CN115061492A - Campus takeout distribution system and progressive three-dimensional space path planning method - Google Patents
Campus takeout distribution system and progressive three-dimensional space path planning method Download PDFInfo
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- CN115061492A CN115061492A CN202210698703.0A CN202210698703A CN115061492A CN 115061492 A CN115061492 A CN 115061492A CN 202210698703 A CN202210698703 A CN 202210698703A CN 115061492 A CN115061492 A CN 115061492A
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Abstract
The invention discloses a campus takeout delivery system and a progressive three-dimensional space path planning method, relates to an unmanned aerial vehicle delivery technology, and provides a scheme aiming at the problem that a campus takeout delivery scene in the prior art lacks technical support. Dividing a path between the starting point of the unmanned aerial vehicle and the user address into three sections for progressive planning; the first section is completed through an artificial potential field algorithm and an electronic map together, the second section is completed through an artificial potential field algorithm and a GPS function together, and the third section is completed through a Bluetooth RSSI signal three-point positioning method. The method has the advantages that the route planning is set completely aiming at the takeaway delivery scene of the school district, and the respective advantages of the electronic map, the GPS and the Bluetooth can be fully exerted through three-section progressive route distribution. Based on artifical potential field algorithm advances work on a large scale, carries out accurate being close to based on the bluetooth, can also carry out real-time early warning to dangerous distance simultaneously. The platform real-time monitoring is not needed, the system setting cost is greatly reduced, and the system is particularly suitable for the takeout industry of Maoliwei micro-thin.
Description
Technical Field
The invention relates to an unmanned aerial vehicle distribution technology, in particular to a campus takeout distribution system and a progressive three-dimensional space path planning method.
Background
A drone is an unmanned aircraft that is operated by a radio remote control device and a self-contained program control device. The civil application of the unmanned aerial vehicle to the fields of aerial photography, agriculture, plant protection, self-photographing, express transportation, air crash rescue, wild animal observation, infectious disease monitoring, surveying and mapping, news report, power inspection, disaster relief, film and television shooting and the like at present greatly expands the application of the unmanned aerial vehicle.
Chinese patent CN 205809702U discloses an unmanned aerial vehicle logistics system, which includes a cloud server, an unmanned aerial vehicle, a delivery control terminal and a receiving control terminal; the goods receiving control terminal is in wireless connection with the cloud server and wirelessly transmits the demand information to the cloud server; the cloud server is in wireless connection with the delivery control terminal, the cloud server wirelessly transmits the demand information to the delivery control terminal, and the delivery control terminal confirms the demand information and transmits a flight control instruction to the unmanned aerial vehicle through the cloud server; unmanned aerial vehicle and high in the clouds server wireless connection to having the goods and carrying on the device of puting in, the goods carries on the main control chip electric connection who puts in device and unmanned aerial vehicle, and unmanned aerial vehicle flies to predetermineeing the place according to flight control instruction, and the goods carries on the device of puting in and puts in the goods to predetermineeing the place. This system accessible unmanned aerial vehicle transports the goods, saves the cost, simplifies and transports the step.
When the unmanned aerial vehicle takes off to reach the delivery point, the unmanned aerial vehicle flies back to the flying point from the delivery point, and the whole process needs a remote platform for intervention control. There is the control demand in space such as mountain area is wide, and the barrier height change is less moreover, and is lower to unmanned aerial vehicle autonomous control demand. The platform-controlled equipment is very costly and is not suitable for use in take-away delivery scenarios in parks.
Within various parks, such as campuses, factories, etc., the area is not large, but the buildings are numerous. The buildings are tall and the mutual distance is not large. The unmanned aerial vehicle is required to have autonomous regulation and control and navigation capability, and the unmanned aerial vehicle does not have corresponding technical support in the application scene of delivery outside a park at present.
Disclosure of Invention
The invention aims to provide a campus takeout delivery system and a progressive three-dimensional space path planning method, so as to solve the problems in the prior art.
The invention relates to a progressive three-dimensional space path planning method, which divides a path between an unmanned aerial vehicle flying point and a user address into three sections for progressive planning;
planning a first section of path: acquiring a plane map of the cruising height of the school zone and a user address; establishing a coordinate system by taking the flying point as an origin, marking the building higher than the cruising height as an obstacle, setting the projection coordinate of the building gate where the user address is located on a plane map as a target value, and calculating the current optimal path to reach the upper part of the building gate by utilizing an artificial potential field algorithm; descending to the height of a user address floor above a building gate;
a second section of path planning step: informing a user to start a GPS function of the mobile phone and acquiring GPS information of the mobile phone of the user; re-establishing a coordinate system by taking the current position of the unmanned aerial vehicle as an original point, setting the GPS position of a user mobile phone as a target position, and calculating the current optimal path to reach the position of the user within a certain range by using the artificial potential field algorithm again;
a third section of path planning step: informing a user to start a Bluetooth function of the mobile phone, finally positioning the mobile phone based on a Bluetooth RSSI signal three-point positioning method, and setting a place which is away from the mobile phone by a certain distance as a path terminal of the unmanned phone for hovering;
the user address comprises information of a gate of a building, information of a floor and information of a room.
The cruising height is 50 m.
The artificial potential field algorithm is provided with a dangerous distance early warning module, a sigmoid activation function is used for continuously predicting dangerous distances, and when the prediction probability is larger than 0.5, the attractive force value in the artificial potential field is equal to the repulsive force value.
The artificial potential field algorithm is provided with a deadlock prevention module, and when a path is unavailable and deadlock is caused in the advancing process of the unmanned aerial vehicle, a gain coefficient is added to the attraction force in an angle mode.
In the artificial potential field algorithm, a gravitational constant is k (att), a repulsive constant is k (rep), and the relationship is satisfied:
the self-adjusting weight function wf of the repulsion constant is:
wherein dist is the total distance of the moving path, mindist is the minimum total distance under different values of k (rep), maxsafedist is the maximum value in the shortest distances between the unmanned aerial vehicle and each obstacle under different values of k (rep), and safedis is the shortest distance between the unmanned aerial vehicle and each obstacle;
let the value of k (rep) when wf is the minimum be the value of the repulsion constant.
In the second path planning, the unmanned aerial vehicle utilizes ultrasonic waves to calculate the distance between the unmanned aerial vehicle and the obstacle in real time, when the distance is smaller than a preset value, the corresponding obstacle is added into a coordinate system, and the current optimal path is calculated by means of the artificial potential field algorithm again.
The certain distance between the hovering end point of the unmanned aerial vehicle and the position of the mobile phone is 1 m.
The invention relates to a campus takeout distribution system which comprises a user mobile phone, a plurality of unmanned aerial vehicles and unmanned aerial vehicle storage points;
the unmanned aerial vehicle storage point is used for storing the plurality of unmanned aerial vehicles and charging any unmanned aerial vehicle; the unmanned aerial vehicle system is also used for acquiring a user address input by a take-out, matching an idle unmanned aerial vehicle and sending the user address and a plane map of the cruise height of the school zone to the matched unmanned aerial vehicle;
after the matched unmanned aerial vehicle is put into the takeaway by a take-out clerk, the unmanned aerial vehicle flies to a user address through the progressive three-dimensional space path planning method and hovers;
and the user mobile phone is used for communicating with the matched unmanned aerial vehicle.
The unmanned aerial vehicle records the whole flight track, and returns to the unmanned aerial vehicle storage point through the original route after the takeout.
The unmanned aerial vehicle rises to a height twice as high as the cruising height after taking out the takeout, and if the height is judged to have no barrier in the range of the park, the unmanned aerial vehicle linearly returns to the position above the storage point of the unmanned aerial vehicle and then vertically lands; and if the obstacle exists, calculating an optimal path returning to the storage point of the unmanned aerial vehicle by using an artificial potential field algorithm.
The campus takeout distribution system and the progressive three-dimensional space path planning method have the advantages that the path planning is set completely aiming at a campus takeout distribution scene, and the respective advantages of an electronic map, a GPS (global positioning system) and Bluetooth can be fully exerted through three-section progressive path distribution. Based on artifical potential field algorithm advances work on a large scale, carries out accurate being close to based on the bluetooth, can also carry out real-time early warning to dangerous distance simultaneously. The platform real-time monitoring is not needed, the system setting cost is greatly reduced, and the system is particularly suitable for the takeout industry of MaoLifei Mingshenji.
Drawings
Fig. 1 is a schematic elevation view of a working process of the progressive three-dimensional space path planning method of the present invention.
Fig. 2 is a schematic plan view of a working process of the progressive three-dimensional space path planning method of the present invention.
Detailed Description
Under the big background of current epidemic situation environment, take-out can not be sent to the campus, needs the student to get by oneself. Adopt unmanned aerial vehicle to be applied to campus takeaway, adopt man-machine cooperation delivery mode, can convenience of customers draws the takeaway, also can avoid the direct contact of takeaway person and user. The user places an order through the mobile phone app, and the take-out personnel send the take-out to the campus unmanned aerial vehicle storage point to start the distribution work disclosed by the invention.
At first, match idle unmanned aerial vehicle, give unmanned aerial vehicle with user address information, directly send takeaway to customer dormitory windowsill by unmanned aerial vehicle, customer draws takeaway through showing the order two-dimensional code or directly through face identification to unmanned aerial vehicle. After takeaway, unmanned aerial vehicle will fly back to deposit some fill electric pile and charge.
The specific work flow is shown in fig. 1 and 2.
The first section of path planning comprises the following specific steps: firstly, acquiring a high-altitude plane map of the cruising height of the school zone, wherein the cruising height can be configured according to different school zone environmentsFor example, 50 m. Establishing a two-dimensional coordinate axis by taking the flying point as an origin, setting a building higher than 50m as an obstacle, taking the overhead position of 50m of the building gate where the user is located as a target value, establishing a repulsive force potential field at the position point of the obstacle by adopting an artificial potential field algorithm, and establishing a gravitational force potential field at the target position. Then
Wherein U (overall) is the total potential field; urep (q) is the repulsive potential field; uatt (q) is the gravitational potential field; f (q) is a resultant force of the attractive force and the repulsive force;
is the gradient of the total potential field; q is a position.
Urep (q) is inversely proportional to the distance between the drone and the target point, and uett (q) is proportional to the distance between the drone and the target point. And F (q) obtaining the direction of each step so as to find out the optimal path.
The parameter selection of the artificial potential field algorithm comprises the following steps: k (att), k (rep), step, Obstacles _ dis, danger _ dist. Step is the step length, and a map is taken to correspond to a value of 2m in the actual life; obstacles _ dis is an obstacle influence distance, and a value of a map corresponding to the radius of an actual life obstacle is 10 times; the danger _ dist is a dangerous distance, so that the safety problem of collision with the obstacle is avoided, and a value which corresponds to 2 times of the radius of the obstacle in actual life is taken from a map; k (att) is the gravitational constant, k (rep) is the repulsive constant, and both satisfy the relationship:
since the repulsion constant k (rep) is an unknown variable, both security and path minimization need to be considered for the calculation of the k (rep) value. Thus two auxiliary parameters, dist and safedist, are set. dist is the total distance the drone has traveled from the starting point to the destination; safedist is the shortest distance between the drone and all obstacles. Considering that safe dist is as large as possible, ensuring that the dist is as small as possible, and adopting a self-adjusting weight function wf to perform self-adaptive adjustment:
the mindist is the minimum total distance under different values of k (rep), the maxsafedist is the maximum value in the shortest distances between the unmanned aerial vehicle and each obstacle under different values of k (rep), and the safedis is the shortest distance between the unmanned aerial vehicle and each obstacle. According to the number of obstacles on the map, a self-adjusting method is set, and the k (rep) value is automatically updated along with the change of the unmanned plane map. And when the self-adjusting weight function wf is calculated to have a minimum value, the corresponding k (rep) value is the current optimal value of the repulsion constant.
A dangerous distance early warning module is added into the artificial potential field algorithm, a sigmoid activation function is adopted to weight danger _ dist to be 1, and prediction is carried out after dist is weighted to be-1. And if the prediction probability is larger than 0.5, activating an emergency risk avoiding function to enable the attraction value in the artificial potential field to be equal to the repulsion value. In order to prevent the unmanned aerial vehicle from being deadlocked due to the fact that the direction of the attractive force is opposite to the direction of the repulsive force, the artificial potential field algorithm is provided with a deadlock prevention module, and when the unmanned aerial vehicle cannot enter deadlock due to the fact that a path is unavailable in the advancing process, a gain coefficient is added to the attractive force in an angle mode.
And after the unmanned aerial vehicle arrives at the position right above the gate by 50m, the unmanned aerial vehicle is adjusted to the corresponding height according to the floor information filled in the user app.
The second section of path planning comprises the following specific steps: the drone sends information to the user, asking him to turn on the cell phone GPS and walk to the balcony. The unmanned aerial vehicle obtains the longitude and latitude information of the mobile phone GPS at the moment, and establishes a coordinate system again by taking the current longitude and latitude of the unmanned aerial vehicle as an origin. And setting the position of the mobile phone as a target position. And calculating the optimal path by using the artificial potential field again. Meanwhile, the distance between the unmanned aerial vehicle and the obstacle is calculated in real time by utilizing ultrasonic waves, and when the distance is smaller than a preset value, the position of the obstacle is added into a coordinate system to regenerate a new path. Finally, obstacle avoidance and path planning of the unmanned aerial vehicle are achieved, and the unmanned aerial vehicle can reach a dormitory balcony within a certain range.
The third section of path planning comprises the following specific steps: because civilian GPS precision has the meter level error, unmanned aerial vehicle still has certain distance with the balcony this moment. Although military GPS can achieve centimeter-level errors, it is obviously not directly transferable to civilian areas and is particularly not suitable for school district takeaway delivery scenarios. At this moment, unmanned aerial vehicle acquires more accurate positional information through connecting user's bluetooth. The mobile phone is positioned by adopting a three-point positioning method based on Bluetooth RSSI signals, beacons are simulated by a mobile phone, and the RSSI of the beacons is obtained by sending and broadcasting. The distance between the beacon and the mobile phone is calculated by acquiring three beacons, and the position coordinate of the mobile phone can be acquired by utilizing a three-point positioning algorithm. After the unmanned aerial vehicle acquires the position of the mobile phone, in order to avoid accidents caused by direct impact on the mobile phone or a user, the unmanned aerial vehicle flies to a certain distance away from the mobile phone and hovers, and waits for the user to take a meal.
The unmanned aerial vehicle can automatically return to the storage point after the user finishes taking meals based on at least the following two methods:
a) the unmanned aerial vehicle records the track of marcing in the process of the three previous steps, and when the unmanned aerial vehicle reaches take-out, the unmanned aerial vehicle returns on the original way according to the previous track of marcing.
b) When the unmanned aerial vehicle flies to the 100m high altitude, an electronic map of the 100m high altitude is given in advance by the storage point, and if no obstacle exists, the straight line between the starting point and the end point returns. If the obstacle exists, the return path is calculated by using the progressive three-dimensional space path planning method.
It will be apparent to those skilled in the art that various other changes and modifications may be made in the above-described embodiments and concepts and all such changes and modifications are intended to be within the scope of the appended claims.
Claims (10)
1. A progressive three-dimensional space path planning method is characterized in that a path from a flying point of an unmanned aerial vehicle to a user address is divided into three sections for progressive planning;
planning a first section of path: acquiring a plane map of the cruising height of the school zone and a user address; establishing a coordinate system by taking the flying point as an origin, marking the building higher than the cruising height as an obstacle, setting the projection coordinate of the building gate where the user address is located on a plane map as a target value, and calculating the current optimal path to reach the upper part of the building gate by utilizing an artificial potential field algorithm; descending to the height of a user address floor above a building gate;
a second path planning step: informing a user to start a GPS function of the mobile phone and acquiring GPS information of the mobile phone of the user; re-establishing a coordinate system by taking the current position of the unmanned aerial vehicle as an original point, setting the GPS position of a user mobile phone as a target position, and calculating the current optimal path to reach the position of the user within a certain range by using the artificial potential field algorithm again;
a third stage of path planning step: informing a user to start a Bluetooth function of the mobile phone, finally positioning the mobile phone based on a Bluetooth RSSI signal three-point positioning method, and setting a place which is away from the mobile phone by a certain distance as a path terminal of the unmanned phone for hovering;
the user address comprises information of a gate of a building, information of a floor and information of a room.
2. The progressive three-dimensional spatial path planning method according to claim 1, wherein the cruising altitude is 50 m.
3. The progressive three-dimensional space path planning method according to claim 1, wherein the artificial potential field algorithm is provided with a dangerous distance early warning module, a sigmoid activation function is used for continuously predicting dangerous distances, and when the prediction probability is greater than 0.5, the attraction value in the artificial potential field is equal to the repulsion value.
4. The progressive three-dimensional space path planning method according to claim 1, wherein the artificial potential field algorithm is provided with an anti-deadlock module, and when the path is unavailable and enters deadlock in the process of advancing the unmanned aerial vehicle, a gain coefficient is added to the gravitation in an angle mode.
5. The progressive three-dimensional space path planning method according to claim 1, wherein in the artificial potential field algorithm, a gravitational constant is k (att), a repulsive constant is k (rep), and a relationship is satisfied:
the self-adjusting weight function wf of the repulsion constant is:
wherein dist is the total distance of the moving path, mindist is the minimum total distance under different values of k (rep), maxsafedist is the maximum value in the shortest distances between the unmanned aerial vehicle and each obstacle under different values of k (rep), and safedis is the shortest distance between the unmanned aerial vehicle and each obstacle;
let the value of k (rep) when wf is minimum be the value of the repulsive constant.
6. The progressive three-dimensional space path planning method according to claim 1, wherein in the second path planning, the unmanned aerial vehicle uses ultrasonic waves to calculate the distance between the unmanned aerial vehicle and the obstacle in real time, when the distance is smaller than a preset value, the corresponding obstacle is added into a coordinate system, and the current optimal path is calculated by reusing an artificial potential field algorithm.
7. The progressive three-dimensional space path planning method according to claim 1, wherein the hovering end point of the unmanned aerial vehicle is 1m away from the position of the mobile phone.
8. A campus takeout distribution system is characterized by comprising a user mobile phone, a plurality of unmanned aerial vehicles and unmanned aerial vehicle storage points;
the unmanned aerial vehicle storage point is used for storing the plurality of unmanned aerial vehicles and charging any unmanned aerial vehicle; the unmanned aerial vehicle system is also used for acquiring a user address input by a take-out, matching an idle unmanned aerial vehicle and sending the user address and a plane map of the cruise height of the school zone to the matched unmanned aerial vehicle;
after a takeaway is put into the takeaway by a takeaway, the matched unmanned aerial vehicle flies to a user address for hovering through the progressive three-dimensional space path planning method according to any one of claims 1 to 7;
and the user mobile phone is used for communicating with the matched unmanned aerial vehicle.
9. The campus takeaway delivery system of claim 8 wherein the drone records the entire flight path and returns to the drone deposit point on the original route after the takeaway.
10. The campus takeout distribution system of claim 8 wherein the drones, after takeout, rise to a height twice the cruising height, and if it is determined that there are no obstacles in the campus area, return straight to above the drones' storage points and then land vertically; and if the obstacle exists, calculating an optimal path returning to the storage point of the unmanned aerial vehicle by using an artificial potential field algorithm.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103995249A (en) * | 2014-06-11 | 2014-08-20 | 王立宁 | Wireless communication system and method for determining position of target object |
| WO2017041730A1 (en) * | 2015-09-09 | 2017-03-16 | 北京进化者机器人科技有限公司 | Method and system for navigating mobile robot to bypass obstacle |
| CN107436610A (en) * | 2017-07-31 | 2017-12-05 | 中南大学 | A kind of vehicle and robot delivery navigation methods and systems of intelligent outdoor environment |
| CN110641700A (en) * | 2019-08-23 | 2020-01-03 | 西南交通大学 | Community-oriented unmanned aerial vehicle distribution system and distribution method |
| CN111123984A (en) * | 2019-11-05 | 2020-05-08 | 东莞理工学院 | Unmanned aerial vehicle path planning method based on position and speed artificial potential field method |
| CN112684807A (en) * | 2019-10-18 | 2021-04-20 | 成都凯天电子股份有限公司 | Unmanned aerial vehicle cluster three-dimensional formation method |
| CN113534838A (en) * | 2021-07-15 | 2021-10-22 | 西北工业大学 | Improved unmanned aerial vehicle track planning method based on artificial potential field method |
| CN113961013A (en) * | 2021-11-10 | 2022-01-21 | 吉林省春城热力股份有限公司 | Unmanned aerial vehicle path planning method based on RGB-D SLAM |
-
2022
- 2022-06-20 CN CN202210698703.0A patent/CN115061492B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103995249A (en) * | 2014-06-11 | 2014-08-20 | 王立宁 | Wireless communication system and method for determining position of target object |
| WO2017041730A1 (en) * | 2015-09-09 | 2017-03-16 | 北京进化者机器人科技有限公司 | Method and system for navigating mobile robot to bypass obstacle |
| CN107436610A (en) * | 2017-07-31 | 2017-12-05 | 中南大学 | A kind of vehicle and robot delivery navigation methods and systems of intelligent outdoor environment |
| CN110641700A (en) * | 2019-08-23 | 2020-01-03 | 西南交通大学 | Community-oriented unmanned aerial vehicle distribution system and distribution method |
| CN112684807A (en) * | 2019-10-18 | 2021-04-20 | 成都凯天电子股份有限公司 | Unmanned aerial vehicle cluster three-dimensional formation method |
| CN111123984A (en) * | 2019-11-05 | 2020-05-08 | 东莞理工学院 | Unmanned aerial vehicle path planning method based on position and speed artificial potential field method |
| CN113534838A (en) * | 2021-07-15 | 2021-10-22 | 西北工业大学 | Improved unmanned aerial vehicle track planning method based on artificial potential field method |
| CN113961013A (en) * | 2021-11-10 | 2022-01-21 | 吉林省春城热力股份有限公司 | Unmanned aerial vehicle path planning method based on RGB-D SLAM |
Non-Patent Citations (1)
| Title |
|---|
| 甄然等: "一种自适应控制的人工势场的无人机路径规划算法", 《无线电工程》 * |
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