CN-121998331-A - Intelligent transportation scheduling system and method based on aerial robot

CN121998331ACN 121998331 ACN121998331 ACN 121998331ACN-121998331-A

Abstract

The invention discloses an intelligent transportation scheduling system and method based on an aerial robot, which relate to the technical field of aerial robots and solve the problems that complex environments cannot be accurately adapted, transportation time can be accurately measured and calculated and robot resources can be efficiently configured; and combining the optimal path gradient and preset full-load/no-load flight parameters, respectively calculating the transportation time and the return time, and improving the accuracy of time estimation, wherein the measurement result is more fit with the real flight state of the unmanned aerial vehicle.

Inventors

YANG HUI
DU CHANGZHENG
LIANG XIAOTING
LIU LIANGKUN
Hu Longpeng
DUAN JINGPING
TANG JIANJUN
LIANG JIANHAO

Assignees

佛山康晋云充技术有限公司

Dates

Publication Date: 20260508
Application Date: 20260121

Claims (8)

1. The intelligent transportation scheduling method based on the aerial robot is characterized by comprising the following steps of: Step one, confirming a dispatching end point from the input dispatching information, confirming the transportation start points of different aerial robots, and confirming a flight path set associated with the different aerial robots according to the marked transportation start points and the dispatching end point; Step two, confirming path characteristics associated with different flight paths from a single-group flight path set according to different flight path sets associated with different aerial robots, and selecting an optimal path from the single-group flight path set according to the different path characteristics associated with the different flight paths; thirdly, confirming the transportation time associated with each group of optimal paths according to different optimal paths confirmed in each group of different flight path sets and preset flight parameters; And step four, confirming the dispatching quantity required by the dispatching terminal point from the dispatching information, synchronizing the total number of the requirements of the aerial robots according to the transportation quantity associated with each group of aerial robots, and then determining and executing the optimal dispatching logic according to the transportation time associated with different aerial robots.
2. The intelligent transportation scheduling method based on the aerial robot according to claim 1, wherein in the first step, the specific way of confirming the flight path set is as follows: according to the confirmed dispatching terminal point and the transportation starting points of different aerial robots, connecting the transportation starting points with the dispatching terminal point, and confirming the characteristic connecting lines associated with the corresponding aerial robots; Synchronously marking the characteristic connecting lines in a three-dimensional solid model, wherein the three-dimensional solid model is a preset model, confirming a plurality of two-dimensional planes of the characteristic connecting lines aiming at the characteristic connecting lines associated with the single aerial robot, confirming intersection points generated by the corresponding single two-dimensional planes and the outer surface of the three-dimensional solid model, vertically upwards translating X1m to confirm displacement points according to the confirmed intersection point positions, sequentially connecting the confirmed groups of displacement points, and confirming flight paths associated with the corresponding aerial robots; And sequentially confirming a plurality of groups of flight paths associated with the corresponding aerial robots in different two-dimensional planes, integrating the confirmed groups of flight paths, and confirming a flight path set associated with the corresponding aerial robots.
3. The intelligent transportation scheduling method based on the aerial robot according to claim 1, wherein in the second step, the specific way of confirming the path characteristics associated with different flight paths is as follows: For a single-group flight path set, a single flight path is selected from the single-group flight path set, the height characteristic and the path characteristic associated with the single flight path are confirmed, the horizontal plane where the transportation starting point is located is taken as a reference plane, the vertical distances between the vertical positions of different path points and the reference plane are confirmed in the flight path, the maximum value is selected from a plurality of groups of vertical distances, the path point associated with the maximum value is marked as the highest point, the vertical distance associated with the highest point is marked as L i-k , i represents different flight path sets, k represents different flight paths, the flight length associated with the flight path is confirmed, and the confirmed flight length is marked as CD i-k .
4. The intelligent transportation scheduling method based on the aerial robot according to claim 3, wherein in the second step, the specific manner of selecting the optimal path is: and confirming the standard characteristic Bz i-k associated with the corresponding flight path by adopting Bz i-k =L i-k ×C1+CD i-k multiplied by C2, wherein C1 and C2 are preset fixed coefficient factors, selecting the minimum value from a plurality of groups of standard characteristics associated with a single group of flight path sets according to different standard characteristics associated with different flight paths, taking the flight path associated with the minimum value as the optimal path, and calibrating the optimal path in the corresponding flight path set.
5. The intelligent transportation scheduling method based on the aerial robot according to claim 1, wherein in the third step, the specific manner of confirming the running time associated with the optimal path is as follows: Determining the associated gradient between adjacent path points from the confirmed optimal path, combining a preset horizontal plane, confirming the included angle between the adjacent path points and the horizontal plane, wherein the included angle is the confirmed gradient, confirming the flight rate associated with the corresponding gradient from preset full-load flight parameters, carrying out average processing on a plurality of confirmed groups of flight rates, confirming the average rate, taking the confirmed average rate as the flight rate of the current optimal path, and confirming the flight time associated with the corresponding optimal path according to the marked flight length and the flight rate in the optimal path; The optimal path is reversely confirmed by taking the dispatching end point as a transportation start point, taking the transportation start point as the dispatching end point, confirming the associated gradients between adjacent path points, confirming the flight rate associated with the corresponding gradients from preset idle flight parameters, carrying out average value processing on the flight rate, confirming the average value rate, and confirming the associated idle time when the corresponding optimal path returns by combining the flight length and the average value rate of the optimal path; The flight time and the dead time associated with the single aerial robot are summed to identify the transit time associated with the corresponding aerial robot.
6. The intelligent transportation scheduling method based on the aerial robot according to claim 1, wherein in the fourth step, the specific way of confirming the optimal scheduling logic is as follows: marking the required dispatching quantity as DL, marking the traffic quantity associated with each group of aerial robots as YL, and adopting DL/YL=Xz to confirm the total number of the requirements Xz of the aerial robots; According to different transportation time associated with different aerial robots, randomly selecting a plurality of aerial robots, stopping when the total number of the selected aerial robots is consistent with Xz, wherein a single aerial robot does not limit to be selected once, carrying out summation processing on the transportation time associated with each selected process, determining the total transportation time, and recording the determined total operation time as the process characteristics of the corresponding selected process; and selecting the minimum value from the different process characteristics associated with different selected processes, recording the selected process associated with the minimum value as an optimal process, recording the scheduling mode associated with the optimal process as optimal scheduling logic and executing the optimal scheduling logic.
7. The intelligent transportation scheduling method based on the aerial robot according to claim 6, wherein if Xz is not an integer, the decimal point is removed and the number is added with 1, and the total number of demands Xz is confirmed.
8. An aerial robot-based intelligent transportation scheduling system that operates in accordance with the aerial robot-based intelligent transportation scheduling method of any one of claims 1-7, comprising: The characteristic confirmation end confirms a dispatching end point from the input dispatching information, confirms the transportation start points of different aerial robots, and confirms the flight path sets associated with the different aerial robots according to the marked transportation start points and the dispatching end points; The path selection end confirms path characteristics associated with different flight paths from a single-group flight path set according to different flight path sets associated with different aerial robots, and then selects an optimal path from the single-group flight path set according to the different path characteristics associated with the different flight paths; The time confirmation end confirms the transportation time associated with each group of optimal paths according to different optimal paths confirmed in each group of different flight path sets and preset flight parameters; The scheduling logic confirmation end confirms the scheduling amount required by the scheduling end point from the scheduling information, and then synchronizes the total number of the requirements of the aerial robots according to the transportation amount associated with each group of aerial robots, and then determines the optimal scheduling logic and executes the optimal scheduling logic according to the transportation time associated with different aerial robots.

Description

Intelligent transportation scheduling system and method based on aerial robot Technical Field The invention relates to the technical field of aerial robots, in particular to an intelligent transportation scheduling system and method based on an aerial robot. Background With the continuous increase of the demand for efficient and flexible transportation in the fields of logistics transportation, emergency rescue, urban distribution and the like, an aerial robot (such as a multi-rotor unmanned aerial vehicle, a vertical take-off and landing fixed-wing unmanned aerial vehicle and the like) gradually becomes a core carrier of an intelligent transportation system by virtue of the advantages of being free from the limitation of ground traffic jam, high in response speed and wide in coverage range. Particularly, in complex scenes (such as mountain material transportation, urban high-rise inter-building distribution and post-disaster area material delivery), the aerial robot can break through the restrictions of terrain and road conditions, so that the transportation efficiency is greatly improved, and the research, development and application of related technologies have become the focus of industry attention. Currently, although the air robot transportation scheduling technology has advanced to some extent, a plurality of bottlenecks are still faced in practical application: Firstly, the path planning precision is insufficient. The existing method is mainly based on a two-dimensional map or a simplified three-dimensional model for generating paths, complex physical barriers (such as mountain bodies and high-rise building groups) in a real environment are difficult to accurately adapt, the problems that the distance between the paths and the barriers is too short or the distance between the paths and the barriers is too long are easily caused, the flight energy consumption is increased, the transportation time is prolonged, only distance factors are singly considered in part of path planning algorithms, the influence of the flight height on the energy consumption and the safety is ignored, and an optimized path considering both efficiency and safety cannot be formed. And secondly, the deviation between the transportation time measurement and the actual measurement is larger. The existing dispatching system usually adopts a fixed flight rate to conduct time estimation, and influences of path gradient change on the flight speed, such as power distribution difference of a climbing section and a flat flight section and performance difference under full load and no-load states, are not fully considered, so that deviation between a time measuring and calculating result and an actual transportation process is obvious, and accuracy of a dispatching plan is further affected. Thirdly, the resource allocation efficiency is low. In a multi-robot collaborative scheduling scene, the existing method mostly adopts simple logic of 'nearby allocation' or 'fixed round robin', does not combine the total amount of task demands, single machine transportation capacity and transportation time to carry out global optimization, is easy to cause the conditions of overload operation of part of robots and idle of part of robots, and causes low overall scheduling efficiency, and can not respond to the demands of a scheduling terminal point quickly. Therefore, how to construct a set of intelligent transportation scheduling method which can be accurately adapted to complex environments, accurately calculate transportation time and efficiently configure robot resources becomes a key problem for promoting the large-scale application of the air robot transportation technology, and is the core direction of the application for solving the problem. Disclosure of Invention Aiming at the defects of the prior art, the invention provides an intelligent transportation scheduling system and method based on an aerial robot, which solve the problems that the system and method cannot be accurately adapted to complex environments, accurately calculate transportation time and efficiently allocate robot resources. In order to achieve the purpose, the intelligent transportation scheduling system based on the aerial robot comprises the following technical scheme: step one, confirming a dispatching end point from the input dispatching information, confirming the transportation start points of different aerial robots, and confirming the flight path sets associated with the different aerial robots according to the marked transportation start points and the dispatching end point, wherein the specific mode is as follows: according to the confirmed dispatching terminal point and the transportation starting points of different aerial robots, connecting the transportation starting points with the dispatching terminal point, and confirming the characteristic connecting lines associated with the corresponding aerial robots; Synchronously marking the characteristic connecting