CN107657412B - Unmanned aerial vehicle and automobile combined distribution system and distribution method for remote areas - Google Patents
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Abstract
The invention discloses a remote area-oriented unmanned aerial vehicle and automobile combined distribution method, which comprises the following steps: 1) The method comprises the steps that an automobile carries packages and unmanned aerial vehicles loaded with the packages to a starting point of a delivery area, wherein the delivery area comprises a plurality of unmanned aerial vehicle delivery points and automobile delivery points; 2) The unmanned aerial vehicle takes off from a starting point or halfway, and returns to a delivery area end point after delivering packages to specified unmanned aerial vehicle delivery points, wherein each unmanned aerial vehicle delivery point is delivered by at most one unmanned aerial vehicle; 3) After all unmanned aerial vehicles return to the voyage, loading the voyage to the automobile, and completing distribution. According to the invention, the geographical distribution condition of the logistics distribution points in the remote area is considered, the logistics distribution points are classified, the unmanned aerial vehicle and the automobile are used for logistics distribution, the flight route of the unmanned aerial vehicle and the automobile is optimized, and the unmanned aerial vehicle and the automobile distribute packages simultaneously, so that the logistics transportation risk in the remote area can be greatly reduced, and the logistics cost and the transportation time are reduced.
Description
Technical Field
The invention relates to the technical field of intelligent express delivery, in particular to an unmanned aerial vehicle and automobile combined delivery system and a delivery method for remote areas.
Background
Along with the development of unmanned aerial vehicle technology, unmanned aerial vehicle's reliability and carrying capacity have obtained very big promotion, unmanned aerial vehicle's acquisition and use cost decline fast, and at present, unmanned aerial vehicle has been widely used in domestic field of china. In the express industry, unmanned aerial vehicles have been used for short-distance parcel delivery service in cities, but there are several obvious constraint factors for this type of service: firstly, unmanned aerial vehicle package distribution in cities faces the problems of responsible urban building and tree distribution, collision avoidance and safety of unmanned aerial vehicles, and is a great challenge; in addition, the country has set a series of regulations for the low-altitude flight of unmanned aerial vehicles, and unmanned aerial vehicle package delivery in urban centers faces the problem of 'low flight'.
The economic development level of vast remote areas in China is relatively low, road traffic infrastructure construction lags, and parcel express service in the areas faces a plurality of problems: firstly, the package express delivery business volume is not high, and the distribution is directly carried out by using an automobile, so that the logistics cost is too high; secondly, the areas are often blocked by mountains and rivers, the road is roundabout and the road condition is poor, and the safety problem of parcel delivery by using automobiles is difficult to ensure. In remote areas with relatively fewer population, the low-altitude flight control of the unmanned aerial vehicle is relatively loose, and the problems of collision prevention and flight safety are fewer. Therefore, the invention comprehensively uses the unmanned aerial vehicle, the automobile and the parcel receiving box to carry out logistics distribution in remote areas.
Chinese patent document CN201620108719.1 proposes an unmanned aerial vehicle express delivery system, this system comprises unmanned aerial vehicle and the express delivery cabinet of distributing in each site, and there is the device of fixed unmanned aerial vehicle undercarriage at express delivery cabinet top, sets up the mark that has the directionality on the fixing device, and the unmanned aerial vehicle below is equipped with the image recognition system that discerns this direction and marks and control it to drop according to the direction of predetermineeing. The Chinese patent document CN201510240751.5 provides a cargo transportation method and system of an unmanned aerial vehicle, wherein landing recognition patterns are arranged on the ground, the unmanned aerial vehicle flies to the upper air to automatically perform image recognition search, if the search is successful, the unmanned aerial vehicle lands, and package transportation is performed. Chinese patent document CN201520244456.2 provides an automatic receiving system for an unmanned aerial vehicle express, which includes a receiving trolley, a lifting platform and a receiving window. The above patent documents focus on the construction of the unmanned aerial vehicle express system, and do not consider the distribution situation of logistics distribution points in remote areas. Chinese patent document CN201520606406.4 proposes an intelligent express delivery system, after the logistics car arrives at a stop, the parcel is manually fixed on an unmanned aerial vehicle, the unmanned aerial vehicle flies according to an optimized flight path, and the parcel is placed into an intelligent cabinet, and meanwhile, a customer is notified to pick up the parcel. The logistic vehicle of the above patent document is only stopped at a stop point and is in a stationary state, and does not deliver packages in parallel with the unmanned aerial vehicle, and does not relate to how to specifically optimize the flight path of the unmanned aerial vehicle, which is disadvantageous in terms of the logistic delivery cost and the logistic delivery time, and in addition, the distribution of the logistic delivery points in remote areas is not considered in the patent document.
Disclosure of Invention
The invention aims at solving the technical defects in the prior art and provides an unmanned aerial vehicle and automobile combined distribution system and a distribution method for remote areas.
The technical scheme adopted for realizing the purpose of the invention is as follows:
a combined delivery method of unmanned plane and automobile facing remote areas comprises the following steps:
1) The method comprises the steps that an automobile carries packages and unmanned aerial vehicles loaded with the packages to a starting point of a delivery area, wherein the delivery area comprises a plurality of unmanned aerial vehicle delivery points and automobile delivery points;
2) The method comprises the steps that an automobile drives to a distribution area terminal according to an automobile distribution path, meanwhile, the automobile distribution points are traversed and distributed, according to an unmanned aerial vehicle distribution strategy, an unmanned aerial vehicle takes off from a starting point or in the middle, packages are distributed to a designated unmanned aerial vehicle distribution point and then returns to the distribution area terminal, and each unmanned aerial vehicle distribution point is distributed by at most one unmanned aerial vehicle;
3) After all unmanned aerial vehicles return to the voyage, loading the voyage to the automobile, and completing distribution.
The unmanned aerial vehicle delivery point is a delivery point which needs detour transportation or has a delivery road grade of four or below.
The unmanned aerial vehicle is provided with an ejection device to assist take-off or directly take-off and land vertically.
The package on be provided with the RFID label, unmanned aerial vehicle delivery point and car delivery point include parcel receiving box, RFID reader and wireless communication module respectively, put in the parcel to the parcel receiving box after, the RFID reader reads the parcel information to send the arrival notice to the user.
And sending a package arrival notification to the mobile phone of the user through a 3G or 4G wireless communication technology.
The calculation method of the automobile distribution path comprises the following steps:
1) Forming a set of starting points, end points and automobile distribution points; the car needs to traverse each point in the set once;
2) The automobile distribution path problem is converted into a travel business problem, and a heuristic algorithm is used for solving the distribution path with the shortest distance;
3) Deleting the connection paths of the designated starting point and the designated end point, and obtaining the rest paths as the automobile distribution paths.
The calculation method of the unmanned aerial vehicle distribution strategy comprises the following steps:
and taking the number and the maximum flight distance of the unmanned aerial vehicles as constraints, only delivering each unmanned aerial vehicle delivery point by one unmanned aerial vehicle, taking off the unmanned aerial vehicle from a starting point or an automobile delivery point, delivering the corresponding unmanned aerial vehicle delivery point, and returning to a delivery area for landing.
The unmanned aerial vehicle distribution strategy is solved based on a multi-objective optimization algorithm of decomposition and elite strategy, and the algorithm is characterized in that:
1) Decomposing the optimization target into two sub-problems, namely, the minimum number of unmanned aerial vehicles are used, and the shortest distribution path of the unmanned aerial vehicles;
2) Generating a flight path population with more than 100 paths of the unmanned aerial vehicle, wherein the weight vector of each flight path is 2 real numbers which are more than 0 and less than 1; and corresponding neighborhood flight paths, wherein the number of the neighborhood paths is more than or equal to 5; the flight path population is provided with an objective function reference point;
3) Based on the flight path of the unmanned aerial vehicle and the maximum flight distance of the unmanned aerial vehicle as constraint conditions, sub-path division is carried out on the flight path, so that the length of each sub-path does not exceed the maximum flight distance of the unmanned aerial vehicle, and the number of unmanned aerial vehicle distribution points contained in the sub-paths and the number of the sub-paths can be obtained; generating a corresponding objective function value of each flight path; taking the flight path with the smallest objective function value as an elite path, and updating an objective function reference point;
4) Calculating chebyshev values of all flight paths in the neighborhood where the elite path is located, wherein each flight path corresponds to a real weight value and an objective function value, and the corresponding chebyshev value = max { real weight value } (objective function value-value of objective function reference point) }; when the Chebyshev value of the elite path is less than or equal to that of the neighborhood flight path, replacing other flight paths in the neighborhood with the elite path, and updating the unmanned plane flight path population to realize an elite strategy;
5) Setting crossover and variation probability, wherein the crossover probability is set to be 0.7-0.9, the variation probability is set to be 0.1-0.15, and performing crossover-like operation and reverse sequence variation operation on all flight paths to improve the diversity of the flight paths;
6) Calculating the objective function value of each flight path of the flight path population after crossing and mutation, finding out the flight path with the minimum objective function value as an elite path, and updating the objective function reference point;
7) Calculating chebyshev values of all flight paths of the neighborhood where the elite path is located, and when the chebyshev values of the elite path are less than or equal to the chebyshev values of the neighborhood flight paths, replacing the neighborhood flight paths with the elite path, and updating the flight path population of the unmanned aerial vehicle to realize elite strategy;
8) Returning to the step 5), carrying out loop iteration, and calculating the flight path of the optimal unmanned aerial vehicle to obtain the number of unmanned aerial vehicles and the distribution track of each unmanned aerial vehicle.
In an optimization model of the unmanned aerial vehicle distribution strategy, the number of logistics distribution points is n, the designated starting point and the designated end point are respectively recorded as 0 and n+1, the number of available unmanned aerial vehicles is m frames, and D ij Is the distance of the distribution path (i, j), F u Is the maximum flight distance of the unmanned aerial vehicle of the u frame; the optimization model is as follows:
1)
2)
3)
4)
5)
6)
7)
8)
9)x uij ＝{0,1}
wherein, the formula (1) is an objective function 1, and the number of unmanned aerial vehicles used is the minimum; equation (2) is an objective function 2, and the delivery path of the unmanned aerial vehicle is the shortest; the meaning of equation (3) is that the unmanned aerial vehicle is slaveTaking off from a designated starting point; the meaning of formula (4) is that the unmanned aerial vehicle drops at a specified end point; the meaning of equation (5) is that for any one unmanned aerial vehicle, the distance of delivery does not exceed its maximum flight distance; the meaning of equation (6) is that the number of aircraft arriving at the destination does not exceed the number of existing unmanned aircraft; the meaning of the formula (7) is that at most one unmanned aerial vehicle arrives at any one logistics distribution point, and the meaning of the formula (8) is that at most one unmanned aerial vehicle leaves at any one logistics distribution point; equation (9) is a decision variable, x when the u-th unmanned aircraft passes through the delivery path (i, j) uij The value is 1.
Unmanned aerial vehicle and car combination formula delivery system towards remote area, including the car for the unmanned aerial vehicle of delivery parcel and contain the delivery area of a plurality of delivery points, the delivery area including car delivery point and unmanned aerial vehicle delivery point, be provided with the RFID label on the parcel, the delivery point including parcel receiving box, RFID reader and wireless communication module, put in the parcel behind the parcel receiving box, RFID reader reads parcel information and sends the arrival notice to the user through wireless communication module, its characterized in that is provided with the air park in the terminal point department of delivery area.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, the geographical distribution condition of the logistics distribution points in the remote area is considered, the logistics distribution points are classified, the unmanned aerial vehicle and the automobile are used for logistics distribution, the flight route of the unmanned aerial vehicle and the automobile is optimized, and the unmanned aerial vehicle and the automobile distribute packages simultaneously, so that the logistics transportation risk in the remote area can be greatly reduced, and the logistics cost and the transportation time are reduced.
Drawings
Fig. 1 is a schematic diagram of a combined unmanned aerial vehicle and automobile distribution system for remote areas.
FIG. 2 is a schematic illustration of a distribution of logistics distribution points.
Fig. 3 is a schematic illustration of a route for parallel delivery of packages by a car-drone.
Detailed Description
The invention is described in further detail below with reference to the drawings and the specific examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The unmanned aerial vehicle and automobile combined delivery system facing remote areas comprises an automobile 1, an unmanned aerial vehicle 2 used for carrying packages and a delivery area comprising a plurality of delivery points, wherein the delivery area comprises an automobile delivery point and an unmanned aerial vehicle delivery point, RFID tags 31 are arranged on the packages 3, the delivery points comprise package receiving boxes 4, RFID readers and wireless communication modules, and after the packages are put in the package receiving boxes, the RFID readers read package information and send arrival notices to users. Wherein an apron is provided at the end of the delivery zone. The wireless communication module is a 3G or 4G wireless communication module to send a package arrival notification to the user mobile phone 5.
The unmanned aerial vehicle lower part be provided with many storehouse goods storehouse and parcel ejection device, this mechanism such as the ejection formula discharge of delivery unmanned aerial vehicle and goods is similar to prior art, does not here develop the description again.
According to the invention, the automobiles and the unmanned aerial vehicles are combined, classified delivery in a certain area is realized, the speed is high, the transportation cost and transportation risk caused by poor road conditions or excessive detouring caused by large mountain and river in remote mountain areas are reduced, the diameter of the delivery area is 10-20km, the organic combination is realized by reasonably dividing the area, the delivery efficiency and the safety are ensured, meanwhile, the unmanned aerial vehicles supply energy for batteries, and the unmanned aerial vehicle charging station is arranged on the automobiles. The method is carried out by dividing the area and time, so that the total effective endurance of the unmanned aerial vehicle is enhanced.
The invention discloses a remote area-oriented unmanned aerial vehicle and automobile combined distribution method, which comprises the following steps of:
1) The method comprises the steps that an automobile carries packages and unmanned aerial vehicles loaded with the packages to a starting point of a delivery area, wherein the delivery area comprises a plurality of unmanned aerial vehicle delivery points and automobile delivery points;
2) The method comprises the steps that an automobile drives to a delivery area terminal according to an automobile delivery path, meanwhile, the automobile delivery points are traversed and delivered, an unmanned aerial vehicle takes off from a starting point or halfway and delivers packages to a designated unmanned aerial vehicle delivery point and returns to the delivery area terminal, and each unmanned aerial vehicle delivery point is at most delivered by one unmanned aerial vehicle;
3) After all unmanned aerial vehicles return to voyage, loading the unmanned aerial vehicles to the automobile, and completing distribution.
The remote area often has river and mountain blocking, and partial road traffic condition is poor, if traditional automobile delivery package is adopted, certain transportation potential safety hazards exist, circuitous transportation is needed, if the circuitous transportation distance/the linear transportation distance is more than or equal to 2, the logistic cost and time are obviously increased, therefore, the remote area has river and mountain blocking, the road traffic condition is poor, if the delivery road grade is four or below, logistic delivery points are distributed to unmanned aerial vehicles for delivery, and the rest delivery points are distributed to automobiles for delivery. Setting a designated starting point in a logistics distribution area, wherein the setting conditions of the starting point comprise: 1. the starting point is in an open and flat area, which is beneficial to vehicle parking and take-off and landing of the unmanned aerial vehicle; 2. more than one fixed logistics distribution points are arranged in the radius range of 10-20km from the starting point, such as nearly ten or more than ten; 3. the starting point is provided with a horizontal field on which the unmanned aerial vehicle falls, and the horizontal field is not less than 10 square meters. The starting point can be planned and constructed uniformly in advance by a logistics company in a certain distribution area according to the principle.
The number and the position information of the package delivery points are also favorable to be determined in advance, the optimization of the delivery route of the unmanned aerial vehicle is well made in advance, and the flight time of the unmanned aerial vehicle is reduced. In popular terms, planning, loading and distributing are performed in advance, cost is saved, and the unmanned aerial vehicle flies according to a set track, so that intermediate communication links are reduced, communication signal loss is avoided, and communication links are reduced.
The unmanned aerial vehicle be provided with ejection device or be provided with ejection device on the car in order to realize supplementary take-off, certainly, also can carry out the vertical lift, the parcel on be provided with the RFID label, unmanned aerial vehicle delivery point include parcel receiving box, RFID reader and wireless communication module, unmanned aerial vehicle or automobile driver put in the parcel behind the parcel receiving box with the parcel, RFID reader reads the parcel information to send the arrival notice to the user, if send the parcel arrival notice to the user's cell-phone through 3G or 4G wireless communication technique.
The calculation method of the automobile distribution path comprises the following steps:
1) Forming a set of starting points, end points and automobile distribution points; the car needs to traverse each point in the set once;
2) The automobile distribution path problem is converted into a traveling salesman problem (Traveling Salesman Problem), and a heuristic algorithm is used for solving the distribution path with the shortest distance;
3) Deleting the connection paths of the designated starting point and the designated end point, and obtaining the rest paths as the automobile distribution paths.
Wherein, the calculation method of the unmanned aerial vehicle distribution strategy comprises the following steps of,
1) Decomposing the optimization target into two sub-problems, namely, the minimum number of unmanned aerial vehicles are used, and the shortest distribution path of the unmanned aerial vehicles;
2) Generating a flight path population with more than 100 paths of the unmanned aerial vehicle, wherein the weight vector of each flight path is 2 real numbers which are more than 0 and less than 1; and corresponding neighborhood flight paths, wherein the number of the neighborhood paths is more than or equal to 5; the flight path population is provided with objective function reference points, namely the initial states of the objective function reference points of each flight path are the same;
for example, the objective function reference point is set to (4,60), wherein 4 represents 4 unmanned aerial vehicles, 60 represents a distribution path of 60km, and the value is used for providing reference for the subsequent update of the objective function value, and the sum of the two real numbers is 1, which respectively corresponds to two targets, namely, the minimum number of unmanned aerial vehicles and the shortest distribution path.
3) Based on the flight path of the unmanned aerial vehicle and the maximum flight distance of the unmanned aerial vehicle as constraint conditions, sub-path division is carried out on the flight path, so that the length of each sub-path does not exceed the maximum flight distance of the unmanned aerial vehicle, and the number of unmanned aerial vehicle distribution points contained in the sub-paths and the number of the sub-paths can be obtained; generating a corresponding objective function value of each flight path; taking the flight path with the smallest objective function value as an elite path, and updating an objective function reference point;
after one flight path is divided, the number of sub paths can be used for determining the number of unmanned aerial vehicles, and then the cruising distance of the unmanned aerial vehicles can be determined by accumulation according to the length of each sub path. The number of unmanned aerial vehicles used and the cruising distance of the unmanned aerial vehicle form an objective function value. If the starting point and the end point are respectively A and B, the unmanned plane starts from the starting point and returns to the end point, if the number of unmanned plane delivery points is 7, an integer random number sequence of 1-7 is generated, a flight path population traversing the 7 points is generated, such as 1-2-3-4-5-6-7,4-3-2-1-5-6-7,6-3-2-1-5-4-7, and the like, wherein the numbers represent the numbers of unmanned plane delivery points, 100 flight paths are obtained by randomly generating the integer random number sequence for 100 times, and the flight paths form a population. The real number is a number greater than 0 and less than 1, and the sum of the two real numbers is 1, such as 0.35 and 0.65,0.55 and 0.45; the neighborhood refers to other flight paths adjacent to a certain flight path (such as the aforementioned 6-3-2-1-5-4-7), such as the flight paths located in the 23 rd, 47,78 th of the population, and the neighborhood paths are used for the crossover and mutation operations of the subsequent steps so as to improve the optimizing capability of the algorithm.
For example, for a flight path of 6-3-2-1-5-4-7, first, it is analyzed whether the distance of sub-path A-6-B is greater than the maximum flight distance of the unmanned aircraft, if not, then it is analyzed whether sub-path A-6-3-2-B is greater than the maximum flight distance of the unmanned aircraft, if so, then sub-path A-6-3-B is the first path; then, based on the rest paths 2-1-5-4-7, the sub-path division is continued according to the method until all starting points or distribution points are divided. For example, if the final division result is A-6-3-B, A-2-1-5-4-B and A-7-B, 3 unmanned sub-paths are all calculated, and the objective function value corresponding to each path is calculated; and according to the geographic coordinates of the delivery point and the starting and ending point of each unmanned aerial vehicle, calculating Euclidean distance between the points, and determining the length of each sub-path. Assuming that the 3 sub-paths have lengths of 10km,12km and 15km, respectively, the unmanned plane distribution distance corresponding to the flight path is 10+12+15=37 km. The objective function value of the flight path is (3,37).
4) Calculating chebyshev values of all flight paths in the neighborhood where the elite path is located, wherein each flight path corresponds to a real weight value and an objective function value, and the corresponding chebyshev value = max { real weight value } (objective function value-value of objective function reference point) }; when the Chebyshev value of the elite path is less than or equal to that of the neighborhood flight path, replacing other flight paths in the neighborhood with the elite path, and updating the unmanned plane flight path population to realize an elite strategy;
5) Setting crossover and variation probability, wherein the crossover probability is set to be 0.7-0.9, the variation probability is set to be 0.1-0.15, and performing crossover-like operation and reverse sequence variation operation on all flight paths to improve the diversity of the flight paths;
the purpose of the cross variation is to disturb the flight path and improve the diversity of the flight path so as to find out the optimal flight path; the chebyshev value changes as the flight path changes.
6) Calculating the objective function value of each flight path of the flight path population after crossing and mutation, finding out the flight path with the minimum objective function value as an elite path, and updating the objective function reference point;
7) Calculating chebyshev values of all flight paths of the neighborhood where the elite path is located, and when the chebyshev values of the elite path are less than or equal to the chebyshev values of the neighborhood flight paths, replacing the neighborhood flight paths with the elite path, and updating the flight path population of the unmanned aerial vehicle to realize elite strategy;
8) Returning to step 5), performing loop iteration, calculating the flight paths of the optimal unmanned aerial vehicles, and obtaining the number of unmanned aerial vehicles and the distribution track of each unmanned aerial vehicle, wherein the termination condition is the maximum iteration number, and is generally set to 300-500 times.
Meanwhile, for different unmanned aerial vehicles and different tracks, in order to realize final better execution, the unmanned aerial vehicle comprises a plurality of unmanned aerial vehicles with different bearing capacities for parcel distribution.
Specifically, in the unmanned aerial vehicle distribution strategy, the number of logistics distribution points is n, the designated starting point and the designated end point are respectively recorded as 0 and n+1, the number of available unmanned aerial vehicles is m frames, and D ij Is the distance of the distribution path (i, j), F u Is the maximum flight distance of the unmanned aerial vehicle of the u frame; the optimization model is as follows:
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)x uij ＝{0,1}
wherein, the formula (1) is an objective function 1, and the number of unmanned aerial vehicles used is the minimum; equation (2) is an objective function 2, and the delivery path of the unmanned aerial vehicle is the shortest; the meaning of formula (3) is that the drone takes off from a designated origin; the meaning of equation (4) is that the drone is at the specified endpointLanding; the meaning of equation (5) is that for any one unmanned aerial vehicle, the distance of delivery does not exceed its maximum flight distance; the meaning of equation (6) is that the number of aircraft arriving at the destination does not exceed the number of existing unmanned aircraft; the meaning of the formula (7) is that at most one unmanned aerial vehicle arrives at any one logistics distribution point, and the meaning of the formula (8) is that at most one unmanned aerial vehicle leaves at any one logistics distribution point; equation (9) is a decision variable, x when the u-th unmanned aircraft passes through the delivery path (i, j) uij The value is 1.
The exemplary description, with reference to the drawings, is made to the specific embodiments:
1) Sorting of logistics distribution points. And distributing logistics distribution points with river and mountain barriers and poor road traffic conditions in remote areas to unmanned aerial vehicles for distribution, and distributing the rest distribution points to automobiles for distribution. As shown in fig. 2, the logistics distribution points 6, 7, 10, 11 are allocated to the automobiles, and the logistics distribution points 1, 2, 3, 4, 5, 8, 9 are allocated to the unmanned aerial vehicle.
2) And optimizing the automobile distribution path.
(1) Setting a designated starting point (A in fig. 2) and a designated ending point (B in fig. 2) of the automobile running, forming a set of distribution points, the starting point and the ending point, and traversing each point in the set once by the automobile;
(2) the automobile distribution path problem is converted into ase:Sub>A traveling business problem (Traveling Salesman Problem), and ase:Sub>A heuristic algorithm is used for solving the distribution path with the shortest distance, such as A-11-7-10-6-B-A in FIG. 3;
(3) on the basis of the optimized path, the connection paths with the designated starting point and the designated end point are deleted (as B-A in FIG. 2), and the rest paths (as A-11-7-10-6-B in FIG. 3) are used as optimized automobile distribution paths.
3) Unmanned aerial vehicle delivery path optimization.
(1) A designated starting point (A in fig. 2) and a designated ending point (B in fig. 2) of the automobile are respectively used as a flying spot and a landing spot of the unmanned aerial vehicle;
(2) and taking the number of unmanned aerial vehicles and the maximum flight distance as constraints, only delivering each logistics delivery point by one unmanned aerial vehicle, taking off the unmanned aerial vehicle from the take-off point (A in fig. 2), delivering the corresponding logistics delivery points, then landing at the landing point (B in fig. 2), taking the minimum number of unmanned aerial vehicles and the shortest delivery distance of the unmanned aerial vehicles as optimization targets, establishing an optimization model of the unmanned aerial vehicle delivery path, and determining the required number of unmanned aerial vehicles and the delivery route of the unmanned aerial vehicles by applying a multi-target heuristic optimization algorithm. As shown in fig. 3, 2 unmanned aerial vehicles are used for delivering packages, the delivery route of the 1 st unmanned aerial vehicle is a-9-3-2-B, and the delivery route of the 2 nd unmanned aerial vehicle is A-8-5-4-1-B.
The car-drone delivers packages in parallel. At the designated starting point (A in FIG. 3), the automobile and the unmanned plane simultaneously start package delivery, and the automobile carries out package delivery according to an optimized delivery path, such as A-11-7-10-6-B in FIG. 3; and the 2 unmanned aerial vehicles carry out package delivery according to the optimized delivery path, such as A-9-3-2-B and A-8-5-4-1-B in fig. 3, and after the delivery task is completed, the automobile and the unmanned aerial vehicles respectively return to the appointed terminal (B in fig. 3).
4) Package information notification. The unmanned aerial vehicle delivers the packages to the logistics points 1, 2, 3, 4, 5, 8 and 9, when a new package is delivered into the logistics point package receiving box, the RFID reader reads the electronic tag information of the package, and sends a package arrival notice to the mobile phone of the user through the wireless 3G and 4G communication technology.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.
Claims (7)
1. The unmanned aerial vehicle and automobile combined distribution method for the remote areas is characterized by comprising the following steps of:
1) The method comprises the steps that an automobile carries packages and unmanned aerial vehicles loaded with the packages to a starting point of a delivery area, wherein the delivery area comprises a plurality of unmanned aerial vehicle delivery points and automobile delivery points;
2) The method comprises the steps that an automobile drives to a distribution area terminal according to an automobile distribution path, meanwhile, the automobile distribution points are traversed and distributed, according to an unmanned aerial vehicle distribution strategy, an unmanned aerial vehicle takes off from a starting point or in the middle, packages are distributed to a designated unmanned aerial vehicle distribution point and then returns to the distribution area terminal, and each unmanned aerial vehicle distribution point is distributed by at most one unmanned aerial vehicle;
3) After all unmanned aerial vehicles return to the voyage, loading the voyage to the automobile, and completing distribution;
the calculation method of the automobile distribution path comprises the following steps:
1) Forming a set of starting points, end points and automobile distribution points; the car needs to traverse each point in the set once;
2) The automobile distribution path problem is converted into a travel business problem, and a heuristic algorithm is used for solving the distribution path with the shortest distance;
3) Deleting the connection paths of the designated starting point and the designated end point, wherein the rest paths are the automobile distribution paths;
taking the number and the maximum flight distance of unmanned aerial vehicles as constraints, each unmanned aerial vehicle delivery point is delivered by only one unmanned aerial vehicle, the unmanned aerial vehicle takes off from a starting point or an automobile delivery point, the corresponding unmanned aerial vehicle delivery point is delivered, then the unmanned aerial vehicle returns to a delivery area to land at a terminal, and an unmanned aerial vehicle delivery strategy is solved based on a multi-objective optimization algorithm of decomposition and elite strategy, and the algorithm is characterized in that:
1) Decomposing the optimization target into two sub-problems, namely, the minimum number of unmanned aerial vehicles are used, and the shortest distribution path of the unmanned aerial vehicles;
2) Generating a flight path population with more than 100 paths of the unmanned aerial vehicle, wherein the weight vector of each flight path is 2 real numbers which are more than 0 and less than 1; and corresponding neighborhood flight paths, wherein the number of the neighborhood paths is more than or equal to 5; the flight path population is provided with an objective function reference point;
3) Based on the flight path of the unmanned aerial vehicle and the maximum flight distance of the unmanned aerial vehicle as constraint conditions, sub-path division is carried out on the flight path, so that the length of each sub-path does not exceed the maximum flight distance of the unmanned aerial vehicle, and the number of unmanned aerial vehicle distribution points contained in the sub-paths and the number of the sub-paths can be obtained; generating a corresponding objective function value of each flight path; taking the flight path with the smallest objective function value as an elite path, and updating an objective function reference point;
4) Calculating chebyshev values of all flight paths in the neighborhood where the elite path is located, wherein each flight path corresponds to a real weight value and an objective function value, and the corresponding chebyshev value = max { real weight value } (objective function value-value of objective function reference point) }; when the Chebyshev value of the elite path is less than or equal to that of the neighborhood flight path, replacing other flight paths in the neighborhood with the elite path, and updating the unmanned plane flight path population to realize an elite strategy;
5) Setting crossover and variation probability, wherein the crossover probability is set to be 0.7-0.9, the variation probability is set to be 0.1-0.15, and performing crossover-like operation and reverse sequence variation operation on all flight paths to improve the diversity of the flight paths;
6) Calculating the objective function value of each flight path of the flight path population after crossing and mutation, finding out the flight path with the minimum objective function value as an elite path, and updating the objective function reference point;
7) Calculating chebyshev values of all flight paths of the neighborhood where the elite path is located, and when the chebyshev values of the elite path are less than or equal to the chebyshev values of the neighborhood flight paths, replacing the neighborhood flight paths with the elite path, and updating the flight path population of the unmanned aerial vehicle to realize elite strategy;
8) Returning to the step 5), carrying out loop iteration, and calculating the flight path of the optimal unmanned aerial vehicle to obtain the number of unmanned aerial vehicles and the distribution track of each unmanned aerial vehicle.
2. The remote area-oriented unmanned aerial vehicle and automobile combined delivery method of claim 1, wherein the unmanned aerial vehicle delivery point is a delivery point requiring detour transportation or delivery road class four or less.
3. The remote area-oriented unmanned aerial vehicle and automobile combined delivery method of claim 1, wherein the unmanned aerial vehicle is provided with an ejector to assist take-off or direct vertical take-off.
4. The unmanned aerial vehicle and automobile combined delivery method for remote areas according to claim 3, wherein the package is provided with an RFID tag, and the unmanned aerial vehicle delivery point and the automobile delivery point respectively comprise a package receiving box, an RFID reader and a wireless communication module, and after the package is put in the package receiving box, the RFID reader reads package information and sends an arrival notification to a user.
5. The remote area-oriented unmanned aerial vehicle and automobile combined delivery method of claim 4, wherein the package arrival notification is sent to the user's cell phone via 3G or 4G wireless communication technology.
6. The remote area-oriented unmanned aerial vehicle and automobile combined distribution method according to claim 1, wherein in the optimization model of the unmanned aerial vehicle distribution strategy, the number of logistics distribution points is n, the designated starting point and the designated end point are respectively marked as 0 and n+1, the number of available unmanned aerial vehicles is m, and D ij Is the distance of the distribution path (i, j), F u Is the maximum flight distance of the unmanned aerial vehicle of the u frame; the optimization model is as follows:
1)
2)
3)
4)
5)
6)
7)
8)
9)x uij ＝{0，1}
wherein, the formula (1) is an objective function 1, and the number of unmanned aerial vehicles used is the minimum; equation (2) is an objective function 2, and the delivery path of the unmanned aerial vehicle is the shortest; the meaning of formula (3) is that the drone takes off from a designated origin; the meaning of formula (4) is that the unmanned aerial vehicle drops at a specified end point; the meaning of equation (5) is that for any one unmanned aerial vehicle, the distance of delivery does not exceed its maximum flight distance; the meaning of equation (6) is that the number of aircraft arriving at the destination does not exceed the number of existing unmanned aircraft; the meaning of the formula (7) is that at most one unmanned aerial vehicle arrives at any one logistics distribution point, and the meaning of the formula (8) is that at most one unmanned aerial vehicle leaves at any one logistics distribution point; equation (9) is a decision variable, x when the u-th unmanned aircraft passes through the delivery path (i, j) uij The value is 1.
7. A delivery system for implementing a combined unmanned aerial vehicle and automobile delivery method for remote areas according to any one of claims 1 to 6, comprising an automobile, an unmanned aerial vehicle for carrying packages, and a delivery area comprising a plurality of delivery points, said delivery area comprising an automobile delivery point and an unmanned aerial vehicle delivery point, the packages being provided with RFID tags, said delivery points comprising a package receiving box, an RFID reader and a wireless communication module, the RFID reader reading package information and sending an arrival notification to a user via the wireless communication module after delivery of the packages to the package receiving box, characterized in that an apron is provided at the end of the delivery area.
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