CN113673747B - Intelligent motion guiding method and system for epidemic prevention robot - Google Patents
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- 230000002265 prevention Effects 0.000 title claims abstract description 61
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Abstract
The application provides an intelligent motion guiding method and system of an epidemic prevention robot, and relates to the technical field of robot motion planning, wherein the method comprises the following steps: and a data acquisition step: collecting map data of an area to be disinfected, and dividing the whole area to be disinfected into a plurality of subareas according to the collected map data; personnel statistics: respectively counting the number of people in each subarea; a machine sterilization step: selecting the subareas needing to be sprayed with the disinfectant based on the number of people in each subarea. According to the application, different parts of the waiting area can be selectively disinfected according to the personnel flow and the personnel residence time, and the defect of personnel-intensive area manpower timing disinfection in real-time is overcome.
Description
Technical Field
The application relates to the technical field of robot motion planning, in particular to an intelligent motion guiding method and system of an epidemic prevention robot.
Background
Image processing and robotics have evolved rapidly in recent years. The high-definition camera can capture moving objects in the shooting range. Personnel are at risk of being infected themselves when conducting epidemic prevention work. The robot is used for replacing personnel to perform epidemic prevention work, so that the epidemic prevention work efficiency can be improved, and the risk of virus infection does not exist.
While railway and subway stations are areas of high personnel and mobile volume, disinfection of such areas is of particular importance. The current disinfection work is mainly born by epidemic prevention personnel, and is carried out according to a certain time interval, so that the artificial workload is large. Meanwhile, the time interval of the disinfection work cannot be too long, otherwise, the sprayed disinfection solution cannot play a role in killing viruses after the effective period, and the time interval cannot be too short, because the volatile chemical substances of the disinfection solution are harmful to human bodies.
Disclosure of Invention
Aiming at the defects in the prior art, the application aims to provide an intelligent motion guiding method and system of an epidemic prevention robot, which are used for overcoming the defect of manpower timing disinfection in a personnel-intensive area in real-time.
According to the intelligent motion guiding method and system of the epidemic prevention robot, the scheme is as follows:
in a first aspect, an intelligent motion guiding method of an epidemic prevention robot is provided, the method comprising:
and a data acquisition step: collecting map data of an area to be disinfected, and dividing the whole area to be disinfected into a plurality of subareas according to the collected map data;
personnel statistics: respectively counting the number of people in each subarea;
a machine sterilization step: selecting the subareas needing to be sprayed with the disinfectant based on the number of people in each subarea.
Preferably, the area to be disinfected is divided into n sub-areas C 1 ，C 2 ，…，C n ；
The average personnel number rho of each sub-area in a period of time T is calculated through an omnidirectional camera of the area to be disinfected and by applying a face recognition technology i (i＝1,2,…n)。
Preferably, from the average personnel number ρ i (i=1, 2, … n), an average person number threshold θ is selected 1 Screening out average personnel number rho i (i=1, 2, … n) is greater than the average person number threshold θ 1 M of (2) 1 A sub-region, numbered c j (j＝1,2,…,m 1 )。
Preferably, continuous Hopfield network generation is employedm 1 +1 subregions: c 0 ，c 1 ，c 2 ，…，c m1； wherein c0 A subregion representing an epidemic prevention machine filled with disinfectant and charged;
steady state solution representation m generated by continuous Hopfield network 1 +1 subregions c 0 ，c 1 ，c 2 ，…，c m1 Spraying disinfectant by the epidemic prevention robot;
epidemic prevention robot is on pair m 1 Sub-region c 1 ，c 2 ，…，c m1 Returning to epidemic prevention machine to fill disinfectant and charging subarea c after disinfectant spraying 0 Filling and charging disinfectant.
Preferably, according to the average personnel number ρ selected i A value of (i=1, 2, … n) greater than the average person count threshold θ 1 The number of sub-regions m 1 Construct having (m 1 +1)×(m 1 +1) a single layer continuous Hopfield network of neurons.
Preferably, the weight w of the continuous Hopfield network is determined αi,βj The sum bias is as follows:
w αi,βj ＝-Ad α,β (δ j,i+1 +δ j,i-1 )-Bδ i,j (1-δ α,β )-Cδ α,β (1-δ i,j )-D，
b i ＝-Dm 1 ，
wherein ,I，j＝0，1，2，…，m 1 ；d α,β represents the distance between sub-region alpha and sub-region beta, alpha,
preferably, the average personnel number ρ is selected i (i=1, 2, … n) is greater than the average person number threshold θ 1 M of (2) 1 After each sub-area, a new average personnel number threshold value theta is selected 2 (θ 2 ＜θ 1 ) Screening out new average personNumber of staff ρ i (i=1, 2, … n) is greater than θ 2 And eliminate the subareas that have been selected before, will be greater than theta 2 The number of sub-regions of (a) is denoted as m 2 The subregion number is c 1 ，c 2 ，…，c m2 The method comprises the steps of carrying out a first treatment on the surface of the In this way, for n sub-regions C in turn 1 ，C 2 ，…，C n Screening to obtain multiple groups of subareas, wherein the number of subareas in each group is m i (i＝1,2,…)。
Preferably, the epidemic prevention robot completes the first group m 1 After the disinfection work of the subareas and the disinfection solution filling and charging are carried out, judging whether the used time t is less than the limit time t c If the number is smaller than the set of m, continuing to the next set of m i The sterilization of the sub-areas until the total time exceeds t c 。
Preferably, the epidemic prevention robot is arranged in the n subareas C 1 ，C 2 ，…，C n While the disinfection work is carried out, a period of time T (T) is calculated by an omnidirectional camera with a disinfection area and using a face recognition technology>t c ) Average personnel number ρ for each zone within i (i＝1,2,…n)；
After a T time when the epidemic prevention robot starts working, a new average personnel number threshold value is set, a new subarea needing to be disinfected is screened, and the epidemic prevention robot starts another round of disinfection work.
In a second aspect, there is provided an intelligent motion guidance system for an epidemic prevention robot, the system comprising:
and a data acquisition module: collecting map data of an area to be disinfected, and dividing the whole area to be disinfected into a plurality of subareas according to the collected map data;
and a personnel statistics module: respectively counting the number of people in each subarea;
machine disinfection module: selecting the subareas needing to be sprayed with the disinfectant based on the number of people in each subarea.
Compared with the prior art, the application has the following beneficial effects:
1. the epidemic prevention robot selectively performs selective disinfection on different parts of the personnel-intensive area according to the personnel flow and the personnel residence time of the personnel-intensive area, and the disinfection is performed in real time, so that the defect of manpower timing disinfection in real-time is overcome;
2. meanwhile, the real-time disinfection work improves the sanitation and safety of the personnel-intensive area.
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Other features, objects and advantages of the present application will become more apparent upon reading of the detailed description of non-limiting embodiments, given with reference to the accompanying drawings in which:
FIG. 1 is a block diagram of a Hopfield neural network;
fig. 2 is a workflow diagram of an epidemic prevention robot provided by the application.
Detailed Description
The present application will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the present application, but are not intended to limit the application in any way. It should be noted that variations and modifications could be made by those skilled in the art without departing from the inventive concept. These are all within the scope of the present application.
The embodiment of the application provides an intelligent motion guiding method of an epidemic prevention robot, which is mainly divided into three parts as shown in the drawings with reference to figures 1 and 2: and counting the flow data of personnel, calculating and generating an epidemic prevention robot path, and sterilizing the epidemic prevention robot.
Statistics of personnel flow data:
dividing the whole area to be disinfected, such as a waiting area of a subway station or a railway station, with dense personnel into n sub-areas C with similar areas 1 ，C 2 ，…，C n . The average personnel number rho of each sub-area in a period of time T is calculated through an omnidirectional camera of the area to be disinfected and by using an image recognition technology i (i=1, 2, … n), the calculation method is as follows:
wherein ,Ni (i=1, 2, … n) is the number of people, T, present in the ith sub-area within time T i For the average time that all persons present in the subarea remain in the subarea, a and b are weighting factors for reflecting the extent to which the flow and the residence time of the persons affect the hygiene of the subarea.
Calculating and generating an epidemic prevention robot path:
the generation of the path is completed by a background computer, and the calculation result is sent to the epidemic prevention robot. For a group of subareas, selecting an average personnel number threshold value theta of one subarea 1 Screening out average personnel number rho i (i=1, 2, … n) is greater than the average person number threshold θ 1 M of (2) 1 Sub-areas, which are numbered together with the word areas of epidemic prevention machine filled with disinfectant and charged, are respectively c j (j＝0,1,…,m 1), wherein c0 Representing the subareas where the epidemic prevention machine is filled with disinfectant and charged.
After screening out the average personnel number ρ i (i=1, 2, … n) is greater than the average person number threshold θ 1 M of (2) 1 After each area, a new average personnel number threshold value theta is selected 2 (θ 2 ＜θ 1 ) Screening out new average personnel number rho i (i=1, 2, … n) is greater than the average person number threshold θ 2 And eliminate the subareas which have been selected before, the average personnel number threshold value theta is larger than 2 The number of sub-regions of (a) is denoted as m 2 Sub-region number ofIn this way for n sub-regions C 1 ,C 2 ,…,C n Screening for multiple times to obtain multiple groups of subareas, wherein the number of each group of subareas is m i (i＝1,2,…)。
Construction of a network with a continuous Hopfield network (m 1 +1)×(m 1 +1) a single layer continuous Hopfield network of neurons. Through network energy functions and constraints (epidemic prevention machinesThe person can only traverse each sub-area once and eventually return to the starting point) determines the weights w of the continuous Hopfield network αi,βj The sum bias is as follows:
w αi,βj ＝-Ad α,β (δ j,i+1 +δ j,i-1 )-Bδ i,j (1-δ α,β )-Cδ α,β (1-δ i,j )-D，
b i ＝-Dm 1
wherein ,i，j＝0，1，2，…，m 1 ；d α,β represents the distance between the region alpha and the region beta, alpha,
the randomly generated initial values are input into a continuous Hopfield network, and after the network parallel operation reaches a steady state (the state is not changed any more), m can be obtained through the state of the network 1 +1 subregionsQuilt is covered with
And spraying disinfectant by the epidemic prevention robot. Such as (m) 1 ＝3)
Epidemic prevention robot pairs m according to the sequence obtained by the method 1 The path taken for sterilization in +1 sub-areas is not necessarily the shortest, but is relatively the shortest.
The epidemic prevention robot performs disinfection work:
and the epidemic prevention robot starts disinfectant spraying work after receiving the path sent by the background computer. Epidemic prevention robot is on pair m 1 Individual areasAfter spraying the disinfectantReturning to the area c where the epidemic prevention machine is filled with disinfectant and charged 0 Filling and charging disinfectant. Judging whether the time t for completing disinfection work, filling disinfection solution and charging is less than the limit time t c If smaller, continuing to m for the next group i The sub-areas are sterilized, and so on until the total time exceeds t c If the limit is exceeded, the epidemic prevention robot enters a rest state.
Epidemic prevention robot is arranged in the n subareas C 1 ，C 2 ，…，C n While the disinfection work is carried out, a period of time T (T) is calculated by an omnidirectional camera of the area to be disinfected and a face recognition technology>t c ) Average personnel number ρ for each zone within i (i=1, 2, … n); after a time T from the start of the epidemic prevention robot, a new average personnel number threshold value theta is set i (i=1, 2, … n), starting to screen a new sub-area to be disinfected, in preparation for the epidemic prevention robot to start another round of disinfection work.
The embodiment of the application provides an intelligent motion guiding method of an epidemic prevention robot, which utilizes a continuous Hopfield network to generate an epidemic prevention robot region traversing scheme. After one round of traversal, the average personnel number threshold is reduced, a group of subareas with the value larger than the average personnel number threshold is selected, and the subareas which appear in the previous group are eliminated, so that a path scheme is generated. Repeating for multiple times to obtain multiple groups of subareas and traversing schemes. The epidemic prevention robot sprays disinfectant to the selected subareas according to each group of traversing schemes, the disinfectant spraying quantity of each group of subareas is determined by the average personnel number threshold value of the group, and the higher the average personnel number threshold value is, the more disinfectant is sprayed.
According to the personnel flow and personnel residence time of the personnel-intensive areas such as subway stations or train station waiting areas, the selective disinfection work is carried out on different parts of the personnel-intensive areas, the disinfection work is carried out in real time, the defect of manpower timing disinfection in real-time is overcome, and the sanitation safety of the personnel-intensive areas is also improved.
Those skilled in the art will appreciate that the application provides a system and its individual devices, modules, units, etc. that can be implemented entirely by logic programming of method steps, in addition to being implemented as pure computer readable program code, in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Therefore, the system and various devices, modules and units thereof provided by the application can be regarded as a hardware component, and the devices, modules and units for realizing various functions included in the system can also be regarded as structures in the hardware component; means, modules, and units for implementing the various functions may also be considered as either software modules for implementing the methods or structures within hardware components.
The foregoing describes specific embodiments of the present application. It is to be understood that the application is not limited to the particular embodiments described above, and that various changes or modifications may be made by those skilled in the art within the scope of the appended claims without affecting the spirit of the application. The embodiments of the application and the features of the embodiments may be combined with each other arbitrarily without conflict.
Claims (4)
1. An intelligent motion guiding method of an epidemic prevention robot is characterized by comprising the following steps:
and a data acquisition step: collecting map data of an area to be disinfected, and dividing the whole area to be disinfected into a plurality of subareas according to the collected map data;
personnel statistics: respectively counting the number of people in each subarea;
a machine sterilization step: selecting a subarea needing to be sprayed with disinfectant based on the number of people in each subarea;
dividing the area to be disinfected into n sub-areas C 1 ，C 2 ，…，C n ；
The average personnel number rho of each sub-area in a period of time T is calculated through an omnidirectional camera of the area to be disinfected and by applying a face recognition technology i (i＝1,2,…n)；
From the average personnel number ρ i (i=1, 2, … n), an average person number threshold θ is selected 1 Screening out average personnel number rho i (i=1, 2, … n) is greater than the average person number threshold θ 1 M of (2) 1 A sub-region, numbered c j (j＝1,2,…,m 1 )；
Generating m using continuous Hopfield network 1 +1 subregions: c 0 ，c 1 ，c 2 ，…， wherein c0 A subregion representing an epidemic prevention machine filled with disinfectant and charged;
steady state solution representation m generated by continuous Hopfield network 1 +1 subregions c 0 ，c 1 ，c 2 ，…，Spraying disinfectant by the epidemic prevention robot;
epidemic prevention robot is on pair m 1 Sub-region c 1 ，c 2 ，…，Returning to epidemic prevention machine to fill disinfectant and charging subarea c after disinfectant spraying 0 Filling and charging disinfectant;
after screening out the average personnel number ρ i (i=1, 2, … n) is greater than the average person number threshold θ 1 M of (2) 1 After each sub-area, a new average personnel number threshold value theta is selected 2 (θ 2 ＜θ 1 ) Screening out new average personnel number rho i (i=1, 2, … n) is greater than θ 2 And eliminate the subareas that have been selected before, will be greater than theta 2 The number of sub-regions of (a) is denoted as m 2 The subregion number is c 1 ，c 2 ，…，According to this method, the following steps are sequentially performedn sub-regions C 1 ，C 2 ，…，C n Screening to obtain multiple groups of subareas, wherein the number of subareas in each group is m i (i＝1,2,…)；
The epidemic prevention robot is completing the first group m 1 After the disinfection work of the subareas and the disinfection solution filling and charging are carried out, judging whether the used time t is less than the limit time t c If the number is smaller than the set of m, continuing to the next set of m i The sterilization of the sub-areas until the total time exceeds t c ；
Epidemic prevention robot is arranged in the n subareas C 1 ，C 2 ，…，C n While the disinfection work is carried out, a period of time T (T) is calculated by an omnidirectional camera with a disinfection area and using a face recognition technology>t c ) Average personnel number ρ for each zone within i (i＝1,2,…n)；
After a T time when the epidemic prevention robot starts working, a new average personnel number threshold value is set, a new subarea needing to be disinfected is screened, and the epidemic prevention robot starts another round of disinfection work.
2. The method according to claim 1, wherein the average personnel number ρ is selected based on i A value of (i=1, 2, … n) greater than the average person count threshold θ 1 The number of sub-regions m 1 Construct having (m 1 +1)×(m 1 +1) a single layer continuous Hopfield network of neurons.
3. The method according to claim 1, characterized in that the weights w of the continuous Hopfield network are determined αi,βj The sum bias is as follows:
w αi,βj ＝-Ad α,β (δ j,i+1 +δ j,i-1 )-Bδ i,j (1-δ α,β )-Cδ α,β (1-δ i,j )-D，
b i ＝-Dm 1 ，
wherein ,i，j＝0，1，2，…，m 1 ；d α,β representing the distance between sub-region α and sub-region β, α, β=c 0 ，c 1 ，c 2 ，…，/>
4. An intelligent motion guidance system for an epidemic prevention robot, the system comprising:
and a data acquisition module: collecting map data of an area to be disinfected, and dividing the whole area to be disinfected into a plurality of subareas according to the collected map data;
and a personnel statistics module: respectively counting the number of people in each subarea;
machine disinfection module: selecting a subarea needing to be sprayed with disinfectant based on the number of people in each subarea;
dividing the area to be disinfected into n sub-areas C 1 ，C 2 ，…，C n ；
The average personnel number rho of each sub-area in a period of time T is calculated through an omnidirectional camera of the area to be disinfected and by applying a face recognition technology i (i＝1,2,…n)；
From the average personnel number ρ i (i=1, 2, … n), an average person number threshold θ is selected 1 Screening out average personnel number rho i (i=1, 2, … n) is greater than the average person number threshold θ 1 M of (2) 1 A sub-region, numbered c j (j＝1,2,…,m 1 )；
Generating m using continuous Hopfield network 1 +1 subregions: c 0 ，c 1 ，c 2 ，…， wherein c0 A subregion representing an epidemic prevention machine filled with disinfectant and charged;
steady state solution representation m generated by continuous Hopfield network 1 +1 subregions c 0 ，c 1 ，c 2 ，…，Spraying disinfectant by the epidemic prevention robot;
epidemic prevention robot is on pair m 1 Sub-region c 1 ，c 2 ，…，Returning to epidemic prevention machine to fill disinfectant and charging subarea c after disinfectant spraying 0 Filling and charging disinfectant;
after screening out the average personnel number ρ i (i=1, 2, … n) is greater than the average person number threshold θ 1 M of (2) 1 After each sub-area, a new average personnel number threshold value theta is selected 2 (θ 2 ＜θ 1 ) Screening out new average personnel number rho i (i=1, 2, … n) is greater than θ 2 And eliminate the subareas that have been selected before, will be greater than theta 2 The number of sub-regions of (a) is denoted as m 2 The subregion number is c 1 ，c 2 ，…，In this way, for n sub-regions C in turn 1 ，C 2 ，…，C n Screening to obtain multiple groups of subareas, wherein the number of subareas in each group is m i (i＝1,2,…)；
The epidemic prevention robot is completing the first group m 1 After the disinfection work of the subareas and the disinfection solution filling and charging are carried out, judging whether the used time t is less than the limit time t c If the number is smaller than the set of m, continuing to the next set of m i The sterilization of the sub-areas until the total time exceeds t c ；
Epidemic prevention robot is arranged in the n subareas C 1 ，C 2 ，…，C n While the disinfection work is carried out, a period of time T (T) is calculated by an omnidirectional camera with a disinfection area and using a face recognition technology>t c ) Average personnel number ρ for each zone within i (i＝1,2,…n)；
After a T time when the epidemic prevention robot starts working, a new average personnel number threshold value is set, a new subarea needing to be disinfected is screened, and the epidemic prevention robot starts another round of disinfection work.
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