Unmanned aerial vehicle energy-saving trajectory design method based on wireless charging sensor network

Abstract

The invention discloses an unmanned aerial vehicle energy-saving trajectory design method based on a wireless charging sensor network, and solves the problems of low charging efficiency and serious energy waste caused by difficulty in accurately deploying a charger in the prior art. The method comprises the following steps: determining a plurality of rechargeable sensors in a wireless rechargeable sensor network in a two-dimensional geographic area to obtain a rechargeable sensor set, calculating to obtain a hovering point position based on a radius expansion search algorithm according to the maximum coverage area of the rechargeable sensors, and determining the position of the hovering point according to the hovering point position. Determining a corresponding coverage rechargeable sensor set, and adding the hovering point position to a hovering point set; deleting the coverage rechargeable sensor set from the rechargeable sensor sets to obtain a first rechargeable sensor set; and outputting a hovering point set until the first rechargeable sensor set is empty, thereby obtaining an energy-saving trajectory of the unmanned aerial vehicle. The radius is prevented from exceeding the maximum charging range, a better charging position can be found, and the charging efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless charging rechargeable sensor networks, and in particular to an energy-saving trajectory design method for unmanned aerial vehicles based on a wireless charging sensor network. Background Art

[0002] At present, with the accelerating process of digital city construction, in order to achieve efficient collection and analysis of various urban information, a large number of rechargeable sensors are widely deployed in every corner of the city for many fields such as environmental monitoring, traffic flow monitoring, and intelligent security. However, due to their limited volume and functional design, the battery capacity carried by these rechargeable sensors is very limited. If the energy cannot be replenished in time, the rechargeable sensors will stop working due to power exhaustion, which will then lead to the interruption of data collection and affect the stable operation of the urban intelligent management system. In the complex urban traffic environment, using traditional cars to charge rechargeable sensors is extremely prone to traffic jams, which will not only prolong the charging cycle but may also cause some rechargeable sensors to fail due to long-term lack of charging. Therefore, unmanned aerial vehicles with wireless charging functions have become an ideal choice for charging rechargeable sensors in wireless rechargeable sensor networks (WRSNs). In the field of WRSNs, charger deployment, path planning and scheduling optimization, and unmanned aerial vehicle trajectory optimization play a key role in improving network performance and extending the service life of rechargeable sensor nodes.

[0003] In the prior art, the minimum enclosing circle of all rechargeable sensors in the wireless charging network is calculated to obtain a set of minimum enclosing circles of all rechargeable sensors. Among many sets of minimum enclosing circles, the set with the most charging requirements is selected as the charging set each time. After determining the charging set, the trajectory of the unmanned aerial vehicle is designed based on these sets. This algorithm aims to minimize the wireless charging energy by optimizing the selection of the charging area.

[0004] In the prior art, for the problem of minimizing the energy cost in a two-dimensional planar wireless rechargeable sensor network, a solution combining a geometric search algorithm (PCES) and an edge selection algorithm (ESA) is proposed. This method first calculates the starting position using the Graham-Scan algorithm, and then calculates the best SP position within the minimum convex hull based on the starting position. Further, by iteratively applying ESA multiple times to identify multiple SPs and plan the optimal charging path, not only the charging efficiency is improved, but also the movement cost is reduced. Finally, this method effectively reduces the total energy consumption during the entire charging cycle while ensuring that the charging requirements of each rechargeable sensor are met, providing an efficient strategy for optimizing the energy management of wireless rechargeable sensor networks.

[0005] However, most of the existing technologies design wireless charging algorithms based on two-dimensional scenarios, while the actual wireless rechargeable network is a three-dimensional space problem. In a complex urban environment, it is difficult to accurately deploy chargers, resulting in low charging efficiency and serious energy waste. When the existing radius expansion search algorithm processes non-linear functions, there is a problem that the radius exceeds the maximum charging range, which makes the calculated charging position not the best, easily missing rechargeable sensors and affecting the comprehensiveness and effectiveness of charging. Moreover, this algorithm is also based on a two-dimensional plane and cannot adapt to a three-dimensional complex environment. Summary of the Invention

[0006] By providing a method for designing an energy-saving trajectory of an unmanned aerial vehicle based on a wireless charging sensor network, the present invention solves the problems in the prior art that in a complex urban environment, it is difficult to accurately deploy chargers, resulting in low charging efficiency and serious energy waste, realizes avoiding the radius exceeding the maximum charging range, can find a better charging position, and improves the charging efficiency.

[0007] The present invention provides a method for designing an energy-saving trajectory of an unmanned aerial vehicle based on a wireless charging sensor network, and the method includes:

[0008] S101, determining a plurality of rechargeable sensors in a wireless rechargeable sensor network within a two-dimensional geographical area to obtain a rechargeable sensor set;

[0009] S102, for each unmanned aerial vehicle, based on the radius expansion search algorithm, calculating a hovering point position according to the maximum coverage area of the rechargeable sensors, then determining a set of covered rechargeable sensors corresponding to the unmanned aerial vehicle according to the hovering point position, and adding the hovering point position to a hovering point set;

[0010] S103, deleting the set of covered rechargeable sensors from the rechargeable sensor set to obtain a first rechargeable sensor set;

[0011] S104, repeatedly executing S102 to S103 until the first rechargeable sensor set is empty, and outputting the hovering point set;

[0012] S105, obtaining an energy-saving trajectory of the unmanned aerial vehicle by using the LKH algorithm according to the hovering point set.

[0013] In a possible implementation manner, the step of for each unmanned aerial vehicle, based on the radius expansion search algorithm, calculating a hovering point position according to the maximum coverage area of the rechargeable sensors, then determining a set of covered rechargeable sensors corresponding to the unmanned aerial vehicle according to the hovering point position, and adding the hovering point position to a hovering point set includes:

[0014] Use the convex hull detection algorithm to screen the rechargeable sensor set to obtain the boundary rechargeable sensor set;

[0015] According to each boundary rechargeable sensor in the boundary rechargeable sensor set, use the radius expansion limit search algorithm to determine the maximum coverage rechargeable sensor set;

[0016] Calculate the maximum distance between two rechargeable sensors based on the maximum coverage rechargeable sensor set, and then calculate the initial hovering point position based on the maximum distance;

[0017] Determine the second rechargeable sensor set according to the maximum distance, judge the intersection points of the rechargeable sensors in the second rechargeable sensor set, and update the maximum distance and the initial hovering point position according to each judgment result to obtain the updated hovering point position until all the rechargeable sensors in the second rechargeable sensor set are judged, and output the finally updated hovering point position; wherein, the second rechargeable sensor set is a subset of the maximum coverage rechargeable sensor set;

[0018] Add the finally updated hovering point position to the hovering point set.

[0019] In a possible implementation manner, the step of using the convex hull detection algorithm to screen the rechargeable sensor set to obtain the boundary rechargeable sensor set includes:

[0020] Determine the two-dimensional coordinates of each rechargeable sensor in the rechargeable sensor set, and sort the two-dimensional coordinates in ascending order to obtain the sorted rechargeable sensor set;

[0021] Identify the rechargeable sensor corresponding to the convex hull starting point in the sorted rechargeable sensor set;

[0022] Based on the polar angle calculation formula, calculate the polar angles of the remaining rechargeable sensors other than the convex hull starting point relative to the rechargeable sensor corresponding to the convex hull starting point respectively, and then sort the polar angles in ascending order to obtain the polar angle sorted rechargeable sensor set;

[0023] Construct a lower convex hull stack according to the polar angle sorted rechargeable sensor set, and the elements in the lower convex hull stack are the boundary rechargeable sensor set.

[0024] In a possible implementation manner, the step of calculating the maximum distance based on the maximum coverage rechargeable sensor set and then calculating the initial hovering point position based on the maximum distance includes:

[0025] Combine the rechargeable sensors in the maximum coverage rechargeable sensor set in pairs to obtain multiple pairs of combined rechargeable sensors;

[0026] Calculate the Euclidean distance between the two rechargeable sensors in each rechargeable sensor pair respectively to obtain a distance set;

[0027] Filter out the maximum distance in the distance set and determine the first rechargeable sensor pair corresponding to the maximum distance;

[0028] Draw circles with the coordinates of the first rechargeable sensor and the second rechargeable sensor in the first rechargeable sensor pair as the centers and the maximum distance as the radius respectively, to obtain the two-dimensional coordinates of the first tangent points of the two circles, and use the two-dimensional coordinates as the two-dimensional coordinates of the initial hovering point position;

[0029] According to the coordinates of the first rechargeable sensor and the second rechargeable sensor and the drone charging requirement, use the distance calculation formula to calculate the first distance from the drone to the first rechargeable sensor and the second distance from the drone to the second rechargeable sensor, and use the height calculation formula to calculate the initial hovering height according to the first distance and the second distance;

[0030] Obtain the initial hovering point position according to the initial hovering height and the two-dimensional coordinates of the initial hovering point position.

[0031] In a possible implementation, the distance calculation formula is expressed as:

[0032]

[0033] where, beita represents the first constant; m represents the independent variable positively correlated with the distance d; d max represents the maximum transmission distance of the drone; m represents the expansion radius; C l represents the charging requirement of the l-th rechargeable sensor.

[0034] In a possible implementation, the height calculation formula is expressed as:

[0035]

[0036] where, represents the first distance; represents the second distance; H tmp represents the initial hovering height.

[0037] In a possible implementation, the updating the maximum distance and the initial hovering point position according to each judgment result to obtain the updated hovering point position includes:

[0038] Calculate the third distance d between each rechargeable sensor in the second rechargeable sensor set and the first tangent point respectively ok;

[0039] Calculate the rechargeable sensor coverage distance d corresponding to each rechargeable sensor in the second set of rechargeable sensors k ;

[0040] Compare the third distance d corresponding to each rechargeable sensor in the second set of rechargeable sensors ok with the rechargeable sensor coverage distance d k , and update the initial hovering point position according to the comparison result to obtain the updated hovering point position.

[0041] In a possible implementation, the updating the initial hovering point position according to the comparison result to obtain the updated hovering point position includes:

[0042] If the third distance d ok is greater than or equal to the rechargeable sensor coverage distance d k , then determine that within the rechargeable sensor coverage distance d k , there is no intersection of three circles, and do not update the first tangent point;

[0043] If the third distance d ok is less than the rechargeable sensor coverage distance d k , then the first tangent point is not within the rechargeable sensor coverage distance d k , there is an intersection of three circles, and calculate the second tangent point when the three circles intersect;

[0044] Among the first pair of rechargeable sensors and the kth rechargeable sensor corresponding to the rechargeable sensor coverage distance d k , delete the rechargeable sensor with the smallest charging requirement, and use the remaining rechargeable sensors as the first pair of rechargeable sensors;

[0045] Calculate the updated hovering point position according to the first pair of rechargeable sensors and the second tangent point.

[0046] In a possible implementation, obtaining the energy-saving trajectory of the UAV using the LKH algorithm according to the set of hovering points includes:

[0047] Convert the position of each hovering point in the set of hovering points into three-dimensional coordinates to obtain a set of three-dimensional coordinates, and then use the LKH algorithm according to the set of three-dimensional coordinates to obtain an initial access sequence;

[0048] Fill in the three-dimensional coordinates with a Z-axis coordinate of 0 in each three-dimensional coordinate in the initial access sequence to obtain a filled set of three-dimensional coordinates;

[0049] Use the filled set of three-dimensional coordinates as the energy-saving trajectory of the UAV.

[0050] One or more technical solutions provided in the present invention have at least the following technical effects or advantages:

[0051] In the present invention, through the radius expansion search algorithm, the maximum coverage area of rechargeable sensors is considered during the search process to avoid exceeding the maximum charging range, and the optimal charging position can be found, so the charging efficiency is higher than that of the radius expansion search algorithm; the traveling salesman problem solver (LKH) algorithm is used to determine the access order of hovering points. The three-dimensional coordinates of the hovering point set are converted into distances and then input into the LKH algorithm to obtain an optimized access order of hovering points, improving the charging efficiency and reducing unnecessary energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a flowchart of the steps of the method for designing an energy-saving trajectory of an unmanned aerial vehicle for a wireless charging sensor network provided by an embodiment of the present invention;

[0053] Figure 2 It is a schematic diagram of an urban wireless rechargeable air-to-ground network provided by an embodiment of the present invention;

[0054] Figure 3 It is a schematic diagram of a set of boundary rechargeable sensors provided by an embodiment of the present invention;

[0055] Figure 4 It is a schematic diagram of determining a covered rechargeable sensor group and a set of possibly covered rechargeable sensors provided by an embodiment of the present invention;

[0056] Figure 5 It is a schematic diagram of the formation of intersections on a plane by the radius expansion search limit algorithm provided by an embodiment of the present invention;

[0057] Figure 6 It is a schematic diagram of the charging trajectory of an unmanned aerial vehicle provided by an embodiment of the present invention;

[0058] Figure 7 It is a schematic diagram of the simulation effect provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0059] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0060] The present invention provides a method for designing an energy-saving trajectory of an unmanned aerial vehicle based on a wireless charging sensor network. Refer to Figure 1 , the method includes:

[0061] S101. Determine multiple rechargeable sensors in a wireless rechargeable sensor network within a two-dimensional geographical area to obtain a set of rechargeable sensors.

[0062] Exemplarily, a city wireless rechargeable air-to-ground network composed of charging piles, mobile drones, and L rechargeable sensors {S1, S2, ……, S L} located in a two-dimensional geographical area. Refer to Figure 2 . This network is located in a three-dimensional space. Each rechargeable sensor S l has different remaining battery levels {b1, b2, ……, b l}, and the charging requirement of rechargeable sensor S l is c l .

[0063] S102. For each drone, based on the radius expansion search algorithm, calculate the hovering point position according to the maximum coverage area of the rechargeable sensors, and then determine the set of covered rechargeable sensors corresponding to the drone according to the hovering point position, and add the hovering point position to the hovering point set;

[0064] Specifically, in step S102, for each drone, based on the radius expansion search algorithm, calculate the hovering point position according to the maximum coverage area of the rechargeable sensors, and then determine the set of covered rechargeable sensors corresponding to the drone according to the hovering point position, and add the hovering point position to the hovering point set, including the following S1021 to S1025.

[0065] S1021. Use the convex hull detection algorithm to screen the set of rechargeable sensors to obtain a set of boundary rechargeable sensors;

[0066] Specifically, in step S1021, using the convex hull detection algorithm to screen the set of rechargeable sensors to obtain a set of boundary rechargeable sensors includes:

[0067] (1) Determine the two-dimensional coordinates of each rechargeable sensor in the set of rechargeable sensors, and sort the two-dimensional coordinates in ascending order to obtain a sorted set of rechargeable sensors;

[0068] (2) Identify the rechargeable sensor corresponding to the starting point of the convex hull in the sorted set of rechargeable sensors;

[0069] (3) Based on the polar angle calculation formula, calculate the polar angles of the remaining rechargeable sensors other than the starting point of the convex hull with respect to the rechargeable sensor corresponding to the starting point of the convex hull, and then sort the polar angles in ascending order to obtain a set of rechargeable sensors sorted by polar angle;

[0070] (4) Construct a lower convex hull stack based on the rechargeable sensors sorted by polar angle. The elements in the lower convex hull stack are the boundary rechargeable sensor set.

[0071] Exemplarily, the input of the convex hull detection algorithm is the two-dimensional coordinates of the rechargeable sensors, and the output of the convex hull detection algorithm is the boundary rechargeable sensor set.

[0072] Here, the specific steps are as follows: First, among the two-dimensional coordinates of all rechargeable sensors, find the rechargeable sensor with the smallest y coordinate. If there are multiple rechargeable sensors with the same y coordinate, then select the rechargeable sensor with the smallest x coordinate. This point must be a point on the convex hull and is used as the starting point P0.

[0073] Then, with the starting point P0 as the origin, calculate the polar angles of all other rechargeable sensors relative to the starting point P0 (the polar angle can be approximately represented by calculating the slope of the vector).

[0074] Next, sort these rechargeable sensors in ascending order of polar angle. If there are multiple rechargeable sensors with the same polar angle, then sort them in ascending order of their distance from P0.

[0075] Then, create a stack, push P0 and the first sorted rechargeable sensor onto the stack; traverse the sorted rechargeable sensors, that is, starting from the second sorted rechargeable sensor, add the rechargeable sensors to the stack in turn. Before adding each rechargeable sensor, check whether the top two rechargeable sensors on the stack and the current rechargeable sensor form a left turn (counterclockwise direction). If it is not a left turn, pop the top element of the stack until the left turn condition is met or there is only one element left in the stack, and then push the current rechargeable sensor onto the stack.

[0076] After traversing all rechargeable sensors, the rechargeable sensors remaining in the stack are the rechargeable sensors on the convex hull, that is, the boundary rechargeable sensor set.

[0077] S1022. Determine the maximum coverage rechargeable sensor set according to each boundary rechargeable sensor in the boundary rechargeable sensor set by using the radius expansion limit search algorithm;

[0078] Exemplarily, in the boundary rechargeable sensor set, calculate the included angle for every three rechargeable sensors in the order of the boundary rechargeable sensors, and finally find the rechargeable sensor corresponding to the smallest angle.

[0079] Then, with the rechargeable sensor as the center, determine the rechargeable sensors within a range greater than one times the recharge range of the rechargeable sensor and less than two times the recharge range as the set of potentially covered rechargeable sensors. At the same time, determine the boundary nodes within one times the recharge range (including the rechargeable sensor corresponding to the minimum angle) as the determined covered rechargeable sensor group.

[0080] The specific implementation is as follows: (1) Using the boundary order in the set of boundary rechargeable sensors, calculate the included angle for every three sensors, and find the rechargeable sensor corresponding to the minimum included angle.

[0081] (2) Use the minimum rechargeable sensor as the center, and with the maximum recharge range d of a drone max Solve to obtain the determined covered rechargeable sensor group tmp1, and then solve to obtain the set of potentially covered rechargeable sensors tmp2 with a range greater than one maximum recharge range and less than two maximum recharge ranges.

[0082] (3) Find the boundary rechargeable sensors in the determined covered rechargeable sensor group tmp1 and use them as the determined covered rechargeable sensors; use the set of potentially covered rechargeable sensors tmp2 as the set of potentially covered rechargeable sensors.

[0083] (4) Based on the determined covered rechargeable sensor group tmp1 plus one rechargeable sensor in the set of potentially covered rechargeable sensors tmp2, form the preliminary covered rechargeable sensor group. Then use the radius expansion limit search algorithm to obtain the preliminary center; based on the preliminary center, obtain the covered rechargeable sensor group. Therefore, according to the number of rechargeable sensors in the set of potentially covered rechargeable sensors tmp2, multiple covered rechargeable sensor groups can be obtained, and the largest covered rechargeable sensor set is retained as the output.

[0084] S1023. Calculate the maximum distance between two rechargeable sensors based on the largest covered rechargeable sensor set, and then calculate the initial hovering point position based on the maximum distance;

[0085] Specifically, in step S1023, calculating the maximum distance between two rechargeable sensors based on the largest covered rechargeable sensor set and then calculating the initial hovering point position includes:

[0086] (1) Combine the rechargeable sensors in the largest covered rechargeable sensor set in pairs to obtain multiple pairs of combined rechargeable sensors;

[0087] (2) Calculate the Euclidean distance between the two rechargeable sensors in each pair of rechargeable sensors respectively to obtain a distance set;

[0088] (3) Screen the maximum distance from the distance set and determine the first rechargeable sensor pair corresponding to the maximum distance;

[0089] (4) Respectively, with the coordinates of the first rechargeable sensor and the second rechargeable sensor in the first rechargeable sensor pair as the centers and the maximum distance as the radius, draw circles to obtain the two-dimensional coordinates of the first tangent points of the two circles, and use the two-dimensional coordinates as the two-dimensional coordinates of the initial hovering point position;

[0090] (5) According to the coordinates of the first rechargeable sensor and the second rechargeable sensor and the charging requirement of the drone, use the distance calculation formula to calculate the first distance of the drone from the first rechargeable sensor and the second distance of the drone from the second rechargeable sensor, and calculate the initial hovering height according to the first distance and the second distance using the height calculation formula;

[0091] Here, the distance calculation formula is expressed as:

[0092]

[0093] Among them, beita represents the first constant; m represents the independent variable that is positively correlated with the distance d and can increase the distance d by increasing the value; d max represents the maximum transmission distance of the drone; d represents the distance between the assumed l-th rechargeable sensor and the assumed drone, that is, the expansion radius. When the value of m is determined, the expansion radius of all sensors is fixed. Therefore, when the value of m is just right, a common intersection point will be formed, which means that the positions of the assumed drones of all sensors are the same; C l represents the charging requirement of the l-th rechargeable sensor.

[0094] Here, the height calculation formula is expressed as:

[0095]

[0096] Among them, represents the first distance; represents the second distance; H tmp represents the initial hovering height. (6) Add the initial height to the two-dimensional coordinates of the initial hovering point position to obtain the initial hovering point position.

[0097] S1024. Determine the second rechargeable sensor set according to the maximum distance, judge the intersection points of the rechargeable sensors in the second rechargeable sensor set, and update the maximum distance and the initial hovering point position according to each judgment result to obtain the updated hovering point position until all the rechargeable sensors in the second rechargeable sensor set are judged, and output the finally updated hovering point position; among them, the second rechargeable sensor set is a subset of the maximum coverage rechargeable sensor set;

[0098] Specifically, the maximum distance and the initial hovering point position are updated according to each judgment result to obtain the updated hovering point position, including:

[0099] (1) Calculate the third distance d between each rechargeable sensor in the second rechargeable sensor set and the first tangent point respectively ok ;

[0100] (2) Calculate the rechargeable sensor coverage distance d corresponding to each rechargeable sensor in the second rechargeable sensor set k ;

[0101] (3) Compare the third distance d corresponding to each rechargeable sensor in the second rechargeable sensor set ok and the rechargeable sensor coverage distance d k , and update the initial hovering point position according to the comparison result to obtain the updated hovering point position.

[0102] Here, according to the comparison result, the initial hovering point position is updated to obtain the updated hovering point position, including:

[0103] (3.1) If the third distance d ok is greater than or equal to the rechargeable sensor coverage distance d k , it is determined that the first tangent point is within the rechargeable sensor coverage distance d k , and there is no intersection of three circles, and the first tangent point is not updated;

[0104] (3.2) If the third distance d ok is less than the rechargeable sensor coverage distance d k , the first tangent point is not within the rechargeable sensor coverage distance d k , there is an intersection of three circles, and calculate the second tangent point when the three circles intersect; (3.3)

[0106] Among the first rechargeable sensor pair and the kth rechargeable sensor corresponding to the rechargeable sensor coverage distance d k , delete the rechargeable sensor with the smallest charging requirement, and use the remaining rechargeable sensors as the first rechargeable sensor pair;

[0107] (3.4) Calculate the updated hovering point position according to the first rechargeable sensor pair and the second tangent point.

[0108] S1025, add the finally updated hovering point position to the hovering point set.

[0109] Exemplarily, the calculation processes of the initial hovering point position and the updated hovering point position are specifically as follows.

[0110] Input: Coordinates of the maximum covering rechargeable sensor set \(S = \{s_1, s_2, \ldots, s N \}\), the charging requirements \(\{c_1, c_2, \ldots, c N \}\) corresponding to these rechargeable sensors, and constant parameters \(\{\alpha, \beta, P T , N_0, \theta (S,X) , d max \};

[0111] Output: Set of hovering points;

[0112] Specific steps:

[0113] (1) Initialize the empty set \(G = \{\}\);

[0114] (2) If there is only one rechargeable sensor in the maximum covering rechargeable sensor set \(S\), directly return the position \(O tmp (x tmp , y tmp , 0)\).

[0115] (3) For the case where the number of rechargeable sensors \(N>1\) in the rechargeable sensor set \(S\), loop to calculate the distance \(x\) when the expansion distances corresponding to a pair of rechargeable sensors \((i, j)\) (\(i\in[1, N - 1], j\in[i + 1, N]\)) intersect ij .

[0116] In each loop, for the current first pair of rechargeable sensors \((i, j)\), solve for the distance \(m\) in the two - circle intersection equation within the domain of the distance \(m ij domain , and add the obtained distance \(x ij to the set \(G\). Among them, the two - circle intersection equation is expressed as: ij

[0117]

[0118] where \(d i represents the expansion radius of the \(i\) - th rechargeable sensor; \(d j represents the expansion radius of the \(j\) - th rechargeable sensor; beita represents the first constant; \(m ij represents the value of the expansion radius \(m\) when the expansion radii of the current \(i\) - th rechargeable sensor and the \(j\) - th rechargeable sensor form a tangent; \(C i represents the charging requirement of the \(i\) - th rechargeable sensor; \(C j represents the charging requirement of the \(j\) - th rechargeable sensor; \(d ij represents the distance between the \(i\) - th rechargeable sensor and the \(j\) - th rechargeable sensor.

[0119] ​(4) Select the maximum distance from set G and denote it as Meanwhile, utilize the maximum distance m * and the coordinates and charging requirements of the first rechargeable sensor pair (i, j) that intersects below to calculate the corresponding Further determine the two-dimensional coordinates O of the first tangent point tmp (x tmp , y tmp ). Meanwhile, calculate the height-related value

[0120] (5) For other rechargeable sensors k ∈ [1, N - 2], k ≠ i, k ≠ j except for the first rechargeable sensor pair i, j corresponding to the maximum distance m * , calculate the distance d between the first tangent point O tmp and the rechargeable sensor k ok , and calculate

[0121] If d k ≥ d ok , it indicates that the first tangent point O tmp is within the distance circle of the rechargeable sensor k.

[0122] If d k < d ok , it indicates that there is a situation where the rechargeable sensor k intersects with the rechargeable sensors i and j in a three-circle intersection, then the maximum distance m * needs to be re-solved. Establish a system of equations and solve it. The specific system of equations is:

[0123]

[0124] Among them, represents the abscissa of the intersection point when three circles intersect; x i represents the abscissa of the first rechargeable sensor in the first rechargeable sensor pair; represents the ordinate of the intersection point when three circles intersect; y i represents the ordinate of the first rechargeable sensor in the first rechargeable sensor pair; d i represents the expansion radius of the first rechargeable sensor in the first rechargeable sensor pair forming a common intersection point with the other two sensors, corresponding to an m * value; x j represents the abscissa of the second rechargeable sensor in the first rechargeable sensor pair; y j represents the ordinate of the second rechargeable sensor in the first rechargeable sensor pair; x k represents the constant coordinate of the rechargeable sensor k; y k represents the ordinate of the rechargeable sensor k; d kIt indicates that the rechargeable sensor k forms a common intersection point with the other two sensors, and at the same time, the m values corresponding to the three sensors are equal. * Equal.

[0125] Then, eliminate the rechargeable sensor with the smallest charging requirement among the rechargeable sensors i, j, and k, and the remaining ones are used as the new rechargeable sensors i and j in the remaining loop. At the same time, update the height and the first tangent point O tmp (x tmp , y tmp ).

[0126] (6) Finally, return the final position O of the power supply point tmp (x tmp , y tmp , H tmp ).

[0127] S103. Delete the covering rechargeable sensor set from the rechargeable sensor set to obtain the first rechargeable sensor set;

[0128] Exemplarily, obtain the covering rechargeable sensor set corresponding to the best hover point, and then delete the covering rechargeable sensor set from the rechargeable sensor set to obtain the first rechargeable sensor set.

[0129] S104. Loop and execute S102 to S103 until the first rechargeable sensor set is empty, and output the hover point set;

[0130] Exemplarily, determine whether there are still uncovered rechargeable sensors in the first rechargeable sensor set; if so, loop and execute S102 to S103; if not, output the hover point set.

[0131] S105. Based on the LKH algorithm, use the hover point set to obtain the energy-saving trajectory of the UAV.

[0132] Specifically, according to the hover point set, use the LKH algorithm to obtain the energy-saving trajectory of the UAV, including the following steps S1051 to S1053.

[0133] S1051. Convert the positions of each hover point in the hover point set into three-dimensional coordinates to obtain a three-dimensional coordinate set, and then use the LKH algorithm according to the three-dimensional coordinate set to obtain an initial access sequence;

[0134] S1052. Fill the three-dimensional coordinates with a Z-axis coordinate of 0 in each three-dimensional coordinate in the three-dimensional coordinate set of the initial access sequence to obtain a filled three-dimensional coordinate set;

[0135] S1053. Use the filled three-dimensional coordinate set as the energy-saving trajectory of the UAV.

[0136] Exemplarily, (1) Initialize two empty sets V = {} and P * = {};

[0137] (2) Input the three-dimensional coordinate set P into the heuristic (Lin-Kernighan-Heuristic Algorithm, LKH) algorithm to obtain the initial access sequence V;

[0138] (3) Perform left-right averaging filling on the three-dimensional coordinates with Z-axis coordinate equal to 0 in each three-dimensional coordinate of the three-dimensional coordinate set in the initial access sequence to obtain the filled three-dimensional coordinate set;

[0139] (4) Use the filled three-dimensional coordinate set as the energy-saving trajectory of the unmanned aerial vehicle.

[0140] Exemplarily, in a specific usage embodiment provided by the invention, first, the rechargeable sensor set of the invention is input into step S102, see Figure 2 , to obtain the boundary rechargeable sensor set, Figure 3 , the rechargeable sensor group corresponding to the red line in Figure 3 . After obtaining the boundary rechargeable sensor set, according to S102, find the rechargeable sensor corresponding to the minimum angle, see Figure 4 . For example, currently, the rechargeable sensor A is found to correspond to the minimum angle. Then draw the first circle H1 with the maximum charging range as the radius. H1 includes rechargeable sensor A, rechargeable sensor B, and rechargeable sensor D. Then draw the second circle H2 with a radius greater than one times the charging range and less than two times the maximum charging range. H2 includes rechargeable sensor C and rechargeable sensor E. Therefore, determine that the rechargeable sensor group tmp1 covering is {rechargeable sensor A, rechargeable sensor B}. And the possible rechargeable sensor set tmp2 covering is {rechargeable sensor C, rechargeable sensor E}.

[0141] Use rechargeable sensor C or rechargeable sensor E in the determined rechargeable sensor group tmp1 covering and the possible rechargeable sensor set tmp2 covering, and use the radius expansion limit search algorithm to determine the center of the circle. Finally, use the center of the circle with the largest number of covered sensors as the best hovering point this time. And the effect of the radius expansion limit search algorithm is shown in Figure 5 , assuming that there are rechargeable sensor A, rechargeable sensor B, rechargeable sensor C, and rechargeable sensor D in the figure. At this time, expand the radius of the rechargeable sensor according to the deduced . Then there will be the final intersection point E shown in the figure.

[0142] Point E is the optimal planar charging point of the present invention. Then, according to the optimal height formula H = 0.23620570473315478 * R, where R refers to the radius with the smallest charging demand in the intersection of circles. Among them, to solve the intersection points of the planes, it is necessary to first solve the intersection points of two-by-two circles. Then, judge the m ij Correspondingly, check whether all the circles intersect. If not, calculate the non-intersecting circles and the intersection points of two-by-two circles, and finally achieve the purpose of all circles intersecting.

[0143] Judge whether there are still uncovered rechargeable sensors in the first set of rechargeable sensors; if so, loop through S102 to S103; if not, output the set of hovering points.

[0144] If the first set of rechargeable sensors is empty, then next, first use the traveling salesman solution algorithm LKH to obtain the visiting order. Then, according to the visiting order, calculate the average of the hovering points at height 0 on the left and right, and then fill them into the hovering points. Finally, obtain the charging trajectory of the drone. See Figure 6 , which is the overall algorithm effect diagram of the present invention.

[0145] In a specific simulation embodiment provided by the present invention, our is the algorithm of the present invention; Greedy-Power is a bundled charging algorithm based on the minimum enclosing circle algorithm; Greedy-Num is a modified version of the bundled charging algorithm based on the minimum enclosing circle algorithm, and each time it selects the largest number of sensors in the coverage area; older is the radius expansion search algorithm. Although these three algorithms are planar algorithms, when extended to three dimensions for comparison, the effects are shown in Figure 7 .

[0146] The present invention fully considers the three-dimensional space environment and the channel occlusion problem in urban wireless transmission. Different from most of the existing technologies based on two-dimensional space research, when determining the hovering points of the drone and planning the trajectory, the present invention comprehensively considers factors such as signal attenuation and airspace restrictions at different heights in the three-dimensional space, and can better adapt to the actual complex environment, such as the scene of high-rise buildings in the city. Therefore, on the premise of being more in line with the actual complex environment, the charging efficiency can be improved, energy waste can be reduced, and the charging requirements of sensor nodes in the three-dimensional space can be accurately met.

[0147] The present invention not only considers the charging energy consumption problem but also takes into account the moving energy consumption of the drone. By comprehensively considering the moving energy consumption and the charging energy consumption, the charging path and the hovering point position of the drone are optimized, so that when the present invention completes the wireless sensor network charging task, it can be achieved with less energy consumption and has higher charging efficiency.

[0148] The present invention considers a more general non - linear model and fully takes into account the complex energy consumption situation of the unmanned aerial vehicle. The radius expansion limit search algorithm adopted by the present invention takes into account the limitation of the limited charging range during the search process, improves the problem that the radius expansion search algorithm does not consider this limitation during the radius expansion process, avoids exceeding the maximum charging range, and can find the optimal charging position, so the charging efficiency is higher than that of the radius expansion search algorithm.

[0149] The charging efficiency advantage of the present invention is significant: through the simulation comparison with the radius expansion search algorithm (older) and the bundled charging algorithms (Greedy - Num, Greedy - Power), the results show that the present invention is superior to the existing algorithms in terms of charging efficiency. The present invention can complete the charging task of the entire network with lower energy consumption, further verifying the advantage of the present invention in improving the performance of the wireless charging sensor network.

[0150] The various embodiments in this specification are described in a progressive manner. For the same or similar parts between the various embodiments, reference can be made to each other. The key point of each embodiment is to illustrate the differences from other embodiments. All or part of the present invention can be used in many general - purpose or special - purpose computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet - type devices, mobile communication terminals, multi - processor systems, micro - processor - based systems, programmable electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and so on.

[0151] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the present invention; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the present invention.

Claims

1. A method for designing energy-saving trajectories of unmanned aerial vehicles based on wireless charging sensor networks, characterized in that: include: S101, determining a plurality of rechargeable sensors in a wireless rechargeable sensor network within a two-dimensional geographic area to obtain a rechargeable sensor set; S102, for each drone, based on a radius expansion search algorithm, calculate the hovering point position according to the maximum coverage area of ​​the rechargeable sensor, then determine the set of covered rechargeable sensors corresponding to the drone according to the hovering point position, and add the hovering point position to the hovering point set; S103, deleting the covering rechargeable sensor set from the rechargeable sensor set to obtain a first rechargeable sensor set; S104, looping through S102 to S103 until the first rechargeable sensor set is empty, and outputting the hovering point set; S105, obtaining an energy-saving trajectory of the UAV by using an LKH algorithm according to the hovering point set.

2. The energy-saving trajectory design method for unmanned aerial vehicle based on wireless charging sensor network according to claim 1 is characterized in that: The method of calculating the hovering point position for each drone based on the radius expansion search algorithm and the maximum coverage area of ​​the rechargeable sensor, determining the set of covered rechargeable sensors corresponding to the drone based on the hovering point position, and adding the hovering point position to the hovering point set includes: Using a convex hull detection algorithm to screen the rechargeable sensor set to obtain a boundary rechargeable sensor set; Determine a maximum coverage rechargeable sensor set using a radius expansion restriction search algorithm according to each boundary rechargeable sensor in the boundary rechargeable sensor set; The maximum distance between two rechargeable sensors is calculated according to the maximum coverage rechargeable sensor set, and the initial hovering point position is calculated according to the maximum distance; Determine a second rechargeable sensor set according to the maximum distance, judge the intersection of each rechargeable sensor in the second rechargeable sensor set, and update the maximum distance and the initial hovering point position according to each judgment result to obtain an updated hovering point position, until all rechargeable sensors in the second rechargeable sensor set have completed the judgment, and output the final updated hovering point position; wherein the second rechargeable sensor set is a subset of the maximum coverage rechargeable sensor set; The finally updated hover point position is added to the hover point set.

3. The energy-saving trajectory design method for unmanned aerial vehicle based on wireless charging sensor network according to claim 2 is characterized in that: The step of using a convex hull detection algorithm to screen the rechargeable sensor set to obtain a boundary rechargeable sensor set includes: Determine the two-dimensional coordinates of each rechargeable sensor in the rechargeable sensor set, and sort the two-dimensional coordinates in ascending order to obtain a sorted rechargeable sensor set; Identifying a rechargeable sensor corresponding to a convex hull starting point in the sorted rechargeable sensor set; Based on the polar angle calculation formula, respectively calculate the polar angles of the remaining rechargeable sensors other than the starting point of the convex hull relative to the rechargeable sensor corresponding to the starting point of the convex hull, and then sort the polar angles in ascending order to obtain a polar angle sorted rechargeable sensor set; A lower convex hull stack is constructed according to the polar angle sorted chargeable sensor set, and the elements in the lower convex hull stack are the boundary chargeable sensor set.

4. The energy-saving trajectory design method for unmanned aerial vehicle based on wireless charging sensor network according to claim 2 is characterized in that: The calculating a maximum distance according to the maximum coverage rechargeable sensor set, and then calculating an initial hovering point position according to the maximum distance, comprises: Combining the rechargeable sensors in the maximum coverage rechargeable sensor set in pairs to obtain a plurality of combined rechargeable sensor pairs; The Euclidean distance between two rechargeable sensors in each rechargeable sensor pair is calculated respectively to obtain a distance set; Screening the distance set to obtain a maximum distance, and determining a first chargeable sensor pair corresponding to the maximum distance; Taking the coordinates of the first rechargeable sensor and the second rechargeable sensor in the first rechargeable sensor pair as points and the maximum distance as a radius, draw a circle to obtain the two-dimensional coordinates of the first tangent point of the two circles, and use the two-dimensional coordinates as the initial two-dimensional coordinates of the hovering point position; According to the coordinates of the first rechargeable sensor and the second rechargeable sensor and the charging requirement of the drone, a first distance between the drone and the first rechargeable sensor and a second distance between the drone and the second rechargeable sensor are calculated using a distance calculation formula, and according to the first distance and the second distance, an initial hovering height is calculated using a height calculation formula; The initial hovering point position is obtained according to the initial hovering height and the initial hovering point position two-dimensional coordinates.

5. The energy-saving trajectory design method for unmanned aerial vehicle based on wireless charging sensor network according to claim 4 is characterized in that: The distance calculation formula is expressed as: Where beita represents the first constant; m represents the independent variable positively correlated with the distance d; d max represents the maximum transmission distance of the drone; m represents the expansion radius; C l Indicates the charging requirement of the lth rechargeable sensor.

6. The energy-saving trajectory design method for unmanned aerial vehicle based on wireless charging sensor network according to claim 4 is characterized in that: The height calculation formula is expressed as: in, represents the first distance; Indicates the second distance; H tmp Indicates the initial hover height.

7. The energy-saving trajectory design method for unmanned aerial vehicle based on wireless charging sensor network according to claim 4 is characterized in that: The updating of the maximum distance and the initial hovering point position according to each determination result to obtain an updated hovering point position includes: Calculate the third distance d between each rechargeable sensor in the second rechargeable sensor set and the first tangent point respectively ok ; Calculate the rechargeable sensor coverage distance d corresponding to each rechargeable sensor in the second rechargeable sensor set k ; Compare the third distances d corresponding to the rechargeable sensors in the second rechargeable sensor set ok and rechargeable sensors covering distance d k , according to the comparison result, the initial hovering point position is updated to obtain an updated hovering point position.

8. The energy-saving trajectory design method for unmanned aerial vehicle based on wireless charging sensor network according to claim 7 is characterized in that: The updating of the initial hovering point position according to the comparison result to obtain an updated hovering point position includes: If the third distance d ok Greater than or equal to the distance d covered by the rechargeable sensor k , then determine that the first tangent point is at the distance d covered by the rechargeable sensor k If there are no three circles intersecting, the first tangent point will not be updated; If the third distance d ok Less than the distance d covered by the rechargeable sensor k , then the first tangent point is not within the range of the rechargeable sensor covering distance d k There are three circles intersecting inside, calculate the second tangent point when the three circles intersect; The first rechargeable sensor pair and the rechargeable sensor cover a distance d k Among the corresponding k-th rechargeable sensors, the rechargeable sensor with the smallest charging demand is deleted, and the remaining rechargeable sensors are used as the first rechargeable sensor pair; An updated hovering point position is calculated based on the first chargeable sensor pair and the second tangent point.

9. The energy-saving trajectory design method for unmanned aerial vehicle based on wireless charging sensor network according to claim 1 is characterized in that: The method of obtaining the energy-saving trajectory of the UAV by using the LKH algorithm according to the hovering point set includes: Converting the position of each hovering point in the hovering point set into a three-dimensional coordinate to obtain a three-dimensional coordinate set, and then obtaining an initial access sequence using the LKH algorithm based on the three-dimensional coordinate set; Filling the three-dimensional coordinates whose Z-axis coordinates are 0 in each three-dimensional coordinate in the initial access sequence to obtain a filled three-dimensional coordinate set; The filled three-dimensional coordinate set is used as the energy-saving trajectory of the UAV.

Images