Obstacle-avoiding mobile charging scheduling algorithm based on Fresnel model

Abstract

The invention discloses an obstacle avoidance mobile charging scheduling algorithm based on a Fresnel model, and belongs to the technical field of wireless rechargeable sensor networks. According to the method, a wireless rechargeable sensor network model is established, and a charging path is dynamically planned in combination with an improved algorithm. Specifically, a Fresnel diffraction model is used for dividing a charging signal enhancement area, a normal area and a weakening area, and the charging power is optimized; generating an initial charging path through simulated annealing, dynamically updating a starting point and a heuristic function when an obstacle is detected, and correcting the path in real time; and meanwhile, path smoothing processing is introduced to reduce redundant inflection points, and the mobile energy consumption is reduced. According to the method, the charging efficiency and the network life cycle are remarkably improved, the balance of high charging effectiveness and low energy consumption is realized by utilizing the obstacle enhancement signal instead of pure avoidance, and the real-time change in a complex environment is effectively adapted through dynamic obstacle avoidance and local re-planning capability.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless rechargeable sensor networks, and more particularly to an obstacle-avoiding mobile charging scheduling algorithm based on a Fresnel model. BACKGROUND

[0002] Obstacles will affect the propagation of wireless charging signals. When a mobile charger (MC) wirelessly transmits energy to a sensor node through WPT technology, if the wireless charging signal encounters an obstacle during propagation, part of the signal will be reflected, diffracted, scattered, etc., so that it cannot penetrate, and the strength of the charging signal may change, affecting the charging effectiveness.

[0003] In practical applications, there are often many obstacles in the coverage area of a wireless rechargeable sensor network (WRSN), causing instability of the charging signal. If the influence of obstacles on the charging process is not specifically analyzed, the applicability of WRSNs will be greatly reduced, thereby hindering their development and practical application.

[0004] Therefore, how to propose an obstacle-avoiding mobile charging scheduling algorithm based on a Fresnel model, describe the influence of obstacles on the charging process according to the actual situation, and at the same time focus on planning the obstacle-avoiding path of the MC to improve the utilization rate of limited energy, is a problem that those skilled in the art need to solve. SUMMARY

[0005] Therefore, the present application provides an obstacle-avoiding mobile charging scheduling algorithm based on a Fresnel model, which improves charging effectiveness and realizes global planning and dynamic obstacle avoidance of the charging path in combination with multiple algorithms.

[0006] In order to achieve the above-mentioned purpose, the present application adopts the following technical solutions: The present application proposes an obstacle-avoiding mobile charging scheduling algorithm based on a Fresnel model, comprising: A wireless rechargeable sensor network model is established based on the positions of sensor nodes, mobile chargers and obstacles; In the wireless rechargeable sensor network model, a charging optimization objective function is constructed according to the effective energy received by the sensor nodes from the mobile chargers, and the charging plan of the mobile chargers is planned with the limited energy of the mobile chargers as a constraint; The charging area of the mobile charger for the sensor nodes is discretized, and the charging sequence and sub-path length of the to-be-charged nodes are obtained by using the algorithm with the goal of maximizing the effective amount of charging; A charging position optimization objective is designed, and the optimal charging position of each sensor node is determined according to the charging position optimization objective; According to the optimal charging position, an initial charging path is obtained by taking the minimum number of dead nodes as the target; the mobile charger charges according to the charging plan along the initial charging path, and if an unknown obstacle is detected during the movement, the initial charging path is corrected by using the improved algorithm.

[0007] Preferably, a wireless chargeable sensor network model is established based on the sensor nodes, the mobile charger and the obstacle positions, and the model comprises: A two-dimensional network region is constructed, the two-dimensional network region contains a plurality of fixed known obstacles, the sensor nodes are randomly distributed in the obstacle-free area of the two-dimensional network region, and the base station is arranged at the center of the two-dimensional network region; In the two-dimensional network region, n concentric ellipses corresponding to n Fresnel zones are constructed with the position coordinates of the mobile charger and the sensor nodes as the focus points; Based on the Fresnel zone, the charging signal influence area of different charging positions of the mobile charger is constructed, and is divided into an enhanced zone, a normal zone and a weakened zone; the enhanced zone, the normal zone and the weakened zone are on the concentric circles drawn with the sensor nodes as the center; wherein, the boundary of the enhanced zone and the weakened zone is determined according to the boundary of the first Fresnel zone and a reference circle with the obstacle as the center, and the boundary of the enhanced zone and the normal zone is determined according to the boundary of half of the second Fresnel zone and the reference circle; In the different charging signal influence areas, the charging power of the i th sensor node s i at the i th candidate charging position l i is as follows: p l i , s i , wherein, a is a constant greater than 1; b is a constant less than 1; α is a hardware influence parameter; β is an environmental influence parameter; is the distance between the sensor node s i and the candidate charging position l i .

[0008] Preferably, the charging optimization objective function is as follows: U i , wherein, a is a constant greater than 1; s i ​​​​​​At the candidate charging location l i The effective energy received from the mobile charger, , is the rechargeable battery capacity of the sensor node, For mobile chargers to sensor nodes s i The energy transmitted, , For mobile chargers at candidate charging locations l i Charging time; The limited energy of the mobile charger is used as a constraint, including: ; Where M is the number of sensor nodes; The maximum energy that a mobile charger can use for wireless charging; e i For mobile chargers at candidate charging locations l i To sensor nodes s i Energy sent; is the initial charging path length of the mobile charger, The battery energy threshold that can be used for mobile chargers E move Maximum driving distance of the mobile charger under driving; is a set of candidate charging locations; is the set of locations to be charged.

[0009] Preferably, the charging area of ​​the sensor node is discretized and the charging efficiency is maximized by using The algorithm obtains the charging order and subpath length of the nodes to be charged, including: For each sensor node, s i The concentric circles drawn with the center as R The radius is concentric circles, so that the charging power difference between adjacent concentric circles does not exceed the threshold , the charging power of the same ring is equal, and each ring is equivalent to a charging power layer; j The radius of the concentric circles is: ; The minimum charging power in each charging power layer As the charging power of the entire charging power layer; where, , D is the maximum effective charging coverage distance of the mobile charger; Distance sensor nodess i The recent known obstacles are abstracted as a grid, and the circumscribed circle of the grid is taken as the reference circle; The enhanced area of the charging power layer is determined according to the reference circle and the Fresnel area; According to The algorithm traverses all possible mobile charger position points in the discretized charging area to obtain the charging sequence of the to-be-charged nodes and the sub-path length, with the objective of maximizing the charging effective amount.

[0010] Preferably, the objective function of the charging position optimization objective is as follows: ; When the mobile charger is located at the candidate charging position l i The sensor node s i receives the charging power; When the mobile charger is located at the candidate charging position l i , the distance from the next candidate charging position in the charging sequence of the to-be-charged nodes, is the weight coefficient for balancing the charging power and the moving cost; Each sensor node determines the corresponding optimal charging position from all candidate charging positions in the signal enhanced area of the first charging power layer according to the objective function of the charging position optimization objective, and outputs the optimal charging position set of all sensor nodes to form the to-be-charged position set .

[0011] Preferably, according to the optimal charging position, the initial charging path is generated by using and SA, with the objective of minimizing the number of dead nodes.

[0012] Preferably, if an unknown obstacle is detected during the movement, the initial charging path is corrected by using the improved algorithm, including: The starting point of the improved heuristic function is updated in real time as the current position of the mobile charger; The improved heuristic function is as follows: ; ; In the formula, x , y represents the horizontal and vertical coordinates of the current node s , , represents the horizontal and vertical coordinates of the starting point ,​ , represents the target node ; is a weight function; generates the child nodes s of the current node , based on the actual cost s from the current node to the target node g ( s ) and the estimated minimum cost s from the current node to the target node through the child node rhs ( s ), calculates the k value in combination with the improved heuristic function, arranges the node priority according to the k value, and determines the first modified node according to the node priority; updates the first modified node as the current node, and repeats the step of determining the modified node until the current node coincides with the target node; generates a local obstacle avoidance path according to all the modified nodes, and modifies the initial charging path; The calculation formula of k value is as follows: ; ; represents the set of all child nodes of the current node s , represents the moving cost from the current node s to the child node , represents the cost value of the child node to the target point, and is the actual distance of each movement; Arrange the k values in ascending order, preferentially arrange the k1 value, and continue to arrange the k2 value in the case of the same k1 value. The modified node is the candidate node corresponding to the k value ranked first, and the candidate node includes the child node generated based on the current node.

[0013] Preferably, the improved algorithm shortens the path distance by adding smoothing processing, including: Step 1. Mark the n path points on the modified charging path, sequentially marked as 1, 2,..., n from the starting point to the target point; Step 2. Connect path point 1 and path point 2, and judge whether the connecting line of the two points passes through the obstacle; Step 3. Check the next path point until the connecting line between the current path point q and path point 1 passes through the obstacle; Step 4. Take waypoint 1 and waypoint q- 1 is the endpoint, connecting the two points, replacing the original path from point 1 to point q- 1 path; Step 5. Determine whether the starting point is the target point; if so, the smoothing process ends; if not, the path point q- 1 as a new starting point and repeat steps 1 to 5.

[0014] Through the above technical solutions, it can be seen that compared with the prior art, the present invention discloses an obstacle avoidance mobile charging scheduling algorithm based on the Fresnel model, which establishes a wireless rechargeable sensor network model and combines the improved The algorithm dynamically plans the charging path. Specifically, it uses the Fresnel diffraction model to divide the charging signal into enhanced area, normal area and weakened area to optimize the charging power. The initial charging path is generated using simulated annealing. When obstacles are detected, the starting point and heuristic function are dynamically updated to correct the path in real time. Path smoothing is also introduced to reduce redundant inflection points and lower mobile energy consumption. This invention significantly improves charging efficiency and network lifecycle. By leveraging obstacle signal enhancement rather than simply avoiding them, it achieves a balance between high charging efficiency and low energy consumption. Dynamic obstacle avoidance and local replanning capabilities effectively adapt to real-time changes in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A flow chart of the method provided by the present invention; Figure 2 This is a model diagram of a wireless rechargeable sensor network; Figure 3 Schematic diagram of the algorithm of the present invention; Figure 4 This is a diagram of the path smoothing steps; Figure 5 This is a schematic diagram of the Fresnel zone; Figure 6 Schematic diagram of the tangency between the first Fresnel zone (FFZ) and the obstacle; Figure 7 Schematic diagram of the charging signal impact zones at different charging positions of a mobile charger (MC); Figure 8 This is the CSOF charging path simulation diagram of the algorithm of the present invention; Figure 9 A charging utility trend chart comparing different algorithms. DETAILED DESCRIPTION

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0017] like Figure 1 and Figure 3 As shown, the embodiment of the present invention proposes an obstacle avoidance mobile charging scheduling algorithm based on the Fresnel model, including: S1. Establish a wireless rechargeable sensor network model based on sensor nodes, mobile chargers, and obstacle locations, including: S11. Construct a two-dimensional network area, which contains multiple fixed known obstacles. Sensor nodes are randomly distributed in the obstacle-free area of ​​the two-dimensional network area, and the base station is deployed in the center of the two-dimensional network area.

[0018] In this embodiment, various parameters of the sensor node (SN) and the mobile charger (MC) are obtained, as well as the location of obstacles, to establish a wireless rechargeable sensor network model. Figure 2 As shown. Multiple three-dimensional static known and fixed obstacles are distributed in L × L Temporary unknown obstacles may also appear in the two-dimensional network area. sensor nodes Randomly distributed in the obstacle-free area. Each sensor node is equipped with a rechargeable battery (capacity ) and a wireless energy receiver. Correspondingly, the MC carries an energy transmitter coil to wirelessly transmit energy to the sensor nodes. The base station (BS) is deployed at the center of the network to receive and process data collected by the sensors and provide fast and efficient energy recharge services to the MC.

[0019] S12. In the two-dimensional network area, construct n concentric ellipses corresponding to n Fresnel zones with the position coordinates of the mobile charger and sensor nodes as foci.

[0020] like Figure 5 As shown in the figure, the Fresnel zone is a concentric ellipse with the MC and the sensor node as the focus. , the receiving end is , the wavelength of the charging signal is , then An ellipse can be represented as: ; The First Fresnel Zone (FFZ) is the main contributing area for charging signal propagation and has a much greater impact on energy transmission than the external area. Figure 5 , at the boundary of FFZ: , then the radius of the FFZ ,because ,so , ,and ,therefore, ;No. n The radius of the Fresnel zone is: .

[0021] S13. Based on the Fresnel zones, construct the charging signal impact areas of different charging positions of the mobile charger and divide them into an enhanced zone, a normal zone, and a weakened zone. The enhanced zone, normal zone, and weakened zone are on concentric circles drawn with the sensor node as the center. Among them, the obstacle position is used as a reference circle. The boundaries of the enhanced zone and the weakened zone are determined based on the boundary of the first Fresnel zone and the reference circle. The boundaries of the enhanced zone and the normal zone are determined based on the boundary of half of the second Fresnel zone and the reference circle.

[0022] Specifically, in Figure 5 In the WRSN, adjacent Fresnel zones have opposite effects on energy transmission in the FFZ: odd-numbered Fresnel zones have a positive effect on energy transmission, while even-numbered Fresnel zones have a negative effect. If an obstacle is placed in the second Fresnel zone, blocking the counteractive wireless charging signal, the total charging signal will be significantly enhanced. In WRSNs, the sensor nodes (focal point) and obstacles are fixed, while the MC (the other focal point) is schedulable. By adjusting the position of the MC so that the obstacle is in the second Fresnel zone, the obstacle can be used to enhance the charging signal. At this point, the MC charging scheduling problem is transformed into: given a set of fixed sensors and obstacles, how to determine the optimal charging position of the MC, so as to further improve the charging power with the help of the obstacle; In order to solve the above problems, the Fresnel zone is divided into enhanced zone, normal zone and weakened zone, such as Figure 7 The enhanced / weakened area refers to the area where the MC charging signal is enhanced / weakened due to the influence of obstacles, and the normal area refers to the area where the MC signal propagation is not affected by obstacles; First, the boundaries between the charging signal enhancement and reduction zones are constructed. Charging signal enhancement is most effective when the obstacle is located at the edge of the FFZ (i.e., primarily covering the second Fresnel zone). However, since the FFZ is the primary propagation area for charging signals, the charging signal will be significantly reduced if an obstacle blocks the FFZ. Therefore, the intersection of the FFZ and the obstacle is used as the boundary between the enhancement and reduction zones.

[0023] like Figure 6As shown, it can be abstracted as a mathematical model: given a fixed circle (i.e. the circumscribed circle of the obstacle grid) and one focus of the ellipse (i.e. the sensor node), how to determine the position of the other focus (i.e. the MC) so that the circle is tangent to the ellipse. Figure 6 In the case of a circular obstacle with a sensor coordinate of and a radius of , the obstacle coordinate is and the tangent point coordinate is , then the optimal position of the MC when the obstacle is tangent to the FFZ is represented as follows: .

[0024] Secondly, the boundary of the charging signal enhancement zone and the normal zone is constructed. In the process of the obstacle gradually moving away from the FFZ boundary, if the obstacle continues to move away after blocking half of the second Fresnel zone, the blocking area of the second Fresnel zone by the obstacle gradually decreases, and the signal enhancement effect also gradually weakens. At this time, the obstacle is not on the propagation path of the charging signal, and this part of the area will not be affected by the obstacle, which is called the signal normal zone. When the MC is located at the boundary of the enhancement zone and the normal zone, only the third formula of the above formula is replaced by .

[0025] The difference between the process of determining the boundary of the enhancement zone and the weakening zone is that represents that the obstacle blocks half of the second Fresnel zone.

[0026] Figure 7 As shown, the charging signal influence partition of the MC at different charging positions. Therefore, in order to maximize the charging utility of the network, the MC transmits energy in the charging signal enhancement zone (green zone), or tries to be located in the normal zone to avoid the MC being located in the weakening zone to supplement energy for the sensor nodes.

[0027] In different charging signal influence areas, the i-th sensor node s i receives charging power l i at the i-th candidate charging position p ( l i , s i ) as follows: ; In the formula, , are constants, , ; α is a hardware influence parameter; β is an environmental influence parameter; is a sensor node​s i and candidate charging locations l i The distance between them.

[0028] S2. In the wireless rechargeable sensor network model, a charging optimization objective function is constructed based on the effective energy received by the sensor nodes from the mobile charger, and the charging plan of the mobile charger is planned with the limited energy of the mobile charger as the constraint.

[0029] In wireless rechargeable sensor networks, increased charging utility is equivalent to additional energy for sensor nodes, delaying their re-issuance of charging requests and increasing the revenue generated by a single charging cycle of the MC. Improving charging utility is crucial for reducing energy costs and maintaining network operation. Therefore, this embodiment, based on the Fresnel model, optimizes the total energy (i.e., effective energy) received by sensor nodes from the MC, subject to the MC's energy constraints, to improve network charging utility and thereby extend the network's lifecycle.

[0030] Specifically, the charging optimization objective function is as follows: ; Where, U i For sensor nodes s i At the candidate charging location l i The effective energy received from the mobile charger is s i After full charge, the excess energy transmitted by the MC will be wasted, so , is the rechargeable battery capacity of the sensor node, Mobile charger to sensor nodes s i The energy transmitted, , For mobile chargers at candidate charging locations l i Charging time; Charging time Need to be controlled Inside, express Moment sensor node Remaining rechargeable battery capacity.

[0031] The limited energy of the mobile charger is used as a constraint, including: ; Where M is the number of sensor nodes; Maximum energy available for wireless charging for the mobile charger; e i Initial charging path length for the mobile charger at candidate charging locations l i Charging area for the sensor node s i Transmitted energy; Initial charging path length for the mobile charger, Battery energy threshold for the mobile charger available for movement E move Maximum driving distance for the mobile charger under driving; Set of candidate charging locations; Set of to-be-charged locations.

[0032] S3. Discretize the charging area of the mobile charger for the sensor node, and use the algorithm to obtain the charging sequence and sub-path length of the to-be-charged node, including: S31. For each sensor node, draw concentric circles with the sensor node as the center, and draw R concentric circles with a radius of , so that the difference in charging power between adjacent concentric circles does not exceed the threshold , and the charging power of the same ring is equal, and each ring corresponds to a charging power layer; the radius of the j first concentric circle is: ; S32. The maximum and minimum charging power are respectively: , , and D is the maximum effective charging coverage distance of the mobile charger; the minimum charging power in each charging power layer is taken as the charging power of the entire charging power layer.

[0033] S33. Abstract the known obstacles closest to the sensor node to a grid, and take the circumscribed circle of the grid as the reference circle.

[0034] S34. Based on the charging signal influence area division process in S13, determine the enhanced area of the charging power layer according to the reference circle and the Fresnel zone.

[0035] In this embodiment, if multiple obstacles appear in the perception area of the sensor node, the obstacle closest to the node is selected as the charging signal influencing factor, and the Fresnel signal enhanced area is constructed based on this, and the influence of other obstacles is ignored.

[0036] S34. According to An algorithm is used to traverse all possible mobile charger position points in the discretized charging area to maximize the charging effective amount, and the charging sequence and sub-path length of the to-be-charged node are obtained.

[0037] S4. Design a charging position optimization target, and determine the optimal charging position of each sensor node according to the charging position optimization target.

[0038] A greedy strategy is used to determine the charging position of the MC from all candidate charging positions in the first charging power layer signal enhancement area of the to-be-charged sensor node. First, since the charging power decays with the increase of the charging distance, the optimization target of improving the charging utility and reducing the mobile consumption is used to determine the charging position, and the objective function of the charging position optimization target is defined as follows: ; Wherein, is the distance between the mobile charger and the next candidate charging position in the charging sequence of the to-be-charged node when the mobile charger is located at the candidate charging position l i ; s i received charging power; is the distance between the mobile charger and the next candidate charging position in the charging sequence of the to-be-charged node when the mobile charger is located at the candidate charging position l i ; is the weight coefficient of balancing the charging power and the mobile cost.

[0039] Each sensor node determines the corresponding optimal charging position from all candidate charging positions in the first charging power layer signal enhancement area according to the objective function of the charging position optimization target, and outputs the optimal charging position of all sensor nodes to form the to-be-charged position set .

[0040] The specific steps are as follows: S41. Input the positions of the to-be-charged sensor nodes, the candidate charging positions and their charging powers, the charging sequence of the to-be-charged nodes and their sub-path lengths; S42. Determine whether there is a candidate charging position located in the signal enhancement area of the to-be-charged sensor node , and calculate the objective function according to the formula ; S43. If the objective function is larger, update to the charging position of the to-be-charged sensor node, and if it is not larger, keep the original position; S44. Output the optimal charging position of all sensor nodes to obtain the to-be-charged position set .

[0041] S5. Based on the optimal charging position, the initial charging path is obtained with the goal of minimizing the number of dead nodes; the mobile charger charges along the initial charging path according to the charging plan. If an unknown obstacle is detected during the movement, the improved The algorithm corrects the initial charging path.

[0042] The planning of the obstacle avoidance charging path in this embodiment is divided into offline global planning and online dynamic adjustment.

[0043] First, based on the optimal charging location, with the goal of minimizing the number of dead nodes, the " Generate an initial global charging path.

[0044] The objective function is: , where Z represents the network node mortality rate, Indicates the number of dead nodes in the network.

[0045] Then, MC moves along the initial planned path and relies on on-board sensors to monitor the network environment in real time during the charging process. When an obstacle is detected, the local planner is triggered and the improved The algorithm updates the local path cost and corrects the initial path.

[0046] In the improved In the algorithm, the heuristic function Starting point The current position is updated in real time, and the priority queue is adjusted accordingly. When a new obstacle is detected and the path is replanned, the path segment near the current position is focused on. rhs ( s ) represents the minimum value of the child node g value prediction based on the parent node (in the reverse search), as follows: ; in, succ ( s ) represents the current node s The set of all child nodes of Indicates the current node s The child nodes of Indicates the current node s arrive The cost of moving express The cost to the target point, The actual distance of each move, the initial value is 0; in traditional In, use k The value ranks the priority of the nodes, k The smaller the value, the higher the priority. k 1 value, if equal, continue to arrange k2 value, the formula is as follows: ; However, when MC gradually approaches the target point from the new starting point, the heuristic function value continues to decrease, and its proportion also gradually decreases, which makes it easy for multiple nodes to appear. k When the values ​​are the same, more nodes are expanded and the search efficiency is reduced. Therefore, the improvement Modify its heuristic function to balance its k The proportion of the value is expressed as follows: ; ; Where, x , y Indicates the current node s The horizontal and vertical coordinates of , Indicates the starting point The horizontal and vertical coordinates of , Indicates the target node The horizontal and vertical coordinates of is the weight function to balance the traditional The proportion of the algorithm heuristic function in the k value.

[0047] Using improved The algorithm updates the local path cost and corrects the initial path, including: Generate the current node s Child nodes of , based on the current node s To the target node s goal The actual cost g ( s ) and from the current node s Through child nodes Arrival at the target node s goal The estimated minimum cost rhs ( s ), calculate the k value in combination with the improved heuristic function, arrange the node priorities according to the k value, and determine the first revised node according to the node priority; Updating the first revised node as the current node, and repeating the step of determining the revised node until the current node coincides with the target node; A local obstacle avoidance path is generated based on all the correction nodes to correct the initial charging path.

[0048] Further, the step of determining the modified node according to the node priority comprises: arranging the k values in ascending order, arranging the k1 values preferentially, arranging the k2 values in the case of the same k1 values, and the modified node is the candidate node corresponding to the k value ranked first, and the candidate node includes the child node generated based on the current node.

[0049] Preferably, the improved algorithm shortens the path distance by adding smoothing processing, and the reference Figure 4 includes: Step 1. Label the n path points on the modified charging path, sequentially labeled as 1, 2,..., n from the starting point to the target point; Step 2. Connect path point 1 and path point 2, and determine whether the connecting line of the two points passes through the obstacle; Step 3. Check the next path point until the connecting line between the current path point q q is an integer, q n and path point 1 passes through the obstacle; Step 4. Connect path point 1 and path point q- 1 as endpoints, and replace the original path from path point 1 to path point q- 1; Step 5. Determine whether the starting point is the target point; if yes, the smoothing processing is ended; if not, path point q- 1 is taken as a new starting point, and steps 1-5 are repeated.

[0050] Figure 8 is the charging path simulation diagram of the algorithm (CSOF) of this embodiment, in which the black grid represents the static obstacle known from the map, and the yellow grid represents the temporary set obstacle unknown from the map after the MC starts charging. In order to clearly show the Fresnel modeling process and the charging position of the MC, the Fresnel model partition diagram is overlaid in the simulation diagram in the later stage, wherein the green area represents the Fresnel signal enhancement area, and the gray circle represents the obstacle substituted in the Fresnel modeling. As can be seen from the diagram, the circumscribed circle of the grid obstacle in the sensing area of s 4, s 7 and s 6 is substituted into the Fresnel modeling, and the MC accurately drives to the Fresnel signal enhancement area of s 4, s 7 and s 6 for charging. At the same time, the CSOF also makes real-time obstacle avoidance response to the unknown obstacle temporarily appearing in the network map, and dynamically adjusts the charging path.

[0051] Figure 9 ​​To compare the charging utility trend of the algorithms, the embodiment defines three algorithms: a Maximum charging Utility Priority (MUP) algorithm, an Ignoring Positive effects of Obstacles (IPO) algorithm, and a Random (RAN) algorithm. The MUP is a charging utility greedy algorithm, which ignores the cost of changing the charging position when determining the charging position. The IPO only focuses on the attenuation effect caused by obstacles and ignores the charging signal enhancement. The RAN randomly selects a charging position that is not covered by obstacles. The graph mainly investigates the impact of the MC energy budget on the charging utility under different algorithms. It can be seen from the graph that, as the energy budget increases, the energy of the MC can charge more sensor nodes, and the charging utility of the CSOF and other algorithms all shows an upward trend. Figure 9 Specifically, during the process of increasing the energy budget from 1000 J to 4000 J, the charging utility of the CSOF always leads other algorithms, and increases by 44.8%, and tends to be stable when the energy budget is close to 3000 J. Since the MUP is a charging utility greedy algorithm, it preferentially selects the charging position with the maximum charging utility and ignores the moving cost of changing the charging position. When the energy budget of the MC grows enough for charging and moving consumption (such as 4000 J in the graph), the disadvantage caused by ignoring the moving cost is compensated, and the charging utility is basically the same as the CSOF algorithm proposed in the embodiment when the energy budget is 4000 J. This phenomenon further illustrates that the CSOF can better balance the charging utility and the moving cost of the MC when the energy budget of the MC is low, and is more suitable for the network environment with limited energy of the MC.

[0052] In summary, the embodiment proposes a CSOF algorithm for obstacle avoidance and mobile charging scheduling based on the Fresnel model. The algorithm constructs a Fresnel charging model using obstacles, effectively improves the charging utility, and realizes global planning of the MC charging path and obstacle avoidance for unknown obstacles.

[0053] The embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The same or similar parts of each embodiment can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the related parts can be referred to the method part.

[0054] The foregoing description of the disclosed embodiments enables a person skilled in the art to make or use the application. Modifications of these embodiments will occur to persons of skill in the art, and that the appended claims are intended to cover all such modifications that do not depart from the true spirit and scope of the application. Therefore, the application is not limited to the embodiments shown but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An obstacle avoidance mobile charging scheduling algorithm based on Fresnel model, characterized in that: include: A wireless rechargeable sensor network model is established based on sensor nodes, mobile chargers and obstacle locations; In the wireless rechargeable sensor network model, a charging optimization objective function is constructed based on the effective energy received by the sensor nodes from the mobile charger, and the charging plan of the mobile charger is planned with the limited energy of the mobile charger as the constraint. Discretize the charging area of ​​the mobile charger to the sensor node and maximize the effective amount of charging. The algorithm obtains the charging order and subpath length of the nodes to be charged; Design the charging position optimization target and determine the optimal charging position for each sensor node based on the charging position optimization target; According to the optimal charging position, the initial charging path is obtained with the goal of minimizing the number of dead nodes; The mobile charger charges according to the charging plan along the initial charging path. If an unknown obstacle is detected during the movement, the improved The algorithm corrects the initial charging path.

2. The obstacle avoidance mobile charging scheduling algorithm based on the Fresnel model according to claim 1 is characterized in that: A wireless rechargeable sensor network model is established based on the locations of sensor nodes, mobile chargers, and obstacles, including: Construct a two-dimensional network area, which contains multiple fixed known obstacles. Sensor nodes are randomly distributed in the obstacle-free area of ​​the two-dimensional network area, and the base station is deployed in the center of the two-dimensional network area. In the two-dimensional network area, n concentric ellipses corresponding to n Fresnel zones are constructed with the position coordinates of the mobile charger and sensor nodes as the focus; Based on the Fresnel zones, the charging signal impact area at different charging positions of the mobile charger is constructed and divided into an enhanced zone, a normal zone, and a weakened zone. The enhanced zone, normal zone, and weakened zone are located on concentric circles drawn with the sensor node as the center. The boundary of the enhanced zone and the weakened zone are determined by the boundary of the first Fresnel zone and the reference circle, and the boundary of the enhanced zone and the normal zone are determined by the boundary of half of the second Fresnel zone and the reference circle. In the area affected by different charging signals, the i-th sensor node s i At the i-th candidate charging location l i Charging power p ( l i , s i )as follows: ； Where, is a constant greater than 1; is a constant less than 1; α Parameters that affect the hardware; β is the environmental impact parameter; For sensor nodes s i and candidate charging locations l i The distance between them.

3. The obstacle avoidance mobile charging scheduling algorithm based on the Fresnel model according to claim 2 is characterized in that: The charging optimization objective function is as follows: ； Where, U i For sensor nodes s i At the candidate charging location l i The effective energy received from the mobile charger, , is the rechargeable battery capacity of the sensor node, Mobile charger to sensor nodes s i The energy transmitted, , For mobile chargers at candidate charging locations l i Charging time; The limited energy of the mobile charger is used as a constraint, including: ； Where M is the number of sensor nodes; The maximum energy that a mobile charger can use for wireless charging; e i For mobile chargers at candidate charging locations l i To sensor nodes s i Energy sent; is the initial charging path length of the mobile charger, The battery energy threshold that can be used for mobile chargers E move Maximum driving distance of the mobile charger under driving; is the set of candidate charging locations; is the set of locations to be charged.

4. The obstacle avoidance mobile charging scheduling algorithm based on the Fresnel model according to claim 2 is characterized in that: Discretize the charging area of ​​the mobile charger to the sensor node and maximize the effective amount of charging. The algorithm obtains the charging order and subpath length of the nodes to be charged, including: For each sensor node, s i The concentric circles drawn with the center as R The radius is concentric circles, so that the charging power difference between adjacent concentric circles does not exceed the threshold , the charging power of the same ring is equal, and each ring is equivalent to a charging power layer; j The radius of the concentric circles is: ; The minimum charging power in each charging power layer As the charging power of the entire charging power layer; where, , D is the maximum effective charging coverage distance of the mobile charger; Distance sensor nodes s i The nearest known obstacle is abstracted as a grid, and the circumscribed circle of the grid is used as the reference circle; Determine the enhanced area of ​​the charging power layer based on the reference circle and the Fresnel zone; according to The algorithm traverses all possible mobile charger locations in the discretized charging area, with the goal of maximizing the effective amount of charging, and obtains the charging order and sub-path length of the nodes to be charged.

5. The obstacle avoidance mobile charging scheduling algorithm based on the Fresnel model according to claim 4 is characterized in that: The objective function of the charging location optimization objective is as follows: ； For mobile chargers located at candidate charging locations l i Time sensor node s i Received charging power; For mobile chargers located at candidate charging locations l i The distance from the next candidate charging position in the charging sequence of the node to be charged, The weight coefficient for balancing charging power and mobility cost; Each sensor node determines the corresponding optimal charging position from all candidate charging positions in the signal enhancement area of ​​the first charging power layer according to the objective function of the charging position optimization target, and outputs the optimal charging positions of all sensor nodes to form a set of charging positions. .

6. The obstacle avoidance mobile charging scheduling algorithm based on the Fresnel model according to claim 1 is characterized in that: According to the optimal charging position, with the goal of minimizing the number of dead nodes, use and SA generate the initial charging path.

7. The obstacle avoidance mobile charging scheduling algorithm based on the Fresnel model according to claim 5 is characterized in that: If an unknown obstacle is detected during movement, the improved The algorithm modifies the initial charging path, including: Improve the heuristic function and change the starting point of the improved heuristic function Real-time updates to the current location of the mobile charger; The improved heuristic function is as follows: ； ； Where, x , y Indicates the current node s The horizontal and vertical coordinates of , Indicates the starting point The horizontal and vertical coordinates of , Indicates the target node The horizontal and vertical coordinates of is the weight function; Generate the current node s Child nodes of , based on the current node s To the target node The actual cost g ( s ) and from the current node s Through child nodes Arrival at the target node The estimated minimum cost rhs ( s ), calculate the k value in combination with the improved heuristic function, arrange the node priorities according to the k value, and determine the first revised node according to the node priority; Updating the first revised node as the current node, and repeating the step of determining the revised node until the current node coincides with the target node; Generate a local obstacle avoidance path based on all the correction nodes and correct the initial charging path; The calculation formula of k value is as follows: ； ； succ ( s ) represents the current node s The set of all child nodes of Indicates the current node s To child node The cost of moving Represents a child node The cost to the target point, is the actual distance moved each time; Arrange the k values ​​in ascending order, prioritize the k1 value, and continue to arrange the k2 value if the k1 values ​​are the same. The corrected node is the candidate node corresponding to the k value ranked first, and the candidate node includes the child node generated based on the current node.

8. The obstacle avoidance mobile charging scheduling algorithm based on the Fresnel model according to claim 7 is characterized in that: Improved The algorithm shortens the path distance by adding smoothing processes, including: Step 1. Mark n points on the corrected charging path, from the starting point to the target point, as 1, 2, ..., n; Step 2. Connect path point 1 and path point 2 and determine whether the line connecting the two points passes through an obstacle; Step 3. Check the next waypoint until the current waypoint q The line connecting to waypoint 1 passes through the obstacle; Step 4. Take waypoint 1 and waypoint q- 1 is the endpoint, connecting the two points, replacing the original path from point 1 to point q- 1 path; Step 5. Determine whether the starting point is the target point; if so, the smoothing process ends; if not, the path point q- 1 as a new starting point and repeat steps 1 to 5.

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