CN106017473A - Indoor socializing navigation system - Google Patents

Indoor socializing navigation system Download PDF

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CN106017473A
CN106017473A CN201610338026.6A CN201610338026A CN106017473A CN 106017473 A CN106017473 A CN 106017473A CN 201610338026 A CN201610338026 A CN 201610338026A CN 106017473 A CN106017473 A CN 106017473A
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indoor
terminal
node
location
model
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CN106017473B (en
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尚建嘎
周智勇
余芳文
汤欣怡
武永峰
程稳
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China University of Geosciences
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations
    • G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation

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Abstract

The invention provides an indoor socializing navigation system. The system comprises a positioning sensor and a location server, wherein the positioning server is used for receiving first positioning sensor data transmitted by a terminal and second positioning sensor data transmitted by a target friend terminal, calculating the first geometrical coordinates of the terminal according to the first positioning sensor data and the second geometrical coordinates of the target terminal according to the second positioning sensor data, and storing the first geometrical coordinates and the second geometrical coordinates into a location database; the location server is used for acquiring indoor space model data, building an indoor space topological network, receiving a navigation request transmitted by the terminal, acquiring the first geometrical coordinates and the second geometrical coordinates from the positioning server according to the navigation request, and using a path searching algorithm to calculate the optimal navigation path in the indoor space topological network.

Description

Indoor social navigation system
Technical Field
The invention belongs to the technical field of indoor location services, and particularly relates to an indoor social navigation system.
Background
With the development of mobile internet and location-aware technology, location-based mobile social network services bring much convenience to people's lives. The position attribute extends the mobile social network to reality, reduces the difference between an online virtual world and an offline real world, improves the service effect of the social network, and enables people to check in the position (check-in), share multimedia content (geo-tagging content) with position tags and the like; meanwhile, the user can also expand the social relationship based on the spatial association of the positions.
At present, the Position information in the mobile social network based on the Position is mainly obtained through positioning modes such as a Global Positioning System (GPS), a mobile base station, Wi-Fi, and the like, the corresponding service precision is in the range of 10 meters to 100 meters, and the service precision of the mobile social network based on the Position is mostly in the building level. People also have a great demand for indoor location services, such as indoor navigation between friends, since on average everyone is indoors for up to 90% of the time each day. However, in the prior art, the position information and the services related to the position in the mobile social network are not refined to the functional space of multiple indoor floors, and most of the mobile social network services based on the position belong to user-requested static services, so that the real-time performance of active updating is lacked, and the navigation path cannot be accurately planned in real time during indoor social navigation.
Based on this, there is a need for an indoor social navigation system with high precision and strong real-time performance.
Disclosure of Invention
Aiming at the problems and the application defects in the prior art, the embodiment of the invention provides an indoor social contact navigation system, which is used for solving the technical problem that an indoor social contact navigation friend-searching function in the prior art cannot plan a navigation path in real time, dynamically and accurately.
The invention provides an indoor social navigation system, which comprises:
the positioning server is used for receiving first positioning sensor data sent by a terminal and second positioning sensor data sent by a target friend terminal, calculating a first geometric coordinate of the terminal according to the first positioning sensor data, and calculating a second geometric coordinate of the target terminal according to the second positioning sensor data; storing the first geometric coordinate and the second geometric coordinate in a position database;
the position server is used for acquiring indoor position model data and constructing a spatial topological network diagram of each layer of the indoor position model according to the indoor space position data;
and receiving a navigation request sent by the terminal, acquiring the first geometric coordinate and the second geometric coordinate from the positioning server according to the navigation request, and calculating an optimal navigation path in the space topology network map by using a path search algorithm.
In the above solution, the spatial topology network diagram of each level of the indoor location model includes: fine grain level AEGVG diagram, exit level model diagram and position level model diagram.
In the foregoing solution, the step of the location server constructing the fine-grained hierarchical AEGVG graph of the indoor location model according to the indoor spatial location data specifically includes:
extracting a one-dimensional framework according to the indoor floor plan to form a one-dimensional Voronoi diagram of an indoor space long and narrow region;
carrying out grid division on the open area according to a preset side length to form a grid map, and adding the grid map into the Voronoi diagram;
sampling nodes by taking the average step length of the pedestrians as sampling intervals, and generating the AEGVG graph.
In the foregoing solution, the step of the location server constructing an exit hierarchical model diagram of the indoor location model according to the indoor spatial location data specifically includes:
determining an exit node of the coarse-grained layer according to an exit position in the fine-grained layer AEGVG graph;
and constructing the outlet hierarchical model graph by taking the reachable paths between the adjacent positions as edges.
In the foregoing solution, the step of the location server building a location hierarchical model diagram of an indoor location model according to the indoor spatial location data specifically includes:
determining a position node of the coarse-grained layer according to the symbolic position in the fine-grained layer AEGVG graph;
and generating the position hierarchical model graph according to the adjacency and communication relation between the position nodes.
In the foregoing solution, the positioning server is configured to calculate a first geometric coordinate of the terminal according to the first positioning sensor data, and specifically includes:
when the positioning server detects an anchor point signal in the first positioning sensor data, performing fingerprint matching on the anchor point signal and a position fingerprint database to determine an initial position of the terminal;
and detecting the anchor point signal at regular time according to a preset period, and if the anchor point signal is detected, fusing a pedestrian dead reckoning PDR method, the anchor point signal and indoor space information by utilizing a particle filter fusion positioning algorithm to determine a first geometric coordinate of the terminal.
In the above scheme, after the location server constructs the spatial topology network map of each level of the indoor location model, the location server is further specifically configured to:
receiving an influence factor of each navigation path in the space topological network graph;
receiving the influence weight of each influence factor on the current navigation path;
and calculating the comprehensive weight of each path according to the influence weight.
In the above scheme, the influencing factor specifically includes: indoor pedestrian reachable distance, reachable time, personnel density and road width.
In the above scheme, the pedestrian reachable distance in the room is represented by the formulaCalculating to obtain; it is composed ofIn (1), the OiA first mobile object corresponding to the terminal; said O isjA second moving object corresponding to the target friend terminal; said (x)x,yk) For the distance in the fine-grained layer to the first moving object OiNearest node nkThe coordinates of (a); and m is an integer.
In the above solution, the evaluation function of the path search algorithm is: (n) ═ g (n) + h (n); wherein, f (n) is a valuation function from the initial node to the target node through the node n; the g (n) is the actual cost of the initial node to node n in the state space; the h (n) is the actual cost of the optimal navigation path from the node n to the target node.
The invention provides an indoor social navigation system, which comprises: the positioning server is used for receiving first positioning sensor data sent by a terminal and second positioning sensor data sent by a target friend terminal, calculating a first geometric coordinate of the terminal according to the first positioning sensor data, and calculating a second geometric coordinate of the target terminal according to the second positioning sensor data; storing the first geometric coordinate and the second geometric coordinate in a position database; the position server is used for acquiring indoor space model data and constructing an indoor space topological network; receiving a navigation request sent by the terminal, and acquiring the first geometric coordinate and the second geometric coordinate from the positioning server according to the navigation request; calculating an optimal navigation path in the indoor space topological network by using a path search algorithm; thus, the position server can obtain the geometric coordinates of both navigation parties from the positioning server; constructing a topological network of each level of an indoor spatial position model, mapping a geometric coordinate to a fine-grained layer of the spatial position model, determining an influence factor of each navigation path in the topological network and an influence weight of each influence factor on the navigation path, calculating a comprehensive weight of each path according to the influence weights, and determining an optimal navigation path according to the comprehensive weight; therefore, the navigation path can be planned dynamically and accurately in real time.
Drawings
Fig. 1 is a schematic overall structure diagram of an indoor navigation system according to an embodiment of the present invention;
fig. 2 is a structural diagram of an indoor spatial position model HiSeLoMo frame according to an embodiment of the present invention;
FIG. 3 is a one-dimensional skeleton diagram of an indoor plan view according to an embodiment of the present invention;
fig. 4 is an AEGVG diagram of the fine-grained layer of HiSeLoMo according to the first embodiment of the present invention;
fig. 5 is a schematic diagram of a location-level location model in a coarse-grained layer according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of an exit level position model in a coarse-grained layer according to an embodiment of the present invention;
fig. 7 is a schematic diagram of a dynamic topological relation of a moving object according to an embodiment of the present invention;
fig. 8 is a schematic view of an indoor spatial position model HiSeLoMo interlayer relationship in an embodiment of the present invention
Fig. 9 is a schematic diagram of an attribute association relationship between layers of the indoor spatial location model HiSeLoMo according to an embodiment of the present invention.
Detailed Description
In order to plan the navigation path between friends in an indoor environment in real time, dynamically and accurately, the invention provides an indoor social navigation system, which comprises: the positioning server is used for receiving first positioning sensor data sent by a terminal and second positioning sensor data sent by a target friend terminal, calculating a first geometric coordinate of the terminal according to the first positioning sensor data, and calculating a second geometric coordinate of the target terminal according to the second positioning sensor data; storing the first geometric coordinate and the second geometric coordinate in a position database; the position server is used for acquiring indoor space model data and constructing an indoor space topological network; acquiring the first geometric coordinate and the second geometric coordinate from the positioning server according to the navigation request; and calculating an optimal navigation path in the indoor space topological network by utilizing a path search algorithm.
The technical solution of the present invention is further described in detail by the accompanying drawings and the specific embodiments.
Example one
The present embodiment provides an indoor social navigation system, as shown in fig. 1, the system includes: the system comprises a terminal 1, a target friend terminal 2, a positioning server 3, a position server 4 and a social application server 5; wherein,
before the terminal 1 wants to perform a friend real-time navigation function, the location server 4 is configured to calculate a distance between the terminal 1 and each friend terminal; the social application server 5 is further configured to display the friend terminals (in the form of a list) on the interface of the terminal 1 according to the distance.
Here, for example, the terminal 1 selects to send a navigation request to the target friend terminal 2, and after the navigation request is granted, the terminal 1 forwards the navigation request to the location server 4.
After the navigation request receives permission of the target friend terminal 2, the positioning server 3 is configured to receive first positioning sensor data sent by the terminal 1 and second positioning sensor data sent by the target friend terminal 2 in a first preset period, so as to calculate first real-time geometric coordinates and second real-time geometric coordinates of the terminal 1 and the target friend terminal 2. Wherein the first preset period is 1 HZ.
Specifically, when the positioning server 3 determines the first geometric coordinate of the terminal 1 according to the first positioning sensor data, it first determines whether an anchor signal in the first positioning sensor data is detected, and if the anchor signal is detected, according to a received signal strength value of the anchor signal, a nearest neighbor matching algorithm is used to perform location fingerprint matching on the anchor signal and a location fingerprint database, distances between the signal strength value and each fingerprint data in the location fingerprint database are calculated, a fingerprint data corresponding to a minimum distance is selected from the distances, and the geometric coordinate represented by the fingerprint data is used as the initial location of the terminal 1; if the anchor point signal is not detected, the initial position is determined by sequentially selecting the blind area point of the GPS/base station signal, the characteristic point of the positioning sensor data of the terminal 1 in a special state and interacting with the terminal 1 (selecting points on a map, scanning two-dimensional codes and the like) according to the priority. The characteristic point of the special state is data when the change of the positioning sensor data is larger than a preset threshold value.
Here, when the positioning server 3 determines the initial position of the terminal 1, the initial position data is stored in the position database. Wherein, the signal intensity value is measured by a WiFi/Bluetooth signal receiving module of the terminal 1; the first positioning sensor data may include: acceleration, angular velocity, and orientation; the anchor signal may include: Wi-Fi signals or Bluetooth signals.
After the positioning server 3 determines the initial position of the terminal 1, real-time geometric coordinates of the terminal 1 are carried out by using a Pedestrian Dead Reckoning (PDR) method, meanwhile, anchor signals are detected at regular time in a second preset period, and multi-source information such as feature points of the anchor signals, indoor space information (indoor map) and feature points of first positioning sensor data are fused by using a particle filter fusion positioning algorithm so as to further correct positioning accumulated errors in the PDR method process, thereby determining the real-time geometric coordinates of the terminal 1. The second preset period can be determined according to the configuration of the terminal 1, and is generally set to 10 to 20HZ, and preferably 11HZ, 12HZ, 15HZ, 18HZ or 19 HZ; the characteristic point of the anchor point signal is the anchor point signal strength value when the anchor point signal has mutation. The indoor map includes: the positions of indoor space elements such as walls, rooms, corridors, doors and the like and the structures of the indoor space elements.
Specifically, the determining, by the positioning server 3, the first real-time geometric coordinate of the terminal 1 by using the multi-source information such as the particle filter fusion positioning algorithm PDR method, the anchor signal, and the indoor spatial information specifically includes:
the positioning server 3 records the state vector of the moving target object to be positioned of the holding terminal 1 as Xi＝(xi,yi,αi)TI-1, 2, …, N, wherein (x)i,yi) Representing coordinates, aiParameters of the Weinberg step size model. Then, the sensor model of the particle filter fusion localization algorithm is shown in formula (1), and the motion model is shown in formula (2):
Z k = ( a k , θ k ′ , θ · k ) T + η - - - ( 1 )
wherein, in the formula (1), akIs the acceleration of the k step, the acceleration akCan be measured by an accelerometer in the terminal 1; thetak' is the orientation of step k, which can be derived from compass measurements of terminal 1,is the angular velocity of the k stepWhich may be measured by a gyroscope in terminal 1, η representing a gaussian random process.
x k i = x k i - 1 + s k i - 1 sinθ k i - - - ( 2 )
y k i = y k i - 1 + s k i - 1 cosθ k i - - - ( 3 )
Wherein, in the formula (2),is formed by a parameter ofiWeinberg step size modelAnd (6) calculating.The orientation of the ith particle in the kth step can be calculated by equation (4):
θ k i = k f ( H · Z k ) - - - ( 4 )
wherein, in the formula (4),the kalman filter calculates the result from compass and gyroscope measurements. The specific calculation steps of the particle filter fusion positioning algorithm can be described as follows:
a) initialization: namely, the initial position of the target is calculated according to the signal intensity value, and the orientation of the target is determined according to the measurement value of the compass.
b) And (3) prediction: obtaining the states of N particles at k moment according to the motion model of the target
c) Calculating a weight value: there are two cases where the weights need to be recalculated. In the first case, the weight of a particle passing through a wall or obstacle is assigned to 0; in the second case, when a feature point is encountered, the weight of the particle is recalculated based on the distance from the feature point, with the closer the distance, the greater the weight. In the invention, by giving a larger weight to the particles closer to the characteristic point, the effect of correcting the positioning error can be achieved, and good user experience can be kept. The calculation formula of the weight is shown in (5).
w k i = w k - 1 i 1 2 π σ exp ( - | | X z k - X x k i | | 2 2 σ 2 ) - - - ( 5 )
Wherein, in the formula (5),σ is the corresponding standard deviation for the coordinates of the feature points.
In addition, when wireless signals (Wi-Fi and bluetooth) exist in the environment, and the positioning server 3 captures feature points (some feature points without obvious marks, such as geomagnetic abnormal points, turning angles and the like, exist in the environment) by resolving the positioning sensor data of the terminal 1, then the position information obtained by the feature points and the wireless fingerprints is weighted and averaged, and the particle weight is updated; if only the wireless signal is sensed, updating the weight value by using the position obtained by the wireless fingerprint; when only the feature points are captured, the weight values are updated based on the positions where the feature points are generated. Wherein the wireless fingerprint is the signal strength value described above; the position information is real-time geometric coordinates; after the weight of the particle is calculated, normalization processing needs to be performed on the weight according to formula (6):
w k i ′ = w k i / Σ j = 1 N w k j - - - ( 6 )
wherein, in the formula (6),representing the weight of the ith particle at time k,representing the sum of the weights of all particles.
d) And (3) state estimation: filtered state probability distribution p of moving objectsk(xk|y1:k) Can be approximately expressed as:
p ( x k | z 1 : k ) ≈ Σ i = 1 N w k i ′ δ ( x k - x k i ) - - - ( 7 )
and thus an estimate of the position state can be derived, as shown in equation (8):
X k ′ = Σ i = 1 N w k i ′ x k i - - - ( 8 )
e) resampling: the basic idea of resampling is to replace small weighted particles with large weighted particles. When the number of samples is insufficient due to elimination of invalid particles, resampling needs to be carried out according to the information at the previous moment, and the Weinberg step size model parameter a does not need to be updated at the momenti。
f) And (6) correcting. Whether the target reaches the vicinity of the feature point is determined based on the measurement values of the sensors. If the area in the vicinity of a certain feature point is reached, the position and orientation of the target are corrected based on the feature point, and (b) to (f) are cyclically executed.
And when the positioning server 3 does not detect the anchor signal in a second preset period, determining a real-time geometric coordinate of the terminal according to the PDR method, specifically: the positioning server 3 captures a walking event of a user on the basis of determining the current geometric coordinate of the user terminal, calculates the walking step length of a pedestrian according to an accelerometer, determines the orientation of the pedestrian according to a compass, carries out constraint through indoor space information (an indoor map), calculates the next position of the user, further determines the first real-time geometric coordinate of the terminal 1, and corrects the real-time position information of the terminal 1 estimated by the pedestrian dead reckoning method when the positioning server 3 detects the anchor point signal so as to reduce the accumulated error of the pedestrian dead reckoning PDR method.
Here, when the positioning server 3 determines the second geometric coordinate of the target friend terminal 2 according to the second positioning sensor data, the method is completely the same as the method for determining the first geometric coordinate of the terminal 1, and details are not repeated here.
Further, after the positioning server 3 determines the first real-time geometric coordinate and the second real-time geometric coordinate of the terminal 1 and the target friend terminal 2, the location server 4 is configured to obtain indoor spatial location model data, and construct a spatial topology network diagram of each level of an indoor location model according to the indoor spatial location data.
Specifically, the spatial topological network diagram of each level of the indoor spatial location model includes: fine grain level AEGVG diagram, exit level model diagram and position level model diagram. The step of the location server 4 constructing a spatial topological network diagram of each level of the indoor location model according to the indoor spatial location data specifically includes:
and according to the indoor space characteristics and the motion characteristics of the moving target object, constructing a fine-grained layer AEGVG diagram of the indoor space position model HiSeLoMo based on an indoor floor plan diagram, and determining the geometric coordinates, symbolic positions, topological relations and time-space relation semantic information of the indoor space object. The semantic information may specifically be: the communication relationship between the rooms and the corridors, the proximity relationship between the rooms, the geometric coordinates of the mobile objects, the symbol positions (room numbers), the functions, the space-time constraint and other attributes. The indoor space position model HiSeLoMo has a frame as shown in fig. 2.
Specifically, the aesgvg diagram of the fine-grained layer of HiSeLoMo includes: the method comprises the steps of obtaining a one-dimensional Voronoi diagram of a long and narrow indoor space area and a two-dimensional regular coverage grid diagram of an open area. Typically, the room space elongated region is expressed by a one-dimensional Voronoi diagram, and the open region is expressed by a grid diagram. Wherein, when the width of the indoor space unit is less than or equal to a certain value (e.g. 3m), the area is called as a narrow and long area, such as a corridor; when the area where the width of the indoor space unit is greater than a certain value (e.g., 3m) is an open area, such as a hall, etc.
Here, the generation of the fine-grained layer aesgvg map of HiSeLoMo specifically includes:
firstly, extracting a one-dimensional skeleton according to the indoor floor plan to form a Voronoi diagram, wherein the one-dimensional skeleton is shown in figure 3; carrying out grid division on an open area according to a preset side length to form a grid map, and adding the grid map into the Voronoi diagram; sampling nodes at sampling intervals of average step sizes of pedestrians, and creating the AEGVG graph, wherein the AEGVG graph is shown in FIG. 4. The average pedestrian step length is used as the side length to sample the nodes, the motion characteristics of pedestrians are met, the number of the nodes in the model can be reduced to the maximum extent, and the pedestrian step length is about 1 m. Meanwhile, the step length of walking of a person is considered to be about 1 m. Therefore, the open area is divided by a square grid with a side length of 1m, and an open area graph model is constructed on the basis of the division.
Here, the fine grain layer aesgvg graph model of HiSeLoMo may be formally defined according to equation (9):
Gfine＝(Vfine,Efine) (9)
in formula (9), Vfine＝{viIs a collection of nodes in the AEGVG graph;is a collection of edges in the AEGVG graph; each edge is composed of two nodes, shown by equation (10).
e＝(Vi,Vj) (10)
Wherein each nodeEach node describes a certain discrete position of the indoor space and has the attributes of geometric coordinates, states, labels and the like; in general, the attribute information of the node may pass through vid,xv,yv,cv,sv,lv,fv,bv>To indicate. V isidIs the number ID of the node; said (x)v,yv) Is the geometric coordinate of the node; c is mentionedvIs the spatial type of the node, cv∈ { from, corridor, door, vertical, passage }; said svIs the physical state of the node, the sv∈ { free, ocpuied }, the lvIs the label attribute of the node, said fvFor the floor identification where the node is located, bvAnd identifying the building where the node is located.
Further, the edge E ∈ EfineExpresses each node in the AEGVG graphThe edge attribute is<eid,vi,vj,fe,be,we>Wherein v isi,vjTwo end nodes representing edges, feAnd beThe symbolic positional attributes representing the edges, i.e. the floors and building information to which the edges correspond. There may be one-to-many dependencies of an edge, i.e. an edge passes through multiple functional space elements. Said weThe weight of an edge is usually expressed by a euclidean distance between two nodes.
Secondly, constructing a position hierarchical model; specifically, a coarse-grained location-level model is abstracted on the basis of the fine-grained layer AEGVG graph model. Here, the location hierarchy expresses semantic information such as topological relationships (e.g., adjacency and inclusion relationships) and spatio-temporal relationships (e.g., spatio-temporal distances, spatio-temporal constraints) between objects in a hierarchical organization. In general, positions are divided into three major categories: rooms (Room), Vertical elevator spaces (Vertical Passage), including stairways, elevators, etc.; corridor (Corridor). The hierarchy here refers to the adjacent reachable order relationship between positions, such as: which adjacent positions pass through in sequence from a certain entry position, and the adjacent positions serve as child nodes of the entry position in the hierarchical diagram; or spatial containment relationships between locations, such as: a floor contains which locations that are child nodes of the hierarchical map.
Based on the AEGVG graph model of the fine-grained layer, the AEGVG graph model will have the same label attribute lvIs aggregated into one symbol position. Determining a position node of the coarse-grained layer according to the symbol position; after the position nodes in the coarse-grained layer are formed, a complete position hierarchical model in the coarse-grained layer can be formed according to the adjacency and communication relation among the position nodes. The position hierarchical model generally represents a hierarchical graph model of symbolic positions with nodes and edges representing positional adjacency or containment relationships, which can be expressed by equation (11).
Gloc＝(Vloc,Eloc) (11)
In formula (11), Vloc＝{viRepresents the set of all symbol positions;represents a set of positional adjacency or containment relationships in the AEGVG graph; each side eloc＝(vi,vj∈Eioc). At the same time, each symbol position vi＝<locid,cloc,lloc,floc,bloc,adj_loc>Said locidNumbering for abstract position spaces, clocAs a class of abstract location space, said cloc∈{room,corridor,vertical passage}，llocSymbolic semantic information representing an abstract location space; f. oflocRepresenting the floor where the abstract position space is located; blocA building representing an abstract location space; at the same time, the user can select the desired position,is the set of all locations that have a neighboring relationship to the abstract location.
In practice, taking a fourth floor of a certain engineering building as an example, abstracting an AEGVG graph of the fine grain layer of the fourth floor to form position nodes, as shown in fig. 5, room positions are represented by circular nodes, vertical lifting space positions are represented by square nodes, and corridors are represented by triangular nodes. For example, the fine-grained nodes in the vertical space VP2, corridor segment HW4, and room RM12 in the fine-grained layer are abstracted into position nodes VP2, HW4, and RM12 in the coarse-grained layer, respectively. After the position nodes in the coarse-grained layer are formed, a position hierarchy is formed according to the relationship between the position nodes, as shown in the lower left of fig. 5. For example, location node VP2 communicates with corridor section node HW4, HW4 communicates with corridor node HW5, and HW5 communicates with or is adjacent to location nodes RM14, HW6, and the like. And forming a complete position hierarchical model in the coarse-grained layer through the adjacency and communication relation among the position nodes.
Then, determining an exit node of the coarse-grained layer according to the exit position in the fine-grained layer AEGVG graph; and constructing the exit level model by taking the reachable paths between the adjacent positions as edges.
Specifically, in combination with a position hierarchical model of a HiSeLoMo coarse-grained layer, in order to support distance and topological expression between coarse-grained positions, an export hierarchical model of coarse granularity is abstracted on the basis of a fine-grained layer model. Here, the exit hierarchy expresses semantic information such as topological relationships (e.g., connectivity, order relationships), distances, constraints, etc., between exit locations in a hierarchical organization. The outlet refers to a connection point for communicating two reachable position spaces in the chamber and comprises an actual outlet and a virtual outlet. The actual outlet is the accessible entrance to two space units, usually the room door; the virtual outlet is an inlet and outlet artificially defined according to the communication relationship between the subspace units, and does not exist in the indoor structure. One outlet can only communicate with two position spaces, one space unit can contain a plurality of outlets, and the outlets are the only way for connecting different space units. The hierarchy indicates the communication relationship between the ports (e.g., a certain exit position communicates two spatial positions), or the sequential relationship of the exits passing through the process of reaching a certain exit position (e.g., the sequential relationship of the exits passing through the exit from a certain floor to a certain position).
The outlet level corresponds to an outlet node set communicated with different space units in the fine-grained layer, and the set is according to the class attribute c of the space in the AEGVG model of the fine-grained layervAnd extracting the nodes of the exit. The egress nodes form a hierarchy based on the adjacency (order of arrival) of the space, where the topmost node represents the entry into the space, and from the top node down, the nodes at different levels represent the reachable hierarchical order. As shown in fig. 6, the exit node DR57 corresponding to the VP2 area in the plan view of the fourth floor of a project floor is the top node, and can reach two exits DR55 and DR20, so that two exit nodes DR55 and DR20 are child nodes of DR 57.
An export hierarchical model is constructed by abstracting the position export into coarse-grained export nodes and taking reachable paths between adjacent positions as edges. The export hierarchy model may be represented by equation (12).
Gexit＝(Vexit,Eexit) (12)
In the formula (12), Vexit＝{viIs the set of all egress nodes, which can be represented by equation (13).
vi＝<exid,lex,loci,locj,parentex>(13)
In the formula (13), exidThe number of the exit node is represented and is consistent with the number of the node with the fine-grained layer space type of door, and lexSemantic location information representing an egress node, such as a functional attribute of the space represented by the node. The outlet location is typically connected to two communicating locations, the two communicating locations passing through (loc)i,locj) And (4) showing. lociAnd locjThe two positions are respectively corresponding to two positions in the position hierarchy, and the two positions refer to any two nodes in the position hierarchy. The parentexIndicating the parent node number of the egress node in the egress hierarchical tree structure,and Eexit＝Vexit×VexitIs the set of all reachable paths, each path can be represented by equation (14):
eexit＝vi×vi(14)
wherein e isexit∈Eexit。
Further, a moving object layer model of the indoor space position model HiSeLoMo is constructed. In particular, in a mobile computing environment, there are a large number of mobile objects (e.g., people, mobile assets, etc.). For convenience of description, the mobile object may be represented as < Moving obj id, (x, y, t), obj semantic >; wherein,
the MovingObjID is the number of the moving object, the (x, y, t) is the geometric coordinate of the moving object at the time t, and the objsemantic is the semantic information of the moving object.
Here, let Σ objsemantic ═ Σ person ≧ Σ asset },
then obj _ semantic e ∑ obj _ semantic ═ person _ id, asset _ id.
In order to simplify the dynamic topological relation between moving objects, a topological graph G based on HiSeLoMo fine-grained layerfineMapping the topological relation of the moving object MovingObject at a certain time t to the topological graph G of the fine-grained layerfine-subAs shown in fig. 7. The specific expression mode is as follows: according to the position (x, y) of the moving object MovingObject at a certain time t, a topological graph G at a fine-grained layerfineQuerying a node nearest to the position, namely, a NearestNode; the topological relation of the moving object MovingObject at the time t can be represented as a fine-grained layer topological subgraph G of the NeoestNodefine-sub. Wherein,then MovingObjecti,tWith NaerestNodeiThe phase is mapped, and the mapping relationship can be represented by equation (15).
f:MovingObjecti,t→NearestNodei(15)
And finally, determining the interlayer relation of the indoor space position model HiSeLoMo.
Specifically, the position hierarchy model in the coarse-grained layer may be aggregated from the fine-grained layer, the export hierarchy model may be derived from the fine-grained layer, and the position hierarchy and the export hierarchy may also be derived from each other, as shown in fig. 8. Since an exit connects two adjacent spaces, including such a communication or proximity relationship in the location level and the exit level, the exit level and the location level can be derived from each other. The relationship between the fine-grained layer and the attributes in the position layer and the exit layer is shown in fig. 9, and the attributes of the nodes and edges in the exit layer and the position layer are derived from the fine-grained layer.
Further, after the location server 4 constructs a spatial topological network diagram of each level of the indoor location model, the location server is specifically configured to receive an influence factor of each navigation path in the spatial topological network diagram; receiving the influence weight of each influence factor on the current navigation path; and calculating the comprehensive weight of each path according to the influence weight. Wherein, the influence factors specifically include: indoor pedestrian reachable distance, reachable time, personnel density and road width.
Specifically, in this embodiment, the optimal navigation path is mainly calculated by using the reachable distance of an indoor pedestrian as an influence factor, so that the reachable distance of an indoor road is a weight of the path, and then the reachable distance of an indoor pedestrian based on the constructed indoor location model can be calculated by the formula (16):
I O D ( o i , o j ) = &Sigma; k = 1 m ( x k + 1 - x k ) 2 + ( y k + 1 - y k ) 2 - - - ( 16 )
wherein, in the formula (16), the IOD (O)i，Oj) Is the indoor pedestrian reachable distance; said O isiA first moving object corresponding to the terminal 1; said O isjA second moving object corresponding to the target friend terminal 2; said (x)x,yk) For the distance in the fine-grained layer to the first moving object OiNearest node nkThe coordinates of (a); and m is an integer.
Here, while the location server 4 constructs a spatial topological network map of each level of the indoor location model, the first real-time geometric coordinates and the second real-time geometric coordinates of the terminal 1 and the target friend terminal 2 are obtained from the positioning server 3, and an optimal navigation path is calculated in the spatial topological network map by using a path search algorithm, where the optimal navigation path is a path with a shortest indoor pedestrian reachable distance, and an evaluation function of the path search algorithm is as follows: (n) ═ g (n) + h (n); wherein, f (n) is a valuation function from the initial node to the target node through the node n; the g (n) is the actual cost of the initial node to node n in the state space; the h (n) is the actual cost of the optimal navigation path from the node n to the target node. In this embodiment, the euclidean distance between the node n and the target node is used as the weight value, and the specific steps of the path search are as follows:
(1) mapping the first real-time geometric coordinates of the terminals 1 at the two sides of the navigation and the second real-time geometric left side of the target friend terminal 2 to fine-grained layer nodes in the indoor position model, wherein the starting node and the end node are respectively vstartAnd vgoal。
(2) Connecting the starting node vstartPut into the OPEN list OPEN (the OPEN list has both f and g values of 0).
(3) At vstartLocation cell space locstartAnd starting path expansion search, searching the node with the minimum value in the OPEN, and taking the searched node as the current node.
(4) The current node is deleted from OPEN and added to the closed list CLOSE.
(5) Sequentially executing the steps (6) - (8) for each node adjacent to the current node when the destination node v isgoalWhen the path is added into the open list as a node to be checked, the path is searched, and the circulation is ended; or when locstartCorresponding exit node vexit-sWhen put into the open list as a node to be checked, represents the cell space loc at the current locationstartWhen the path is not searched, the fine-grained layer is switched to the exit layer for path expansion search, and the exit node v is searchedexit-sDelete from OPEN, put closed list CLOSE, and perform step (9).
(6) If the neighbor node is not current or is already in CLOSE, then the next node continues to be expanded.
(7) If the adjacent node is not in the OPEN, the node is added to the OPEN, and the parent node of the adjacent node is set as the current node, while the g value and the f value of the adjacent node are saved.
(8) If the adjacent node is in the OPEN, whether the g value reaching the adjacent node through the current node is smaller than the original stored g value is judged, if so, the parent node of the adjacent node is set as the current node, and the g value and the f value of the adjacent node are reset.
(9) With vexit-sPerforming steps (6) - (8) for each adjacent exit node of the current node at the exit layer when the destination node vgoalLocation cell space locgoalCorresponding exit node vexit-gWhen the node is added into the open list as the node to be checked, the exit layer is switched to the fine-grained layer for path expansion search, and the exit node v is searchedexit-gDeleted from OPEN, put in the closed list CLOSE, and perform step (10).
(10) With vexit-gAnd (5) returning the current node to the fine grain layer, and executing the steps (6) - (8) on each adjacent fine grain layer node when the destination node v returns to the fine grain layergoalWhen the path is added to the OPEN list OPEN as the node to be checked, the path is searched, and the circulation is ended; or when empty, indicating that there is no new node that can be added, and that there is no destination node v in the checked nodesgoalThis means that a path cannot be found, and the loop is also ended.
And determining the optimal navigation path, wherein the position server 4 displays the optimal navigation path on an interface of the terminal 1, and meanwhile, in the navigation process, the terminal 1 and the target friend terminal 2 can also increase interaction and contact in the process of finding a person through text chatting so as to supplement information lacked in the map navigation process.
When the indoor navigation system provided by the embodiment determines the optimal navigation path by combining the hierarchical indoor position model and the path search algorithm, the complexity of the algorithm can be reduced, and the search efficiency and the navigation precision can be improved.
Example two
Corresponding to the first embodiment, after the real-time geometric coordinates of the terminal 1 can be determined, the check-in content can be published in the social network according to the geometric coordinates; specifically, when the terminal 1 receives a sign-in request, the location server 4 matches the symbolic location of the sign-in point of the terminal 1 in the geometric coordinates with semantic information in the indoor space location model database to obtain semantic location information of the sign-in point; uploading the received dynamic text record content and picture content of signing in; meanwhile, the semantic position of the check-in point is displayed and the position of the check-in point is displayed on the map. Here, the terminal 1 may enter the social network through a wireless network established by Wi-Fi, or may enter the social network through a 3G/4G network of a mobile operator.
Here, before issuing the check-in content, the terminal 1 may further set a visibility right, that is, the check-in content and the location are visible to which friends, but not visible to which friends, so as to protect the privacy of the user.
In practical applications, people often record activity dynamics of particular significance in daily life, for example, people go to a delicious restaurant on weekends to enjoy delicious food, go to a report hall to listen to reports, or buy practical clothes at a clothing store. The position sign-in dynamic depicts the real activity information of people in the real world at a certain time and a certain place, and the indoor position granularity information with fine granularity can more truly reflect the space activity of people. After the user signs in the positions and dynamically shares the positions in the mobile social network, an image tag of the user in the mobile social network is gradually constructed, and the impression construction requirement of the user in the friend mind is met. For example, a user has more dynamic check-in at a place published by a library or a bookstore, and friends of the user have tags and impressions such as "scholarly tyrant", "favorite study" and the like in mind of the user.
In the embodiment, the terminal 1 collects the data of the positioning sensor in real time, and the positioning server 3 calculates the real-time geometric coordinates of the terminal 1 by using a hybrid positioning technology (a PDR method, Wi-Fi and Bluetooth), so that the dynamic property and high precision of the geometric coordinates of the check-in point when the user issues the check-in are ensured.
EXAMPLE III
Corresponding to the first embodiment, the location server 4 calculates the distance between the terminal 1 and each friend terminal; after the social application server 5 displays the friend terminal (in a list form) on the interface of the terminal 1 according to the distance, the terminal 1 is further configured to send a tracking request to the target friend terminal 2 through the instant messaging server 6.
When friend tracking is carried out, because the access right of the user position is involved, a tracking request mechanism is introduced, namely, the terminal 1 sends a friend tracking request to a target friend terminal through the instant messaging server 6, and after the friend tracking request is allowed, the terminal 1 sends the friend tracking request to the position server 4; after receiving the friend tracking request, the location server 4 acquires a second geometric coordinate of the target friend terminal 2 from the positioning server 3 in a third preset period according to the friend tracking request, matches the second geometric coordinate with a fine-grained layer node in the indoor space location model database to obtain a closest node, and acquires location semantic information of a location layer corresponding to the node; and the terminal 1 displays the position semantic information of the target friend terminal 2 on an interface. The matching process and the building process of the indoor space position model are completely the same as those of the first embodiment, and are not described again; the third preset period may be 1 to 3HZ, and preferably, may be 1HZ, 1.5HZ, or 2 HZ.
Here, after the tracking request is confirmed, both the terminal 1 and the target friend terminal 2 may view the location information of each other to know the mutual distance therebetween.
When the target friend is a special group, the tracking request mechanism needs to be forcibly allowed or preset to be allowed; wherein the special population may include: children, elderly people or patients, etc.
Further, the terminal 1 may further receive a preset geo-fence of the target friend terminal 2, and when the location server 4 determines that the location of the target friend terminal 2 exceeds the geo-fence, the social application server 5 is configured to push a notification message to the terminal 1. The geo-fence is specifically a motion area of the target friend terminal 2.
In practical application, when a and B travel in indoor shopping malls, because the people in the shopping malls are dense and the attention points of the two people are different, the two people are likely to be buried in the stream of people and dispersed by the stream of people. The A and B can use the dynamic tracking of the friend to check the position of the other party in real time, meanwhile, a reminding range is set, when one party leaves the range, the other party can receive the reminding message of the friend going away, and then the user can quickly realize that the friend is to be found.
In addition, if the nursing staff is limited in indoor places such as a nursing home, a kindergarten, a hospital and the like, a special terminal positioning device is provided for the corresponding tracking object, and the nursing staff can know the position of the tracking object in real time through the terminal 1. When the tracked object is careless to pay attention to and walk out of a certain range, the nursing staff receives message reminding immediately.
In this embodiment, the terminal 1 collects the data of the positioning sensor in real time, and the positioning server 3 calculates the real-time geometric coordinates of the terminal 1 by using a hybrid positioning technology (Wi-Fi, bluetooth and PDR methods), so as to ensure the dynamic and high accuracy of the real-time geometric coordinates of the friend when the user tracks the friend.
Example four
Corresponding to the first embodiment, the location server 4 may further implement message pushing of the fine location information, and the specific process includes the following steps:
step a: the terminal 1 sends a geo-fencing service request to the location server 4;
step b: after the location server 4 confirms the request, the location server 4 starts a location tracking function, and the terminal 1 sends location sensor data to the location server 3 at regular time;
step c: the positioning server 3 determines the real-time geometric coordinates of the terminal 1 according to the positioning sensor data and sends the real-time geometric coordinates to the position server 4; here, the method for determining the real-time geometric coordinates of the terminal 1 by the positioning server 3 according to the positioning sensor data is completely the same as the method for positioning the real-time geometric coordinates of the friend of the terminal 1 in the first embodiment, and details are not repeated here.
Step d: when receiving the real-time geometric coordinates of the terminal 1, the location server 4 determines whether the real-time geometric coordinates meet a preset triggering condition for pushing a message, if so, pushes a corresponding service message to the terminal 1, when the terminal 1 sends the reading mark information of the read service message to the location server 4, the service is completed, otherwise, pushes the corresponding service message to the location server 4 again. If not, continuously acquiring the real-time geometric coordinates of the terminal 1 from the positioning server 3, and repeating the judgment process. Wherein the trigger condition is a preset geo-fence range, and may include: 10m, 20m, etc.
In practical application, when the user a enters a shopping mall, the user a subscribes to a message push service of a certain point of interest (a special shop). When Alice shops near a specializing store, it will automatically receive a message that the store's offers, novelties, etc. are with the specializing store. Unlike the peripheral query function of the first embodiment, the peripheral query function is a service that requires a subscription to specific point of interest information and automatically receives the subscription according to a location proximity relationship. Of course, the point of interest information is not limited to the merchant information mentioned in the embodiment, and includes museum exhibits, office departments, airport duty-free stores, and the like.
EXAMPLE five
Corresponding to the first embodiment, the terminal 1 is further configured to send a query request and a query parameter to the location server 4; the location server 4 is configured to search for an object meeting the query parameter according to the query request, and return the geometric coordinate of the query object meeting the query parameter to the terminal 1. Here, the query parameters include: query object type, query range, interest point type and query quantity; the query object categories include: peripheral friend query and peripheral interest point query. The interest point categories include: digital products, clothing, gourmet food, etc. The query scope may include: 10m, 20m, 50m, 100m, etc.
After the position server 4 receives the query request and the query parameters, judging the query object type; when the query object type is determined to be the query of surrounding friends, sending a query request to the social application server 3, and sending friend information of the terminal 1 to the location server 4 by the social application server 5 according to the query request; and after receiving the friend information, the location server 4 acquires real-time geometric coordinates of each friend of the terminal 1 from the positioning server 3 in a fourth preset period according to the friend information. The method for acquiring the real-time geometric coordinates of each friend in this embodiment is completely the same as the method for acquiring the real-time geometric coordinates of the terminal 1 in the first embodiment, and is not described herein again. The friend information may include: friend head portraits, names, friend social relationships and the like. Wherein the fourth preset period is 1 Hz.
After the positioning server 3 determines the real-time geometric coordinates of each friend of the terminal 1, the real-time geometric coordinates of each friend are stored in a position database, the real-time geometric coordinate information of the corresponding friend is sent to the position server 4 according to the friend information sent by the position server 4, and after the position server 4 receives the real-time geometric coordinate information of the corresponding friend of the terminal 1, the real-time geometric coordinate information is mapped to a fine-grained layer of an indoor space position model. The real-time geometric coordinates cannot be visually displayed through a terminal, and the semantic position information can be visually displayed through the terminal. The indoor space position model is composed of a fine-grained layer, an outlet layer and a position layer. The fine-grained layer is an adaptive graph model consisting of nodes and edges, wherein the nodes represent specific position points in the indoor space, and the edges represent the connection relation among the position points. And the exit layer and the position layer abstract an exit graph and a semantic position graph from the fine-grained layer, wherein the exit graph represents exit nodes and topological relations thereof, and the semantic position graph represents each indoor subspace and topological relations thereof. The building process of the indoor spatial location model is completely the same as that of the indoor spatial location model in the first embodiment, and is not described herein again.
After the real-time geometric coordinate information of the corresponding friend of the terminal 1 is mapped to a fine-grained layer in an indoor space position model, the position server 4 also determines the real-time geometric coordinate of the terminal 1 by using the same positioning method, searches an object conforming to the query parameter by using a peripheral query algorithm, and returns the geometric coordinate information of the query object conforming to the query parameter to the terminal 1. In this embodiment, the detailed process of the peripheral query algorithm based on the hierarchical indoor location model is as follows:
(1) inquiring the geometric coordinates of the mobile reference point (namely the terminal 1 initiating the inquiry request) and obtaining the corresponding network node;
(2) obtaining the result of the first search tree through the hierarchical network expansion, and obtaining a mobile object meeting the conditions in the range of the network expansion;
(3) if the reference point does not move, the network expansion search tree does not change, and a mobile object meeting the conditions can be directly obtained;
(4) if the reference point moves, updating the root node of the network search tree, wherein the root node is the network node mapped by the current reference point;
(5) next, acquiring a boundary node according to the inquiry based on the position at the previous moment, judging whether the boundary node exceeds a range threshold value, and if the boundary node is within the range threshold value, continuing network expansion; wherein the range threshold is a preset movable range, for example, 10m, 20m, etc.
(6) If the boundary node is not within the range threshold, the parent node of the boundary node is tracked reversely, all nodes with the distance values larger than the range threshold are deleted along the parent node pointer, and the updated network expansion search tree is obtained;
(7) and finally traversing the updated network expansion search tree to obtain the mobile friends meeting the conditions.
Further, the detailed process of the hierarchical network extension method involved in step (2) is as follows:
(a) and obtaining a location unit space name identifier of the reference point according to the reference point, performing network expansion in a fine-grained layer diagram corresponding to the identifier, and stopping when the identifier is expanded to an exit node connected with the location unit space.
(b) And switching the network expansion to an exit layer for expansion, performing network expansion on all exit nodes of the space unit at the position of the mobile reference point, and adding all exit nodes with the distance to the reference point less than or equal to the range threshold value into the network expansion search tree in the process.
(c) And (3) leaf nodes of the network expansion search tree obtained in the step (2) are all exit nodes, a corresponding fine-grained layer graph is obtained according to the position space unit connected with the exit nodes, and network expansion is carried out on the fine-grained layer. And stopping the expansion when the distance from the expanded node to the reference point is greater than the range threshold.
In addition, when the query object category is a peripheral point of interest query, the location server 4 is further specifically configured to: reading (connecting and accessing) the interest point information in the indoor space model; and calculating the interest point result by utilizing a peripheral query algorithm. And returns the point of interest result to the terminal 1. The process of querying the interest point is the same as the process of querying the surrounding friends, and is not described herein again.
According to the peripheral position query function provided by the embodiment, the peripheral friend positions and the interest points are positioned by utilizing a particle filter fusion positioning algorithm and combining an indoor space position model, so that the positioning precision and the dynamic property are improved; and the query results of surrounding friends and interest points are calculated by using a surrounding query algorithm, so that the accuracy of the query results is improved, and the user experience is improved.
The above description is only exemplary of the present invention and should not be taken as limiting the scope of the present invention, and any modifications, equivalents, improvements, etc. that are within the spirit and principle of the present invention should be included in the present invention.

Claims (10)

1. An indoor social navigation system, the system comprising:
the positioning server is used for receiving first positioning sensor data sent by a terminal and second positioning sensor data sent by a target friend terminal, calculating a first geometric coordinate of the terminal according to the first positioning sensor data, and calculating a second geometric coordinate of the target terminal according to the second positioning sensor data; storing the first geometric coordinate and the second geometric coordinate in a position database;
the position server is used for acquiring indoor position model data and constructing a spatial topological network diagram of each layer of the indoor position model according to the indoor space position data;
and receiving a navigation request sent by the terminal, acquiring the first geometric coordinate and the second geometric coordinate from the positioning server according to the navigation request, and calculating an optimal navigation path in the space topology network map by using a path search algorithm.
2. The system of claim 1, wherein the spatial topology network map of each level of the indoor location model comprises: fine grain level AEGVG diagram, exit level model diagram and position level model diagram.
3. The system of claim 2, wherein the location server building a fine-grained hierarchical AEGVG graph of an indoor location model from the indoor spatial location data specifically comprises:
extracting a one-dimensional framework according to the indoor floor plan to form a one-dimensional Voronoi diagram of an indoor space long and narrow region;
carrying out grid division on the open area according to a preset side length to form a grid map, and adding the grid map into the Voronoi diagram;
sampling nodes by taking the average step length of the pedestrians as sampling intervals, and generating the AEGVG graph.
4. The system of claim 2, wherein the location server building an egress hierarchical model map of an indoor location model from the indoor spatial location data specifically comprises:
determining an exit node of the coarse-grained layer according to an exit position in the fine-grained layer AEGVG graph;
and constructing the outlet hierarchical model graph by taking the reachable paths between the adjacent positions as edges.
5. The system of claim 2, wherein the location server building a location hierarchical model map of an indoor location model from the indoor spatial location data specifically comprises:
determining a position node of the coarse-grained layer according to the symbolic position in the fine-grained layer AEGVG graph;
and generating the position hierarchical model graph according to the adjacency and communication relation between the position nodes.
6. The system according to claim 1, wherein the positioning server is configured to calculate a first geometric coordinate of the terminal from the first positioning sensor data, and in particular comprises:
when the positioning server detects an anchor point signal in the first positioning sensor data, performing fingerprint matching on the anchor point signal and a position fingerprint database to determine an initial position of the terminal;
and detecting the anchor point signal at regular time according to a preset period, and if the anchor point signal is detected, fusing a pedestrian dead reckoning PDR method, the anchor point signal and indoor space information by utilizing a particle filter fusion positioning algorithm to determine a first geometric coordinate of the terminal.
7. The system according to claim 1, wherein after the location server constructs the spatial topology network map of each level of the indoor location model, the system is further specifically configured to:
receiving an influence factor of each navigation path in the space topological network graph;
receiving the influence weight of each influence factor on the current navigation path;
and calculating the comprehensive weight of each path according to the influence weight.
8. The system of claim 7, wherein the impact factors specifically include: indoor pedestrian reachable distance, reachable time, personnel density and road width.
9. The system of claim 8, wherein the indoor pedestrian reachable distance is formulated by the formulaCalculating to obtain; wherein, said O isiA first mobile object corresponding to the terminal; said O isjA second moving object corresponding to the target friend terminal; said (x)x,yk) For the distance in the fine-grained layer to the first moving object OiNearest node nkThe coordinates of (a); and m is an integer.
10. The system of claim 1, wherein the path search algorithm's valuation function is: (n) ═ g (n) + h (n); wherein, f (n) is a valuation function from the initial node to the target node through the node n; the g (n) is the actual cost of the initial node to node n in the state space; the h (n) is the actual cost of the optimal navigation path from the node n to the target node.
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