of the class cluster is as follows:
in the formula, ciTo center the updated cluster, miIndicates the total number of samples in the ith class cluster,
representing the sum of the dimensions of all sample vectors in the class cluster.
Step 3.7, repeating the steps 3.4-3.6 until the square error function converges or the iteration number reaches the set number, wherein the expression of the square error function is as follows:
3. the robot gesture recognition method based on the improved K-means clustering algorithm of claim 1, characterized in that: in step 5, if the euclidean distance from the data acquired in real time to each cluster center is greater than any category threshold, the specific description of the retraining model is as follows:
labeling a sample acquired in real time through prior knowledge, and then bringing the data into a trained model to update and correct the model: and updating the clustering centers of all the categories and the corresponding category threshold values. The model supports updating optimization, and the generalization of the model is greatly improved.
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