Truck-unmanned aerial vehicle multi-target collaborative express delivery and pickup path planning method

Abstract

The invention discloses a truck-unmanned aerial vehicle multi-target cooperative express delivery and pickup path planning method. The method comprises the following steps: acquiring related parameters and a constraint condition set of a vehicle-aircraft cooperative logistics service; constructing a target function set representing the path cost minimization of all client points of the vehicle-machine collaborative logistics service based on the related parameters; performing joint solution on the target function set and the constraint condition set in combination with a heuristic algorithm to obtain an optimal express delivery and pickup path scheme of the vehicle-machine collaborative logistics service; and based on the optimal express delivery and pickup path scheme, the unmanned aerial vehicle and the truck are controlled to realize delivery and pickup of the express delivery of the client. According to the method, the truck executes the express pickup task, and the unmanned aerial vehicle only executes the express delivery task on the premise of meeting the load and voyage constraints, so that the performability is increased; through subarea division and loading of the multiple unmanned aerial vehicles by the truck, each unmanned aerial vehicle only takes off once in each subarea, and delivery service can be provided for multiple client points, so that the service efficiency of the client points is greatly improved, and the total driving cost of the truck and the unmanned aerial vehicles is optimal through reasonable path planning.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles (UAVs), and in particular to a method for planning a path for truck-UAV multi-target collaborative delivery of express items. Background Art

[0002] At present, drone logistics distribution is mainly used in express delivery, e-commerce, medical and other fields. However, drone transportation has disadvantages such as short range and small load capacity. Therefore, the two-level collaborative distribution system constructed by trucks and drones has become the mainstream of research, that is, trucks transport express and drones to the corresponding locations, and then drones complete the express delivery to minimize delivery costs. In this process, trucks serve as mobile warehouses and mobile charging stations for drones. Its essence is an extension of the two-echelon routing problem (2E-RP), which is an NP-hard problem. Compared with traditional transportation, by carrying drones on trucks, the advantages of trucks' long range and large load capacity can be combined with the advantages of drone transportation such as high efficiency, low cost, strong adaptability, environmental protection and energy saving. At present, the relevant vehicle-machine collaborative logistics technology has the following problems:

[0003] First: Some related technologies consider vehicle-mounted drones in cooperation with express delivery, only consider one drone, or the truck only acts as a mobile warehouse and charging station. The above situations will lead to low efficiency of truck-drone collaborative logistics and extend the overall logistics time.

[0004] Secondly: Currently, most of the research on collaborative logistics only involves express delivery services. For drone delivery, the advantage is that drones can rely on trucks to serve a wider range of customer points under limited flight range, and have more prominent advantages in distribution in a large service area. However, when customer points are relatively concentrated, frequent take-offs and landings will result in a higher total path cost. In addition, the potential demand for courier pick-up at customer points cannot be met, and the practicality is not high.

[0005] Third: Although some related technologies take express delivery retrieval into consideration, the retrieval task of express delivery is usually performed by drones. However, the retrieval of drones is obviously susceptible to realistic constraints, such as the impact of the surrounding environment due to the descent altitude and the need for cooperation from personnel at the customer point. Therefore, the retrieval conditions are relatively harsh and it is difficult to be feasible in real life. Summary of the invention

[0006] The present invention provides a truck-UAV multi-objective collaborative express delivery and collection path planning method to overcome the technical problems in the existing UAV-truck collaborative logistics distribution service that the UAV is subject to practical constraints, has low efficiency in the process of retrieving express delivery, has high time cost and cannot meet customer needs in a practical situation.

[0007] In order to achieve the above object, the technical solution of the present invention is:

[0008] A truck-UAV multi-target collaborative delivery and collection express delivery path planning method, comprising:

[0009] S1: Obtain relevant parameters and constraint condition sets of the vehicle-machine collaborative logistics service, wherein the relevant parameters include drone parameters, customer point parameters, and deployment point parameters; the constraint condition set is used to constrain the way in which trucks and drones deliver express within the total service area;

[0010] S2: constructing an objective function set of the vehicle-machine collaborative logistics service based on the relevant parameters, wherein the objective function set is an objective function for minimizing the path cost of all customer points of the vehicle-machine collaborative logistics service;

[0011] S3: jointly solving the objective function set and the constraint condition set by combining a heuristic algorithm to obtain an optimal delivery and pickup route plan for the vehicle-machine collaborative logistics service, wherein the optimal delivery and pickup route plan satisfies the path cost minimization of all customer points of the vehicle-machine collaborative logistics service;

[0012] S4: Based on the optimal delivery and pickup route plan, control drones and trucks to deliver and pick up express to customer points.

[0013] Furthermore, the set of constraints in S1 includes:

[0014] The restrictions on drones include but are not limited to:

[0015] Constraint 1: The total energy consumption of the a-th UAV in performing its mission cannot exceed the ideal maximum energy consumption budget of the UAV, as shown in formula (1):

[0016]

[0017] in, represents the total energy consumption of the a-th UAV in performing its mission, J max represents the ideal maximum energy consumption of the UAV; p l represents the coordinates of the truck-drone rendezvous point, i.e., the landing point of the drone swarm;

[0018] Constraint 2: The total flight range of the ath UAV cannot exceed the ideal maximum flight range of the UAV, that is, the longest flight range constraint of the UAV, as shown in formula (2).

[0019]

[0020] in, represents the total distance flown by the a-th UAV in the sub-area k to be divided, represents the ideal maximum flight range of the a-th UAV; all sub-areas k in the following constraints refer to the sub-areas to be divided;

[0021] Constraint 3: The actual load of the a-th UAV when taking off from the take-off point cannot exceed the maximum load capacity of the UAV, that is, the maximum load constraint of the UAV, as shown in formula (3):

[0022]

[0023] in, Indicates that the ath drone is at the take-off point p f Actual load at takeoff, Q max Indicates the ideal maximum payload of the drone;

[0024] Constraint 4: The total weight of the delivered express in the sub-area cannot exceed the maximum load capacity of all drones, that is, the maximum weight constraint of the express delivery required by the customer point, as shown in formula (4).

[0025]

[0026] in, represents the weight of the express delivered by the drone to the ith delivery point in sub-area k; p s represents the set of delivery points, P s ={p1,p2,...,p N1}, N1 is the number of delivery points; Constraint 5: Obtain the energy consumption model of the a-th UAV and obtain the actual energy consumption of the a-th UAV so that it is less than the maximum ideal energy consumption of the UAV. The energy consumption model is shown in formula (5).

[0027]

[0028] Among them, Q a (n) represents the load of the a-th UAV after traversing the n-th customer point, W represents the weight of the UAV, g represents the acceleration of gravity, ρ represents the fluid (air) density, ζ represents the area of ​​the rotating blade disk, r represents the number of rotors of the UAV, represents the total flight time of the a-th UAV traversing each delivery point in the area, n a(max) represents the maximum number of delivery points traversed by the a-th drone, Q a (n) represents the load of the a-th UAV after traversing the n-th point. Its value is negatively correlated with the number of traversed points n. Q a (n a(max) )=0;

[0029] Constraint 6: All customer points that need to deliver express can only be served by drones once at most, as shown in formula (6):

[0030]

[0031] in, is a binary decision variable. If the ath drone in sub-area k starts from point p i Fly to point p j ,but The value of is 1, otherwise it is 0;

[0032] Constraint 7: All customer points that need delivery services must be served by drones, as shown in formula (7):

[0033]

[0034] in, Represents the number of all delivery points in sub-area k;

[0035] The constraints on trucks in the sub-area include but are not limited to:

[0036] Constraint 8: All customer points that need to pick up express delivery can only be served by a truck once at most, as shown in formula (8),

[0037]

[0038] in, is a binary decision variable. If the truck starts from the pickup point p i Drive to the pickup point j ,but The value of is 1, otherwise it is 0;

[0039] Constraint 9: All customer points that need to be picked up must be served by trucks, as shown in formula (9),

[0040]

[0041] in, represents the number of all customer points that need to retrieve services in sub-area k;

[0042] The constraints on trucks between sub-regions include but are not limited to:

[0043] Constraint 10: All sub-areas need to be traversed by trucks, as shown in formula (10).

[0044]

[0045] in, is a binary decision variable. If the truck comes from sub-area k i Travel to sub-area k j , then the value is 1, otherwise it is 0;

[0046] Constraint 11: All sub-areas can be traversed by a truck only once, as shown in formula (11),

[0047]

[0048] Among them, N k Indicates the number of sub-regions.

[0049] Furthermore, S2 constructs an objective function set of vehicle-machine collaborative logistics service based on the relevant parameters, including:

[0050] Construct the distance cost function for the drone to pick up the express from the customer point, as shown in formula (12):

[0051]

[0052] Among them, K is the total service area where the customer point is located, is the ath UAV in sub-area k from point p i To point p j distance;

[0053] Construct the distance cost function for trucks to deliver express to customers, as shown in formula (13):

[0054]

[0055] in, The truck in sub-area k starts from the pickup point p i To the pickup point j Distance moved;

[0056] Construct the cost function of the truck driving distance between sub-regions, as shown in formula (14):

[0057]

[0058] in, For trucks in subarea k i To sub-region k j The distance travelled between

[0059] The three objective functions satisfy the shortest distance of the truck and drone delivery paths under the constraint set, as shown in formula (15):

[0060]

[0061] Where D represents the shortest distance of the truck and drone delivery paths that satisfy the constraint set.

[0062] Furthermore, S3 combines a heuristic algorithm to jointly solve the objective function set and the constraint condition set, including:

[0063] S31, judging whether the total service area satisfies the constraint condition set, and if not, designing a constrained k-means clustering method based on the K-means algorithm, using the constrained k-means clustering method to divide the total service area into a number of sub-areas that satisfy the UAV load and range constraints, and using the cluster center to represent the location of the sub-area and serve as the landing point of the UAV group in the sub-area;

[0064] S32, the truck starts from a warehouse outside the total service area and traverses all sub-areas, and uses the ant colony algorithm to solve the optimal path for the truck to traverse between sub-areas;

[0065] S33, pre-selecting a take-off point for the drone, and designing a genetic-particle swarm hybrid algorithm based on the genetic algorithm and the particle swarm algorithm, and using the genetic-particle swarm hybrid algorithm to solve the optimal path for the drone swarm to perform the express delivery task in the sub-area;

[0066] S34, using the take-off point of the drone group as the starting point of the truck in the sub-area, and the landing point of the drone group as the end point of the truck, using the ant colony algorithm to solve the optimal path for the truck to perform the task of retrieving express delivery in the area;

[0067] S35. Based on the three optimal paths, the best delivery and pickup route plan is obtained to minimize the path cost of drones and trucks serving all customer points.

[0068] Furthermore, a constrained k-means clustering method is designed based on the K-means algorithm. The constrained k-means clustering method is used to divide the total service area into several sub-areas that meet the UAV load and range constraints, including:

[0069] S311: Use the elbow method to obtain the optimal number of clusters, which is used as the number of clusters at the beginning of the iteration;

[0070] S312: Obtain the objective function of the k-means algorithm, as shown in formula (16):

[0071]

[0072] μ (j) represents cluster k j The cluster center, that is, sub-region k j The center point, p i represents the i-th customer point, n k Indicates the number of clusters, that is, the number of sub-regions after the sub-regions are divided;

[0073] Determine whether all sub-areas currently divided meet the UAV's longest flight range constraint, the UAV's maximum load constraint and the minimum J(K) value. If not, iterate the k-means algorithm. Each iteration adds 1 to the number of sub-areas in the previous iteration, and repeatedly calculates J(K) to determine whether the current sub-area meets the UAV's longest flight range constraint, the UAV's maximum load constraint and the minimum J(K) value.

[0074] Obtain the longest flight range constraint of the UAV, as shown in formula (17),

[0075]

[0076] p i represents the coordinates of the i-th customer point; μ (j) represents the coordinates of the cluster center;

[0077] When the number of sub-areas simultaneously meets the conditions of the longest flight range constraint of the UAV, the maximum load constraint of the UAV and the minimum J(K) value, the iteration is stopped and the currently divided sub-areas and the number of sub-areas are saved.

[0078] Furthermore, the take-off point of the drone is pre-selected, and a genetic-particle swarm hybrid algorithm is designed based on the genetic algorithm and the particle swarm algorithm. The genetic-particle swarm hybrid algorithm is used to solve the optimal path for the drone swarm to perform the express delivery task in the sub-area, including:

[0079] S331: Discretize the truck paths between sub-areas, and obtain multiple sampling points as candidate points for the pre-selected take-off point of the UAV. The discrete formula is shown in formula (18):

[0080]

[0081] Among them, n p represents the number of pre-selected take-off points between sub-area i and sub-area j, D ij represents the distance between sub-region i and sub-region j, and l represents the distance discrete accuracy;

[0082] S332: Using a sequence encoding method to create an initial population, the initial population is a set of delivery paths of the drone group; the initial population includes Z individual genes, and the individual genes are a delivery path corresponding to a delivery task involving more than two drones to complete collaboratively;

[0083] Initialize the individual genes in the initial population:

[0084] Use the routing sequence and interruption sequence to form individual genes, calculate the interval of each interruption point, and obtain the initial interruption sequence according to the interval of the interruption points, as shown in formula (19):

[0085]

[0086] Among them, int represents the interval of the break point, represents the number of pick-up and delivery points in the kth sub-area, and M is the number of drones; the initial interrupt sequence divides the routing sequence into multiple initial gene sub-sequences starting from the starting end, and the gene sub-sequence is the delivery path of a drone;

[0087] S333. Determine whether each gene subsequence of each individual gene meets the maximum load constraint of the drone, and adjust the individual genes that do not meet the maximum load constraint:

[0088] Determine whether the total demand for the delivery and pickup points of the first part of the gene subsequence of the routing sequence of an individual gene meets the maximum load constraint of the drone;

[0089] If the maximum load constraint of the UAV is met, the first element of the gene subsequence adjacent to the first part of the gene subsequence is selected and added to the first part of the gene subsequence to form a new first part of the gene subsequence, and it is determined whether the new first part of the gene subsequence meets the maximum load constraint of the UAV. If it does, the first element in the aforementioned adjacent gene subsequence is added to the first part of the gene subsequence to continue to form a new first part of the gene subsequence; the above process is repeated until the first element selected does not meet the maximum load constraint of the UAV, and the element is removed; the element represents a certain delivery point sequence number;

[0090] If the maximum load constraint of the drone is not met, the last element of the first part of the gene subsequence is added to the gene subsequence adjacent to the first part of the gene subsequence to form a new gene subsequence, and the element is removed from the first part of the gene subsequence; the process of removing elements is repeated until the total demand of the delivery and pickup points of the part of the gene subsequence meets the maximum load constraint of the drone, and a new gene subsequence is formed;

[0091] Repeat the above process of judging whether the total demand for gene subsequence delivery points meets the maximum load constraint of the drone and forming a new gene subsequence until the total demand for delivery points of all gene subsequences meets the maximum load constraint of the drone, and the adjustment of individual genes is completed;

[0092] S334: Use the PSO algorithm to solve the delivery path of a single individual gene and obtain the optimal path for each gene subsequence of the single individual gene:

[0093] Set the number of particle swarms, the speed and position of the initial particles, and the maximum number of iterations to initialize the particle swarm; the particle swarm represents a path set of a gene subsequence;

[0094] The initial position of the particle swarm is the take-off point p of the sub-region. f ;

[0095] The particle velocity and position update formulas are shown in formulas (20) and (21), and the next delivery point is obtained;

[0096]

[0097]

[0098] in, represents the current velocity of particle i, represents the velocity of particle i at the next moment, Indicates the current position of particle i, Pbest i represents the historical optimal solution of particle i, Gbest i represents the historical optimal solution of the group, Pbest i -x i There is a swap sequence X that makes X i After X transformation, we get Pbest i Similarly, Gbest i -x i is also a swap sequence, symbol It means that two exchange sequences are combined into one exchange sequence. The symbol + means that a sequence is exchanged according to the exchange sequence. r1 and r2 are parameters, which represent Pbest respectively. i -x i With Gbest i -x i The acceptance probability of is shown in formulas (22) and (23).

[0099]

[0100] w represents the total number of iterations, t represents the number of current iterations, t∈(1,w);

[0101] When Gbest i When convergence is reached, the calculated particle position is the optimal position, that is, the optimal path of a gene subsequence;

[0102] Repeat the PSO algorithm solution process for the Z individual genes in the initial population to obtain the optimal path for each individual gene, that is, Z individuals;

[0103] S334: Use genetic algorithm to update individual genes in the initial population:

[0104] Select operator:

[0105] Using the elitist model, the best individual among the Z individuals obtained by the PSO algorithm in S334 is retained, and the other Z-1 individuals are selected according to the roulette method based on the fitness value, and γ-1 individuals are selected from the remaining Z-1 individuals and the best individual to form γ individuals;

[0106] Use γ individuals to generate offspring:

[0107] Randomly select two individuals from the γ individuals as parent individuals, and select one of the sequential crossover operator and the position-based crossover operator to generate the selection parameter λ of the offspring, as shown in formula (24),

[0108]

[0109] The symbol ⊙ indicates that if the given random number is greater than λ, the sequential crossover operator A is selected. ox , otherwise the position-based crossover operator A is used pbx ,symbol It means that the selected crossover operator is used to cross the parents P1 and P2 to generate new offspring, and C1 and C2 represent the two offspring generated by the crossover of the parent individuals;

[0110] Traverse all individuals among the γ individuals until the number of offspring generated and the sum of the γ individuals are equal to the initial population size, and stop generating offspring;

[0111] Repeat the iterative steps S332-S334 until the total distance of the drones converges, stop updating the individual genes, and obtain the optimal path.

[0112] Furthermore, the customer point parameters include customer point coordinates, the services required by each customer point, the weight of the express delivery required by each customer node that requires delivery services, and the visited status; wherein the visited status is a binary variable, taking a value of one or zero.

[0113] Beneficial effect: The present invention provides a truck-UAV multi-objective collaborative delivery and retrieval express delivery path planning method. By setting constraints, the constraints on the truck and the UAV to perform the express retrieval task are determined. The truck performs the express retrieval task, and the UAV only performs the express delivery task under the premise of satisfying the load and range constraints, thereby increasing the executability. At the same time, through the division of sub-service areas and the loading of multiple UAVs by trucks, each UAV only takes off once in each sub-service area. Each UAV can provide delivery services to multiple customer points, greatly improving the service efficiency for customer points. An objective function for minimizing the path cost is set, and the objective function set and the constraint condition set are jointly solved by combining a heuristic algorithm to obtain a reasonable path planning, so that the total driving cost of the truck and the UAV is optimized. BRIEF DESCRIPTION OF THE DRAWINGS

[0114] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0115] Figure 1 A schematic diagram of a method for planning a truck-UAV multi-target collaborative delivery and collection express delivery path according to the present invention;

[0116] Figure 2 A schematic diagram of the trajectory correction of a truck traveling between sub-areas in the present invention;

[0117] Figure 3 It is a schematic diagram of coding initialization adjustment of the improved genetic-particle swarm hybrid algorithm of the present invention;

[0118] Figure 4 It is a schematic diagram of the truck-UAV multi-target collaborative delivery and pickup express delivery path planning method in the present invention. DETAILED DESCRIPTION

[0119] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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 creative work are within the scope of protection of the present invention.

[0120] This embodiment provides a method for planning a truck-drone multi-target collaborative delivery path. Figure 1 As shown, including:

[0121] S1: Obtain relevant parameters and constraint condition sets of the vehicle-machine collaborative logistics service, wherein the relevant parameters include drone parameters, customer point parameters, and deployment point parameters; the constraint condition set is used to constrain the way in which trucks and drones deliver express within the total service area;

[0122] S2: constructing an objective function set of the vehicle-machine collaborative logistics service based on the relevant parameters, wherein the objective function set is an objective function for minimizing the path cost of all customer points of the vehicle-machine collaborative logistics service;

[0123] S3: jointly solving the objective function set and the constraint condition set by combining a heuristic algorithm to obtain an optimal delivery and pickup route plan for the vehicle-machine collaborative logistics service, wherein the optimal delivery and pickup route plan satisfies the path cost minimization of all customer points of the vehicle-machine collaborative logistics service;

[0124] S4: Based on the optimal delivery and pickup route plan, control drones and trucks to deliver and pick up express to customer points.

[0125] Specifically, firstly, the relevant parameters and constraint condition sets of the vehicle-machine collaborative logistics service are obtained, and the relevant parameters include drone parameters, customer point parameters and deployment point parameters; the constraint condition set is used to constrain the way in which trucks and drones deliver express within the total service area, and multiple constraints are set for trucks and drones, which can discuss the feasibility of retrieval services by trucks and drones from a more practical perspective, so that the obtained results are closer to the actual situation; based on the relevant parameters, an objective function set of the vehicle-machine collaborative logistics service is constructed, and the objective function set is an objective function for minimizing the path cost of all customer points of the vehicle-machine collaborative logistics service. This scheme sets the objective function to provide a premise for calculating the minimum path cost; the objective function set and the constraint condition set are jointly solved in combination with the heuristic algorithm to obtain the vehicle-machine collaborative logistics service. The optimal delivery and pickup route plan satisfies the path cost minimization of all customer points of the vehicle-machine collaborative logistics service. This plan designs a constrained k-means clustering method based on the K-means algorithm, which can ensure that the divided sub-areas meet all constraints. The ant colony algorithm is used to obtain the optimal path for trucks to traverse sub-areas and the optimal path for trucks to perform express delivery tasks in the area. A genetic-particle swarm hybrid algorithm is designed based on the genetic algorithm and the particle swarm algorithm, which can obtain the optimal path for drone swarms to perform express delivery tasks in the sub-areas, thereby realizing the optimal delivery and pickup route for trucks carrying multiple drones to provide express delivery and retrieval services to several customers in a certain area; finally, based on the optimal delivery and pickup route plan, drones and trucks are controlled to achieve delivery and retrieval of express at customer points.

[0126] In a specific embodiment, relevant parameters and constraint condition sets of the vehicle-machine collaborative logistics service are obtained, wherein the relevant parameters include drone parameters, customer point parameters, and deployment point parameters; the constraint condition set is used to constrain the delivery method of trucks and drones in the total service area:

[0127] Model the service environment of customer points and assign corresponding coordinate information to each customer point;

[0128] The customer point parameters include the customer point coordinates, the services required by each customer point, the weight of the express delivery required by each customer node that needs delivery services, and the accessed status; wherein the accessed status is a binary variable, and the value is one or zero;

[0129] The services that customer nodes need to provide are divided into delivery service, retrieval service, and delivery and retrieval service. The delivery service is provided by drones, and the retrieval service is provided by trucks. All customer nodes have their delivery or retrieval requirements. In this embodiment, the weight is randomly distributed between 1-5kg.

[0130] The drone parameters mainly consider the maximum load, actual load and maximum range of each drone, the number of drones dispatched in each sub-service area, the number of drones that constitute a drone set, and the service status of each customer node, which is a binary variable with a value of one or zero;

[0131] The restrictions on drones include but are not limited to:

[0132] Constraint 1: The total energy consumption of the ath UAV in performing this mission cannot exceed the ideal maximum energy consumption budget of the UAV, as shown in formula (25),

[0133]

[0134] in, represents the total energy consumption of the ath UAV in performing this mission, J max represents the ideal maximum energy consumption of the UAV; p l represents the coordinates of the truck-drone rendezvous point, i.e., the landing point of the drone swarm;

[0135] Constraint 2: The total flight range of the ath UAV in this mission cannot exceed the ideal maximum flight range of the UAV, that is, the longest flight range constraint of the UAV, as shown in formula (26),

[0136]

[0137] in, represents the total distance flown by the a-th UAV in the sub-area k to be divided, represents the ideal maximum flight range of the a-th UAV; all sub-areas k in the following constraints refer to the sub-areas to be divided;

[0138] Constraint 3: The actual load of the a-th UAV when taking off from the take-off point cannot exceed the maximum load capacity of the UAV, that is, the maximum load constraint of the UAV, as shown in formula (27),

[0139]

[0140] in, Indicates that the ath drone is at the take-off point p f Actual load at takeoff, Q max Indicates the ideal maximum payload of the drone;

[0141] Constraint 4: The total weight of the delivered express in the sub-area cannot exceed the maximum load capacity of all drones, that is, the maximum weight constraint of the express delivery required by the customer point, as shown in formula (28):

[0142]

[0143] in, represents the weight of the express delivered by the drone to the ith delivery point in sub-area k; p s represents the set of delivery points, P s ={p1,p2,...,p N1}, N1 is the number of delivery points; Constraint 5: Obtain the energy consumption model of the a-th UAV and obtain the actual energy consumption of the a-th UAV so that it is less than the maximum ideal energy consumption of the UAV. The energy consumption model is shown in formula (29).

[0144]

[0145] Among them, Q a (n) represents the load of the a-th UAV after traversing the n-th customer point, W represents the weight of the UAV, g represents the acceleration of gravity, ρ represents the fluid (air) density, ζ represents the area of ​​the rotating blade disk, r represents the number of rotors of the UAV, represents the total flight time of the a-th UAV traversing each delivery point in the area, n a(max) represents the maximum number of delivery points traversed by the a-th drone, Q a (n) represents the load of the a-th UAV after traversing the n-th point. Its value is negatively correlated with the number of traversed points n. Q a (n a(max) )=0;

[0146] Constraint 6: All customer points that need to deliver express can only be served by drones once at most, as shown in formula (30),

[0147]

[0148] in, is a binary decision variable. If the ath drone in sub-area k starts from point p i Fly to point p j ,but The value of is 1, otherwise it is 0;

[0149] Constraint 7: All customer points that need delivery services must be served by drones, as shown in formula (31),

[0150]

[0151] in, Represents the number of all delivery points in sub-area k;

[0152] The constraints on trucks in the sub-area include but are not limited to:

[0153] Constraint 8: All customer points that need to pick up express delivery can only be served by a truck once at most, as shown in formula (32),

[0154]

[0155] in, is a binary decision variable. If the truck starts from the pickup point p i Drive to the pickup point j ,but The value of is 1, otherwise it is 0;

[0156] Constraint 9: All customer points that need to be picked up must be served by trucks, as shown in formula (33),

[0157]

[0158] in, represents the number of all customer points that need to retrieve services in sub-area k;

[0159] The constraints on trucks between sub-regions include but are not limited to:

[0160] Constraint 10: All sub-areas need to be traversed by trucks, as shown in formula (34),

[0161]

[0162] in, is a binary decision variable. If the truck comes from sub-area k i Travel to sub-area k j , then the value is 1, otherwise it is 0;

[0163] Constraint 11: All sub-areas can be traversed by a truck only once, as shown in formula (35),

[0164]

[0165] Among them, N k Indicates the number of sub-service areas.

[0166] In this scheme, multiple constraints are set for trucks and drones, which can discuss the feasibility of retrieval services by trucks and drones from a more practical perspective, making the results closer to reality.

[0167] In a specific embodiment, an objective function set of the vehicle-machine collaborative logistics service is constructed based on the relevant parameters, and the objective function set is a scheme for minimizing the path cost of all customer points of the vehicle-machine collaborative logistics service:

[0168] Construct the distance cost function for the drone to pick up the express from the customer point, as shown in formula (36):

[0169]

[0170] Among them, K is the total service area where the customer point is located, is the ath UAV in sub-area k from point p i To point p j distance;

[0171] Construct the distance cost function for trucks to deliver express to customers, as shown in formula (37):

[0172]

[0173] in, The truck in sub-area k starts from the pickup point p i To the pickup point j Distance moved;

[0174] Construct the cost function of the truck driving distance between sub-service areas, as shown in formula (38):

[0175]

[0176] in, For trucks in subarea k i To sub-region k j The distance travelled between

[0177] The three objective functions satisfy the shortest distance of the truck and drone delivery paths under the constraint set, as shown in formula (39):

[0178]

[0179] Where D represents the shortest distance of the truck and drone delivery paths that satisfy the constraint set.

[0180] The objective problem of truck-drone collaborative path planning is the objective function, which includes the objective function of drone delivery under the constraint of payload range, the objective function of truck picking up express within the sub-area, and the objective function of truck driving between sub-areas. This scheme sets the objective function to provide a prerequisite for calculating the minimum path cost.

[0181] In a specific embodiment, the objective function set and the constraint condition set are jointly solved in combination with a heuristic algorithm to obtain the best delivery and pickup route plan for the vehicle-machine collaborative logistics service. The best delivery and pickup route plan satisfies the path cost minimization of all customer points of the vehicle-machine collaborative logistics service:

[0182] S31. Determine whether the total service area satisfies the constraint condition set. If not, design a constrained k-means clustering method based on the K-means algorithm. Use the constrained k-means clustering method to divide the total service area into several sub-areas that meet the UAV load and range constraints. Cluster all the delivery and pickup points in the area under the premise of satisfying constraints (3) and (4). Obtain the optimal number of clusters m and each sub-task area. Use the cluster center point to represent the location of the sub-area and serve as the landing point P of the UAV group in the sub-area. l :

[0183] S311: Use the elbow method to obtain the optimal number of clusters, which is used as the number of clusters at the beginning of the iteration;

[0184] S312: Obtain the objective function of the k-means algorithm, as shown in formula (40):

[0185]

[0186] μ (j) represents cluster k j The cluster center, that is, sub-region k j The center of i represents the i-th customer point, n k Indicates the number of clusters, that is, the number of sub-regions after the sub-regions are divided;

[0187] Determine whether all sub-areas currently divided meet the UAV's longest flight range constraint, the UAV's maximum load constraint and the minimum J(K) value. If not, iterate the k-means algorithm. Each iteration adds 1 to the number of sub-areas in the previous iteration, and repeatedly calculates J(K) to determine whether the current sub-area meets the UAV's longest flight range constraint, the UAV's maximum load constraint and the minimum J(K) value.

[0188] Obtain the longest flight range constraint of the UAV, as shown in formula (41),

[0189]

[0190] p i represents the coordinates of the i-th customer point; μ (j) represents the coordinates of the cluster center;

[0191] When the number of sub-areas simultaneously meets the conditions of the longest flight range constraint of the UAV, the maximum load constraint of the UAV and the minimum J(K) value, the iteration is stopped and the currently divided sub-areas and the number of sub-areas are saved;

[0192] S32, the truck starts from a warehouse outside the total service area and traverses all sub-areas. It is stipulated that between any two sub-areas, the truck takes the previous sub-area K i-1 The cluster center point (i.e., the landing point of the drone) is used as the starting point of the truck. i The direction of the pickup point closest to the starting point is selected as the driving direction of the truck, and the take-off point of the drone in the next sub-area is used as the end point of the truck's driving between sub-areas, so as to shorten the driving distance between the two sub-areas. The ant colony algorithm is used to solve the optimal path for the truck to traverse the sub-areas between sub-areas, and the path planning problem between sub-areas is approximately solved as the optimal traversal order of the truck for the cluster center points of each sub-area, to the optimal path S k ={K1,K2,...,K m}, is the order in which the truck performs tasks between sub-areas; the schematic diagram is as follows Figure 2 As shown;

[0193] S33, pre-selecting the take-off point of the drone, and designing a genetic particle swarm hybrid algorithm based on the genetic algorithm and the particle swarm algorithm, and using the genetic particle swarm hybrid algorithm to solve the optimal path for the drone swarm to perform the express delivery task in the sub-area:

[0194] S331: Discretize the truck paths between sub-areas to obtain a series of sampling points as candidate points for the pre-selected take-off point of the UAV. The discrete formula is shown in formula (42):

[0195]

[0196] Among them, n p represents the number of pre-selected take-off points between sub-area i and sub-area j, D ij represents the distance between sub-region i and sub-region j, and l represents the distance discrete accuracy;

[0197] S332: Using a sequence encoding method to create an initial population, the initial population is a set of delivery paths of the drone group; the initial population contains Z individual genes, and the individual genes are a delivery path corresponding to a delivery task involving more than two drones to complete the task collaboratively; in this embodiment, the value of Z is 80;

[0198] Initialize the individual genes in the initial population:

[0199] The routing sequence and interruption sequence are used to form individual genes. Considering the constraints of the drone, in order to reduce invalid genes, when initializing the interruption sequence, the interval of each interruption point is calculated using prior knowledge, and the initial interruption sequence is obtained according to the interval of the interruption points, as shown in formula (43).

[0200]

[0201] Among them, int represents the interval of the break point, represents the number of pick-up and delivery points in the kth sub-area, and M is the number of drones; the interrupt sequence divides the routing sequence into multiple initial gene sub-sequences starting from the starting end, i.e., the delivery path of a drone; if int is not an integer, it is rounded up to maintain the margin;

[0202] S333: Determine whether each gene subsequence of each individual gene meets the maximum load constraint of the drone, and adjust the individual genes that do not meet the maximum load constraint:

[0203] Determine whether the total demand for the delivery and pickup points of the first part of the gene subsequence of the routing sequence of an individual gene meets the maximum load constraint of the drone;

[0204] If the maximum load constraint of the drone is met, the first element of the gene subsequence adjacent to the first gene subsequence is selected and added to the first gene subsequence to form a new first gene subsequence, and it is determined whether the new first gene subsequence meets the maximum load constraint of the drone;

[0205] Repeat the process of adding elements until the maximum load constraint of the drone is not satisfied after adding the first selected element, and then remove the element; the element represents a sequence number of a pick-up point;

[0206] If the maximum load constraint of the drone is not satisfied, then the last element of the first part of the gene subsequence is added to the gene subsequence adjacent to the first part of the gene subsequence to form a new gene subsequence, and the element is removed from the first part of the gene subsequence; and it is determined whether the first part of the gene subsequence after removing the element satisfies the maximum load constraint of the drone;

[0207] The process of removing elements is repeated until the total demand for the delivery and pickup points of this part of the gene subsequence meets the maximum load constraint of the drone, thus forming a new gene subsequence;

[0208] Repeat the above process of judging whether the total demand for gene subsequence delivery points meets the maximum load constraint of the drone and forming a new gene subsequence until the total demand for delivery points of all gene subsequences meets the maximum load constraint of the drone, and the adjustment of individual genes is completed;

[0209] Schematic diagram as Figure 3 As shown in the figure, the individual gene before adjustment contains three drones, and the interrupt sequence divides the routing sequence into three gene subsequences;

[0210] After judging that the first part of the gene subsequence meets the maximum load constraint of the UAV, the first part of the sequence is adjusted, and the first element of the adjacent gene subsequence is added to the first part of the gene subsequence. As shown in the adjusted figure, the fifth element is added to the first part of the gene subsequence to generate a new first part of the gene subsequence. At this time, the first element in the adjacent gene subsequence has a delivery point sequence number of 4. It is re-judged whether the new first part of the gene subsequence meets the maximum load constraint of the UAV. If it does, the element with the delivery point sequence number of 4 is added to the new first part of the gene subsequence. The process is repeated until the constraint is not satisfied after the first element is added, and the element is stopped from being added, and the element is removed;

[0211] If the first part of the gene subsequence does not meet the constraint, the last element of the first part of the gene subsequence is added to the adjacent gene subsequence. For example, the element with the feed point sequence number 1 is added to the adjacent gene subsequence. At this time, the first part of the gene subsequence has only 3 elements, and the adjacent gene subsequence has 5 elements. It is judged whether the first part of the gene subsequence meets the constraint after removing the element. If not, the current last element is continued to be added to the adjacent gene subsequence until the constraint is met after removing the element. Stop removal.

[0212] S334: Use the PSO algorithm to solve the delivery path of a single individual gene and obtain the optimal path for each gene subsequence of the single individual gene:

[0213] Set the number of particle swarms, the speed and position of the initial particles, and the maximum number of iterations to initialize the particle swarm; the particle swarm represents a set of paths of a gene subsequence; in this scheme, the value of the particle swarm is set to 10;

[0214] The initial position of the particle swarm is the take-off point p of the sub-region. f ;

[0215] The particle velocity and position update formulas are shown in formulas (44) and (45), and the next delivery point is obtained;

[0216]

[0217]

[0218] in, represents the current velocity of particle i, represents the velocity of particle i at the next moment, Indicates the current position of particle i, Pbest i represents the historical optimal solution of particle i, Gbest i represents the historical optimal solution of the group, Pbest i -x i There is a swap sequence X that makes X i After X transformation, we get Pbest i Similarly, Gbest i -x i is also a swap sequence, symbol It means that two exchange sequences are combined into one exchange sequence, and the symbol + means that a sequence is exchanged according to the exchange sequence.

[0219] In order to prevent the particle swarm algorithm from falling into the local optimum and causing stagnation in the later stage, r1 and r2 are designed as parameters, representing Pbest and Pbest respectively. i -x i With Gbest i -x i The acceptance probability of is shown in formulas (46) and (47).

[0220]

[0221] w represents the total number of iterations, t represents the number of current iterations, t∈(1,w); as the number of iterations t increases, r1 gradually decreases and r2 gradually increases, so that the algorithm can jump out of the current stagnation and coordinate its local search and global search capabilities to avoid the algorithm falling into the local optimum;

[0222] When Gbest i When convergence is reached, the calculated particle position is the optimal position, that is, the optimal path of a gene subsequence;

[0223] Repeat the PSO algorithm solution process for the Z individual genes in the initial population to obtain the optimal path for each individual gene, that is, Z individuals;

[0224] S335: Use genetic algorithm to update individual genes in the initial population:

[0225] Select operator:

[0226] Using the elitist model, the best individual among the Z individuals obtained by the PSO algorithm in S334 is retained, and the other Z-1 individuals are selected according to the roulette method based on the fitness value, and γ-1 individuals are selected from the remaining Z-1 individuals and the best individual to form γ individuals;

[0227] Use γ individuals to generate offspring:

[0228] Randomly select two individuals from the γ individuals as parent individuals, and select one of the sequential crossover operator and the position-based crossover operator to generate the selection parameter λ of the offspring, as shown in formula (48),

[0229]

[0230] The symbol ⊙ indicates that if the given random number is greater than λ, the sequential crossover operator A is selected. ox , otherwise the position-based crossover operator A is used pbx ,symbol It means that the selected crossover operator is used to cross the parents P1 and P2 to generate new offspring, and C1 and C2 represent the two offspring generated by the crossover of the parent individuals;

[0231] Traverse all individuals among the γ individuals until the number of offspring generated and the sum of the γ individuals are equal to the initial population size, and stop generating offspring;

[0232] Repeat the steps S333-S335 until the total distance of the drone converges, stop updating the individual genes, and get the optimal path

[0233] S34, release point P with drone f As the starting point of the truck in the sub-area, the drone landing point P l The ant colony algorithm is used to solve the optimal path for the truck to perform the task of picking up express delivery in the area, and the optimal path is obtained.

[0234] S35. Based on the three optimal paths, the best delivery and pickup path solution S = {S i ,S k}, the schematic diagram is as follows Figure 4 shown.

[0235] In this scheme, a genetic-particle swarm hybrid algorithm is designed based on the genetic algorithm and the particle swarm algorithm. First, the initial population is created and their genes are initialized. The present invention adopts a sequence encoding method, and the genes of each individual are composed of two parts: a routing sequence and a breakpoint sequence. Considering the constraints of the drone, in order to reduce invalid genes, when initializing the break sequence, the interval int of each breakpoint is estimated using prior knowledge, so as to make the delivery point assigned to each drone meet the constraints of the drone as much as possible;

[0236] By adding prior knowledge to the population gene initialization, the existence of individuals that do not meet the constraints (invalid genes) can be avoided as much as possible, thereby reducing the probability of extreme values ​​in the initial results. The innovation of the population update algorithm can effectively increase the diversity of population genes, thereby finding the optimal solution faster. The adjustment after the population update is to recode the individual genes that do not meet the constraints, reduce invalid genes, and improve the efficiency of each iteration. The above operations can ensure that the optimal solution to the problem is obtained with a smaller number of iterations.

[0237] In summary, this scheme designs a constrained k-means clustering method based on the K-means algorithm, which can ensure that the divided sub-areas meet all constraints. The ant colony algorithm is used to obtain the optimal path for the truck to traverse the sub-areas and the optimal path for the truck to perform the express delivery task in the area. The genetic particle swarm hybrid algorithm is designed based on the genetic algorithm and the particle swarm algorithm, which can obtain the optimal path for the drone swarm to perform the express delivery task in the sub-area, and then realize the optimal delivery and pickup path for the truck to carry multiple drones to provide express delivery and retrieval services to several customers in a certain area.

[0238] In a specific embodiment, the solution for controlling drones and trucks to deliver and retrieve express to customer points based on the optimal delivery and retrieval route solution is:

[0239] The best delivery and pickup route is that the truck starts from the warehouse and drives to the nearest customer point in the first planned area. When it reaches the take-off point, the drone is released. From the take-off point, the drone traverses all the delivery points in the area to perform delivery services, and the truck traverses all the pickup points in the area in turn to perform return delivery services. After completing the task, the drone and the truck go to the landing point to gather. If the drone arrives before the truck, it will wait here; if the truck arrives at the landing point before the drone, the drone will land on the truck. When all drones have landed on the truck, they will go to the next area.

[0240] From a modeling perspective, unlike most studies on delivery and pickup services performed by drones, the present invention discusses the feasibility of truck retrieval services from a more practical perspective. On this basis, the present invention studies the optimal solution for path planning to minimize the total driving distance for delivery and pickup services, including the path planning for drone delivery and the path planning for truck retrieval. When discussing the path planning problem of drone swarms delivering express, the optimal take-off point, flight path, and landing point of the drone swarm in each sub-region are discussed under the premise of satisfying the actual constraints of the drones. This method shortens the time to execute the task as much as possible while ensuring the shortest total flight distance.

[0241] From the perspective of algorithm design, since the tasks of drones and trucks are different, the connection between the two is not close enough during the execution of the tasks. Therefore, the present invention combines a variety of heuristic algorithms to ensure that customers in each sub-area meet the load and range constraints of the drone group. In order to take advantage of the speed of drones, it is necessary to release drones as early as possible. The present invention also discusses the selection of take-off points for drone groups. When the pre-selected take-off point and landing point of the drone are determined, the path planning problem of the UAV is transformed into a constrained MVRP problem. The present invention takes into account the actual constraints of the range and load of the drone, and designs a genetic particle swarm hybrid algorithm to solve the path planning of the UAV.

[0242] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A truck-UAV multi-target collaborative delivery and collection express delivery path planning method, characterized in that: include: S1: Obtain relevant parameters and constraint condition sets of vehicle-machine collaborative logistics services, wherein the relevant parameters include drone parameters, customer point parameters, and deployment point parameters; The set of constraints is used to constrain the way in which trucks and drones perform express deliveries within the total service area; S2: constructing an objective function set of the vehicle-machine collaborative logistics service based on the relevant parameters, wherein the objective function set is an objective function for minimizing the path cost of all customer points of the vehicle-machine collaborative logistics service; S3: jointly solving the objective function set and the constraint condition set by combining a heuristic algorithm to obtain an optimal delivery and pickup route plan for the vehicle-machine collaborative logistics service, wherein the optimal delivery and pickup route plan satisfies the path cost minimization of all customer points of the vehicle-machine collaborative logistics service; S4: Based on the optimal delivery and pickup route plan, control drones and trucks to deliver and pick up express to customer points.

2. The truck-UAV multi-target collaborative delivery and collection express delivery path planning method according to claim 1 is characterized in that: The set of constraints in S1 includes: The restrictions on drones include but are not limited to: Constraint 1: The total energy consumption of the a-th UAV in performing its mission cannot exceed the ideal maximum energy consumption budget of the UAV, as shown in formula (1): in, represents the total energy consumption of the a-th UAV in performing its mission, J max represents the ideal maximum energy consumption of the UAV; p l represents the coordinates of the truck-drone rendezvous point, i.e., the landing point of the drone swarm; Constraint 2: The total flight range of the ath UAV cannot exceed the ideal maximum flight range of the UAV, that is, the longest flight range constraint of the UAV, as shown in formula (2). in, represents the total distance flown by the a-th UAV in the sub-area k to be divided, represents the ideal maximum flight range of the a-th UAV; all sub-areas k in the following constraints refer to the sub-areas to be divided; Constraint 3: The actual load of the a-th UAV when taking off from the take-off point cannot exceed the maximum load capacity of the UAV, that is, the maximum load constraint of the UAV, as shown in formula (3): in, Indicates that the ath drone is at the take-off point p f Actual load at takeoff, Q max Indicates the ideal maximum payload of the drone; Constraint 4: The total weight of the delivered express in the sub-area cannot exceed the maximum load capacity of all drones, that is, the maximum weight constraint of the express delivery required by the customer point, as shown in formula (4). in, represents the weight of the express delivered by the drone to the ith delivery point in sub-area k; p s represents the set of delivery points, P s ={p1,p2,...,p N1 }, N1 is the number of delivery points; Constraint 5: Obtain the energy consumption model of the a-th UAV and obtain the actual energy consumption of the a-th UAV so that it is less than the maximum ideal energy consumption of the UAV. The energy consumption model is shown in formula (5). Among them, Q a (n) represents the load of the a-th UAV after traversing the n-th customer point, W represents the weight of the UAV, g represents the acceleration of gravity, ρ represents the fluid (air) density, ζ represents the area of ​​the rotating blade disk, r represents the number of rotors of the UAV, represents the total flight time of the a-th UAV traversing each delivery point in the area, n a(max) represents the maximum number of delivery points traversed by the a-th drone, Q a (n) represents the load of the a-th UAV after traversing the n-th point. Its value is negatively correlated with the number of traversed points n. Q a (n a(max) )=0; Constraint 6: All customer points that need to deliver express can only be served by drones once at most, as shown in formula (6): in, is a binary decision variable. If the ath drone in sub-area k starts from point p i Fly to point p j ,but The value of is 1, otherwise it is 0; Constraint 7: All customer points that need delivery services must be served by drones, as shown in formula (7): in, Represents the number of all delivery points in sub-area k; The constraints on trucks in the sub-area include but are not limited to: Constraint 8: All customer points that need to pick up express delivery can only be served by a truck once at most, as shown in formula (8), in, is a binary decision variable. If the truck starts from the pickup point p i Drive to the pickup point j ,but The value of is 1, otherwise it is 0; Constraint 9: All customer points that need to be picked up must be served by trucks, as shown in formula (9), in, represents the number of all customer points that need to retrieve services in sub-area k; The constraints on trucks between sub-regions include but are not limited to: Constraint 10: All sub-areas need to be traversed by trucks, as shown in formula (10). in, is a binary decision variable. If the truck comes from sub-area k i Travel to sub-area k j , then the value is 1, otherwise it is 0; Constraint 11: All sub-areas can be traversed by a truck only once, as shown in formula (11), Among them, N k Indicates the number of sub-regions.

3. The truck-UAV multi-target collaborative delivery and collection express delivery path planning method according to claim 3 is characterized in that: S2 constructs a target function set of vehicle-machine collaborative logistics service based on the relevant parameters, including: Construct the distance cost function for the drone to pick up the express from the customer point, as shown in formula (12): Where K is the total service area where the customer point is located; is the ath UAV in sub-area k from point p i To point p j distance; Construct the distance cost function for trucks to deliver express to customers, as shown in formula (13): in, The truck in sub-area k starts from the pickup point p i To the pickup point j Distance moved; Construct the cost function of the truck driving distance between sub-regions, as shown in formula (14): in, For trucks in subarea k i To sub-region k j The distance travelled between The three objective functions satisfy the shortest distance of the truck and drone delivery paths under the constraint set, as shown in formula (15): Where D represents the shortest distance of the truck and drone delivery paths that satisfy the constraint set.

4. The truck-UAV multi-target collaborative delivery and collection express delivery path planning method according to claim 1 is characterized in that: S3 combines the heuristic algorithm to jointly solve the objective function set and the constraint condition set, including: S31, judging whether the total service area satisfies the constraint condition set, and if not, designing a constrained k-means clustering method based on the K-means algorithm, using the constrained k-means clustering method to divide the total service area into a number of sub-areas that satisfy the UAV load and range constraints, and using the cluster center to represent the location of the sub-area and serve as the landing point of the UAV group in the sub-area; S32, the truck starts from a warehouse outside the total service area and traverses all sub-areas, and uses the ant colony algorithm to solve the optimal path for the truck to traverse between sub-areas; S33, pre-selecting a take-off point for the drone, and designing a genetic-particle swarm hybrid algorithm based on the genetic algorithm and the particle swarm algorithm, and using the genetic-particle swarm hybrid algorithm to solve the optimal path for the drone swarm to perform the express delivery task in the sub-area; S34, using the take-off point of the drone group as the starting point of the truck in the sub-area, and the landing point of the drone group as the end point of the truck, using the ant colony algorithm to solve the optimal path for the truck to perform the task of retrieving express delivery in the area; S35. Based on the three optimal paths, the best delivery and pickup route plan is obtained to minimize the path cost of drones and trucks serving all customer points.

5. The truck-UAV multi-target collaborative delivery and collection express delivery path planning method according to claim 4 is characterized in that: A constrained k-means clustering method is designed based on the K-means algorithm. The constrained k-means clustering method is used to divide the total service area into several sub-areas that meet the UAV load and range constraints, including: S311: Use the elbow method to obtain the optimal number of clusters, which is used as the number of clusters at the beginning of the iteration; S312: Obtain the objective function of the k-means algorithm, as shown in formula (16): μ (j) represents cluster k j The cluster center, that is, sub-region k j The center point, p i represents the i-th customer point, n k Indicates the number of clusters, that is, the number of sub-regions after the sub-regions are divided; Determine whether all sub-areas currently divided meet the UAV's longest flight range constraint, the UAV's maximum load constraint and the minimum J(K) value. If not, iterate the k-means algorithm. Each iteration adds 1 to the number of sub-areas in the previous iteration, and repeatedly calculates J(K) to determine whether the current sub-area meets the UAV's longest flight range constraint, the UAV's maximum load constraint and the minimum J(K) value. Obtain the longest flight range constraint of the UAV, as shown in formula (17), p i represents the coordinates of the i-th customer point; μ (j) represents the coordinates of the cluster center; When the number of sub-areas simultaneously meets the conditions of the longest flight range constraint of the UAV, the maximum load constraint of the UAV and the minimum J(K) value, the iteration is stopped and the currently divided sub-areas and the number of sub-areas are saved.

6. The truck-UAV multi-target collaborative delivery and collection express delivery path planning method according to claim 4 is characterized in that: Pre-select the take-off point of the drone, design a genetic-particle swarm hybrid algorithm based on the genetic algorithm and the particle swarm algorithm, and use the genetic-particle swarm hybrid algorithm to solve the optimal path for the drone swarm to perform the express delivery task in the sub-area, including: S331: Discretize the truck paths between sub-areas, and obtain multiple sampling points as candidate points for the pre-selected take-off point of the UAV. The discrete formula is shown in formula (18): Among them, n p represents the number of pre-selected take-off points between sub-area i and sub-area j, D ij represents the distance between sub-region i and sub-region j, and l represents the distance discrete accuracy; S332: Using a sequence encoding method to create an initial population, the initial population is a set of delivery paths of the drone group; the initial population includes Z individual genes, and the individual genes are a delivery path corresponding to a delivery task involving more than two drones to complete collaboratively; Initialize the individual genes in the initial population: Use the routing sequence and interruption sequence to form individual genes, calculate the interval of each interruption point, and obtain the initial interruption sequence according to the interval of the interruption points, as shown in formula (19): Among them, int represents the interval of the break point, represents the number of pick-up and delivery points in the kth sub-area, and M is the number of drones; the initial interrupt sequence divides the routing sequence into multiple initial gene sub-sequences starting from the starting end, and the gene sub-sequence is the delivery path of a drone; S333: Determine whether each gene subsequence of each individual gene meets the maximum load constraint of the drone, and adjust the individual genes that do not meet the maximum load constraint: Determine whether the total demand for the delivery and pickup points of the first part of the gene subsequence of the routing sequence of an individual gene meets the maximum load constraint of the drone; If the maximum load constraint of the UAV is met, the first element of the gene subsequence adjacent to the first part of the gene subsequence is selected and added to the first part of the gene subsequence to form a new first part of the gene subsequence, and it is determined whether the new first part of the gene subsequence meets the maximum load constraint of the UAV. If it does, the first element in the aforementioned adjacent gene subsequence is added to the first part of the gene subsequence to continue to form a new first part of the gene subsequence; the above process is repeated until the first element selected does not meet the maximum load constraint of the UAV, and the element is removed; the element represents a certain delivery point sequence number; If the maximum load constraint of the drone is not met, the last element of the first part of the gene subsequence is added to the gene subsequence adjacent to the first part of the gene subsequence to form a new gene subsequence, and the element is removed from the first part of the gene subsequence; the process of removing elements is repeated until the total demand of the delivery and pickup points of the part of the gene subsequence meets the maximum load constraint of the drone, and a new gene subsequence is formed; Repeat the above process of judging whether the total demand for gene subsequence delivery points meets the maximum load constraint of the drone and forming a new gene subsequence until the total demand for delivery points of all gene subsequences meets the maximum load constraint of the drone, and the adjustment of individual genes is completed; The element represents a certain delivery point sequence number S334: Use the PSO algorithm to solve the delivery path of a single individual gene and obtain the optimal path for each gene subsequence of the single individual gene: Set the number of particle swarms, the speed and position of the initial particles, and the maximum number of iterations to initialize the particle swarm; the particle swarm represents a path set of a gene subsequence; The initial position of the particle swarm is the take-off point p of the sub-region. f ; The particle velocity and position update formulas are shown in formulas (20) and (21), and the next delivery point is obtained; Among them, V i t Indicates the current velocity of particle i, V i t+1 represents the velocity of particle i at the next moment, Indicates the current position of particle i, Pbest i represents the historical optimal solution of particle i, Gbest i represents the historical optimal solution of the group, Pbest i -x i Say there is a swap sequence X that makes X i After X transformation, we get Pbest i Similarly, Gbest i -x i is also a swap sequence, symbol It means that two exchange sequences are combined into one exchange sequence. The symbol + means that a sequence is exchanged according to the exchange sequence. r1 and r2 are parameters, which represent Pbest respectively. i -x i With Gbest i -x i The acceptance probability of is shown in formulas (22) and (23). w represents the total number of iterations, t represents the number of current iterations, t∈(1,w); When Gbest i When convergence is reached, the calculated particle position is the optimal position, that is, the optimal path of a gene subsequence; Repeat the PSO algorithm solution process for the Z individual genes in the initial population to obtain the optimal path for each individual gene, that is, Z individuals; S335: Use genetic algorithm to update individual genes in the initial population: Select the operator: Using the elitist model, the best individual among the Z individuals obtained by the PSO algorithm in S334 is retained, and the other v-1 individuals are selected according to the roulette method based on the fitness value, and γ-1 individuals are selected from the remaining Z-1 individuals and the best individual to form γ individuals; Use γ individuals to generate offspring: Randomly select two individuals from y individuals as parent individuals, and select one of the sequential crossover operator and the position-based crossover operator to generate the selection parameter λ of the offspring, as shown in formula (24), The symbol ⊙ indicates that if the given random number is greater than λ, the sequential crossover operator A is selected. ox Otherwise, the position-based crossover operator Apbx is used, symbol It means that the selected crossover operator is used to cross the parents P1 and P2 to generate new offspring, and C1 and C2 represent the two offspring generated by the crossover of the parent individuals; Traverse all individuals among the γ individuals until the number of offspring generated and the sum of the γ individuals are equal to the initial population size, and stop generating offspring; Repeat the iterative steps S333-S335 until the total distance of the UAVs converges, stop updating individual genes, and obtain the optimal path.

7. The truck-UAV multi-target collaborative delivery and collection express delivery path planning method according to claim 1 is characterized in that: The customer point parameters include customer point coordinates, the services required by each customer point, the weight of the express delivery required by each customer node that needs delivery services, and the visited status; wherein the visited status is a binary variable, taking a value of one or zero.

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