Intelligent transportation scheduling system and method based on aerial robot
By optimizing 3D solid models and path features, the problems of path planning and resource allocation for aerial robots in complex environments were solved, achieving efficient and accurate transportation scheduling.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- FOSHAN KANGJIN YUNCHONG TECHNOLOGY CO LTD
- Filing Date
- 2026-01-21
- Publication Date
- 2026-05-08
AI Technical Summary
Existing aerial robot transportation scheduling technologies suffer from insufficient path planning accuracy, large errors in transportation time calculation, and low resource allocation efficiency, making it difficult to schedule efficiently in complex environments.
By combining a 3D solid model with the translation of intersection points of multiple 2D planes, a flight path set is generated. A standard feature formula based on the maximum altitude and path length is constructed. The transportation time is calculated by combining the optimal path slope and preset flight parameters, and the resource allocation logic is optimized to achieve optimal scheduling.
It improves the accuracy of path planning, reduces flight energy consumption and time costs, enhances the accuracy of transportation time estimation, and realizes the intelligent allocation and scheduling of aerial robot resources.
Smart Images
Figure CN121998331A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of aerial robot technology, specifically to an intelligent transportation scheduling system and method based on aerial robots. Background Technology
[0002] With the continuous growth in demand for efficient and flexible transportation in logistics, emergency rescue, and urban delivery, aerial robots (such as multi-rotor drones and vertical take-off and landing fixed-wing drones) are gradually becoming the core carriers of intelligent transportation systems due to their advantages of not being restricted by ground traffic congestion, fast response speed, and wide coverage. Especially in complex scenarios (such as material transportation in mountainous areas, delivery between high-rise buildings in cities, and post-disaster material delivery), aerial robots can overcome the constraints of terrain and road conditions, significantly improving transportation efficiency. Therefore, the research and application of related technologies have become a focus of industry attention.
[0003] While aerial robot transportation scheduling technology has made some progress, it still faces many bottlenecks in practical applications: First, the accuracy of path planning is insufficient. Existing methods are mostly based on two-dimensional maps or simplified three-dimensional models for path generation, which makes it difficult to accurately adapt to complex physical obstacles in the real environment (such as mountains and high-rise building complexes). This can easily lead to problems such as the path being too close to the obstacle or excessive detours, resulting in increased flight energy consumption and extended transportation time. Some path planning algorithms only consider distance factors and ignore the impact of flight altitude on energy consumption and safety, thus failing to form an optimized path that balances efficiency and safety.
[0004] Secondly, there is a significant discrepancy between the estimated and actual transport time. Existing scheduling systems often use a fixed flight speed for time estimation, without fully considering the impact of changes in flight gradient on flight speed—such as the difference in power distribution between the climb and level flight phases, and the performance differences between fully loaded and empty states. This leads to a significant deviation between the estimated time and the actual transport process, thus affecting the accuracy of the scheduling plan.
[0005] Third, resource allocation efficiency is low. In multi-robot collaborative scheduling scenarios, existing methods mostly adopt the simple logic of "nearby allocation" or "fixed round-robin", without combining the total task demand, single-robot transportation capacity and transportation time for global optimization. This easily leads to some robots running overloaded and some robots being idle, resulting in low overall scheduling efficiency and inability to quickly respond to the needs of the scheduling destination.
[0006] Therefore, how to construct an intelligent transportation scheduling method that can accurately adapt to complex environments, accurately calculate transportation time, and efficiently allocate robot resources has become a key issue in promoting the large-scale application of aerial robot transportation technology, and it is also the core direction that this application is committed to solving. Summary of the Invention
[0007] To address the shortcomings of existing technologies, this invention provides an intelligent transportation scheduling system and method based on aerial robots, which solves the problems of not being able to accurately adapt to complex environments, accurately calculate transportation time, and efficiently allocate robot resources.
[0008] To achieve the above objectives, the present invention provides the following technical solution: an intelligent transportation scheduling system based on aerial robots, comprising: Step 1: Confirm the scheduling destination from the input scheduling information, then confirm the transportation starting point for different aerial robots. Based on the marked transportation starting point and scheduling destination, confirm the set of flight paths associated with different aerial robots. The specific method is as follows: Based on the confirmed scheduling endpoint and the transportation starting point where different aerial robots are located, connect the transportation starting point and the scheduling endpoint to confirm the characteristic connection line associated with the corresponding aerial robot; The feature lines are synchronously marked in the 3D solid model, which is a preset model. For the feature lines associated with a single aerial robot, several 2D planes where the feature lines are located are identified. The intersection points between the corresponding 2D planes and the outer surface of the 3D solid model are identified. Based on the identified intersection point positions, the displacement points are vertically translated upward by X1m. The identified displacement points are connected sequentially to identify the flight path associated with the corresponding aerial robot. Then, the flight paths associated with the corresponding aerial robot in different two-dimensional planes are confirmed in sequence, and the confirmed flight paths are integrated to confirm the flight path set associated with the corresponding aerial robot. Step 2: Based on the different flight path sets associated with different aerial robots, identify the path characteristics associated with different flight paths from a single set of flight paths. Then, based on the different path characteristics associated with different flight paths, select the optimal path from the single set of flight paths. The specific method is as follows: For a single set of flight paths, a single flight path is selected from the set, and the altitude and path characteristics associated with the single flight path are confirmed: using the horizontal plane where the transport origin is located as the reference plane, the vertical distances between different path points and the reference plane are confirmed within the flight path. The maximum value is selected from several sets of vertical distances, and the path point associated with the maximum value is recorded as the highest point. The vertical distance associated with the highest point is recorded as L. i-k Here, i represents different sets of flight paths, k represents different flight paths, and the flight length associated with each flight path is confirmed, with the confirmed flight length marked as CD. i-k ; Adopted by: Bz i-k =L i-k ×C1+CD i-k×C2 confirms the standard feature Bz associated with the corresponding flight path. i-k C1 and C2 are preset fixed coefficient factors. Based on the different standard features associated with different flight paths, the minimum value is selected from several sets of standard features associated with a single set of flight paths. The flight path associated with the minimum value is taken as the optimal path and marked in the corresponding set of flight paths. Step 3: Based on the different optimal paths identified in each set of flight paths, and according to preset flight parameters, confirm the transportation time associated with each optimal path. The specific method is as follows: From the confirmed optimal path, determine the slope associated with adjacent path points. Combined with the preset horizontal plane, determine the angle between adjacent path points and the horizontal plane. This angle is the confirmed slope. From the preset full-load flight parameters, determine the flight speed associated with the corresponding slope. Then, average the confirmed flight speeds to determine the average speed. Use the determined average speed as the flight speed of the current optimal path. Based on the flight length and flight speed marked within the optimal path, determine the flight time associated with the corresponding optimal path. Then, the optimal path is confirmed in reverse: taking the scheduling endpoint as the transportation starting point and the transportation starting point as the scheduling endpoint, the slope associated with adjacent path points is confirmed, and the flight speed associated with the corresponding slope is confirmed from the preset empty flight parameters. The flight speed is averaged and the average speed is confirmed. Combined with the flight length of the optimal path and the average speed, the empty time associated with the return of the corresponding optimal path is confirmed. The flight time and idle time associated with a single aerial robot are summed to determine the transportation time associated with the corresponding aerial robot. Step 4: Confirm the required scheduling volume of the destination from the scheduling information, then simultaneously confirm the total demand for aerial robots based on the transportation volume associated with each group of aerial robots, and finally determine and execute the optimal scheduling logic based on the transportation time associated with different aerial robots. The specific method is as follows: Mark the required scheduling volume as DL, and mark the transportation volume associated with each group of aerial robots as YL. Use DL÷YL=Xz to determine the total demand for aerial robots Xz. Based on the different transportation times associated with different aerial robots, several aerial robots are randomly selected until the total number of selected aerial robots is consistent with Xz. Each aerial robot is not limited to being selected once. The transportation time associated with each selection process is summed to determine the total transportation time. The determined total running time is recorded as the process characteristic of the corresponding selection process. Based on the different process characteristics associated with different selected processes, the minimum value is selected, the selected process associated with the minimum value is recorded as the optimal process, and the scheduling method associated with the optimal process is recorded as the optimal scheduling logic and executed.
[0009] Preferably, if Xz is not an integer, the decimal places are removed and 1 is added to confirm the total required number Xz.
[0010] Preferably, an intelligent transportation scheduling system based on aerial robots includes: The feature confirmation end confirms the scheduling destination from the input scheduling information, then confirms the transportation starting point of different aerial robots, and confirms the flight path set associated with different aerial robots based on the marked transportation starting point and scheduling destination. The path selection end identifies the path characteristics associated with different flight paths from a single set of flight paths based on the different flight path sets associated with different aerial robots, and then selects the optimal path from the single set of flight paths based on the different path characteristics associated with different flight paths. The time confirmation terminal confirms the transportation time associated with each optimal path based on the different optimal paths confirmed in each set of different flight paths and according to the preset flight parameters. The scheduling logic confirmation end confirms the scheduling quantity required by the scheduling destination from the scheduling information, then simultaneously confirms the total demand of aerial robots based on the transportation quantity associated with each group of aerial robots, and then determines and executes the optimal scheduling logic based on the transportation time associated with different aerial robots.
[0011] This invention provides an intelligent transportation scheduling system and method based on aerial robots. Compared with existing technologies, it has the following advantages: This invention generates a flight path set by combining a three-dimensional solid model with the translation of multiple two-dimensional plane intersections. This avoids the influence of physical obstacles and provides a rich sample for optimal path selection. Based on the maximum altitude and path length, a standard feature formula is constructed, which can quickly lock the optimal path of "low altitude + short distance" and reduce flight energy consumption and time costs. By combining the optimal path gradient with preset full-load / empty flight parameters, the transport and return times are calculated separately. The calculation results are closer to the actual flight status of the drone, improving the accuracy of time prediction. The number of robots required is determined by the total demand and the transportation capacity per robot, and the optimal selection process is selected with the goal of minimizing the total transportation time. This achieves efficient allocation of aerial robot resources, ensures rapid response to the needs at the dispatch endpoint, and significantly improves the intelligence and efficiency of the overall transportation scheduling. Attached Figure Description
[0012] Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 This is a schematic diagram of the principle framework of the present invention. Detailed Implementation
[0013] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0014] First Embodiment Please see Figure 1 This application provides an intelligent transportation scheduling method based on aerial robots, including the following steps: Step 1: Confirm the scheduling destination from the input scheduling information, then confirm the transportation origin of different aerial robots. Based on the marked transportation origin and scheduling destination, confirm the set of flight paths associated with different aerial robots: Based on the confirmed scheduling endpoint and the transportation starting point where different aerial robots are located, connect the transportation starting point and the scheduling endpoint to confirm the characteristic connection line associated with the corresponding aerial robot; The feature lines are synchronously marked in the 3D solid model. The 3D solid model is a preset model, which is prepared in advance by relevant personnel based on experience. For the feature lines associated with a single aerial robot, the number of 2D planes in which the feature lines are located is identified (a line is located in several different 2D planes, and at least ten groups are selected, and the included angle between each group of adjacent 2D planes is the same). The intersection point between the corresponding 2D plane and the outer surface of the 3D solid model is identified (that is, the point of intersection, for example, when there is a lateral 2D plane in a mountain model, then there is a corresponding intersection point between the outer side of the mountain model and the corresponding 2D plane). Based on the identified intersection point position, the displacement point is confirmed by vertically moving upward by X1m. The minimum value of X1 is 10. The identified groups of displacement points are connected in sequence (that is, adjacent displacement points are connected step by step) to confirm the flight path associated with the corresponding aerial robot. Then, the flight paths associated with the corresponding aerial robot in different two-dimensional planes are confirmed in sequence, and the confirmed flight paths are integrated to confirm the flight path set associated with the corresponding aerial robot. Specifically, each aerial robot has a starting point and an ending point. Based on the corresponding flight process, the corresponding feature lines can be determined. Based on the corresponding feature lines, the two-dimensional plane where the corresponding lines are located can be effectively identified. Then, based on the intersection of the determined two-dimensional plane and the solid model, the intersection point between the outer surface of the solid model and the two-dimensional plane can be identified. By translating and confirming the intersection point, the corresponding displacement point can be determined, thereby locking the flight path associated with the corresponding aerial robot. Step 2: Based on the different flight path sets associated with different aerial robots, identify the path characteristics associated with different flight paths from a single set of flight path sets. Then, based on the different path characteristics associated with different flight paths, select the optimal path from a single set of flight path sets. Specifically, the optimal path is the one in the corresponding flight path set where the flight altitude and flight distance are both in the optimal state, so that the corresponding aerial robot can complete the corresponding cargo transportation work in the least amount of time. The specific method for selecting the optimal path is as follows: For a single set of flight paths, a single flight path is selected from the set, and the altitude and path characteristics associated with the single flight path are confirmed: using the horizontal plane where the transport origin is located as the reference plane, the vertical distances between different path points and the reference plane are confirmed within the flight path. The maximum value is selected from several sets of vertical distances, and the path point associated with the maximum value is recorded as the highest point. The vertical distance associated with the highest point is recorded as L. i-k Here, i represents different sets of flight paths, k represents different flight paths, and the flight length associated with each flight path is confirmed (i.e., the total flight length of the path). The confirmed flight length is marked as CD. i-k ; Adopted by: Bz i-k =L i-k ×C1+CD i-k ×C2 confirms the standard feature Bz associated with the corresponding flight path. i-k C1 and C2 are preset fixed coefficient factors. Their specific values are determined by the operator based on experience. According to the different standard features associated with different flight paths, the minimum value is selected from several sets of standard features associated with a single set of flight paths. The flight path associated with the minimum value is taken as the optimal path and marked in the corresponding set of flight paths. Specifically, each set of flight paths has a set of associated optimal paths. Since each set of different flight paths is associated with different path features, in the process of confirming the corresponding path features, the lower the corresponding flight altitude, the shorter the associated flight length, and the smaller the overall standard feature of the associated corresponding flight path will be. Therefore, in the corresponding set of flight paths, it belongs to an optimal path.
[0015] Step 3: Based on the different optimal paths identified in each set of flight paths, and according to the preset flight parameters, confirm the transportation time associated with each optimal path: From the confirmed optimal path, determine the slope associated with adjacent path points. Combined with the preset horizontal plane, determine the angle between adjacent path points and the horizontal plane. This angle is the confirmed slope. From the preset full-load flight parameters, determine the flight speed associated with the corresponding slope (these parameters are all preset parameters, pre-determined by relevant personnel). Then, average the confirmed flight speeds to determine the average speed. Use the determined average speed as the flight speed of the current optimal path. Based on the flight length and flight speed marked within the optimal path, determine the flight time associated with the corresponding optimal path. The preset flight parameters associated here correspond to the aerial robot being in a fully loaded state. Then, the optimal path is confirmed in reverse: taking the scheduling endpoint as the transportation starting point and the transportation starting point as the scheduling endpoint, the slope associated with adjacent path points is confirmed, and the flight speed associated with the corresponding slope is confirmed from the preset empty flight parameters. The flight speed is averaged and the average speed is confirmed. Combined with the flight length of the optimal path and the average speed, the empty time associated with the return of the corresponding optimal path is confirmed. The flight time and idle time associated with a single aerial robot are summed to determine the transportation time associated with that aerial robot.
[0016] Step 4: Confirm the required scheduling volume of the destination from the scheduling information, then simultaneously confirm the total demand of aerial robots based on the transportation volume associated with each group of aerial robots, and finally determine and execute the optimal scheduling logic based on the transportation time associated with different aerial robots. The specific method for determining the optimal scheduling logic is as follows: Mark the required scheduling volume as DL, and mark the transportation volume associated with each group of aerial robots as YL (a preset value, to be determined in advance by relevant personnel). Use DL÷YL=Xz to confirm the total demand for aerial robots Xz. If Xz is not an integer, remove the decimal part and add 1 to confirm the total demand Xz (for example, if it is 6.5, then take 7 directly). Based on the different transportation times associated with different aerial robots, several aerial robots are randomly selected until the total number of selected aerial robots is consistent with Xz. Each aerial robot is not limited to being selected once (that is, it can be selected multiple times). The transportation time associated with each selection process is summed to determine the total transportation time. The determined total running time is recorded as the process characteristic of the corresponding selection process. Based on the different process characteristics associated with different selected processes, the minimum value is selected, the selected process associated with the minimum value is recorded as the optimal process, and the scheduling method associated with the optimal process is recorded as the optimal scheduling logic and executed. Specifically, once the corresponding optimal logic is determined, the aerial robots can be effectively scheduled based on this logic. Moreover, the total transportation time generated during the scheduling and transportation process is minimized, which can fully guarantee the transportation and scheduling process of the corresponding aerial robots.
[0017] Second Embodiment Combination Figure 2 An intelligent transportation scheduling system based on aerial robots includes: The feature confirmation end confirms the scheduling destination from the input scheduling information, then confirms the transportation starting point of different aerial robots, and confirms the flight path set associated with different aerial robots based on the marked transportation starting point and scheduling destination. The path selection end identifies the path characteristics associated with different flight paths from a single set of flight paths based on the different flight path sets associated with different aerial robots, and then selects the optimal path from the single set of flight paths based on the different path characteristics associated with different flight paths. The time confirmation terminal confirms the transportation time associated with each optimal path based on the different optimal paths confirmed in each set of different flight paths and according to the preset flight parameters. The scheduling logic confirmation end confirms the scheduling quantity required by the scheduling destination from the scheduling information, then simultaneously confirms the total demand of aerial robots based on the transportation quantity associated with each group of aerial robots, and then determines and executes the optimal scheduling logic based on the transportation time associated with different aerial robots.
[0018] Some of the data in the above formulas are numerical calculations with dimensions removed, and the contents not described in detail in this specification are all prior art known to those skilled in the art.
[0019] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.
Claims
1. An intelligent transportation scheduling method based on aerial robots, characterized in that, include: Step 1: Confirm the scheduling destination from the input scheduling information, then confirm the transportation starting point of different aerial robots, and based on the marked transportation starting point and scheduling destination, confirm the flight path set associated with different aerial robots; Step 2: Based on the different flight path sets associated with different aerial robots, identify the path characteristics associated with different flight paths from a single set of flight paths, and then select the optimal path from a single set of flight paths based on the different path characteristics associated with different flight paths. Step 3: Based on the different optimal paths identified in each set of different flight paths, and according to the preset flight parameters, confirm the transportation time associated with each set of optimal paths. Step 4: Confirm the required scheduling volume of the destination from the scheduling information, then simultaneously confirm the total demand of aerial robots based on the transportation volume associated with each group of aerial robots, and finally determine and execute the optimal scheduling logic based on the transportation time associated with different aerial robots.
2. The intelligent transportation scheduling method based on aerial robots according to claim 1, characterized in that, In step one, the specific method for confirming the flight path set is as follows: Based on the confirmed scheduling endpoint and the transportation starting point where different aerial robots are located, connect the transportation starting point and the scheduling endpoint to confirm the characteristic connection line associated with the corresponding aerial robot; The feature lines are synchronously marked in the 3D solid model, which is a preset model. For the feature lines associated with a single aerial robot, several 2D planes where the feature lines are located are identified. The intersection points between the corresponding 2D planes and the outer surface of the 3D solid model are identified. Based on the identified intersection point positions, the displacement points are vertically translated upward by X1m. The identified displacement points are connected sequentially to identify the flight path associated with the corresponding aerial robot. Then, the flight paths associated with the corresponding aerial robot in different two-dimensional planes are confirmed in sequence, and the confirmed flight paths are integrated to confirm the flight path set associated with the corresponding aerial robot.
3. The intelligent transportation scheduling method based on aerial robots according to claim 1, characterized in that, In step two, the specific method for confirming the path characteristics associated with different flight paths is as follows: For a single set of flight paths, a single flight path is selected from the set, and the altitude and path characteristics associated with the single flight path are confirmed: using the horizontal plane where the transport origin is located as the reference plane, the vertical distances between different path points and the reference plane are confirmed within the flight path. The maximum value is selected from several sets of vertical distances, and the path point associated with the maximum value is recorded as the highest point. The vertical distance associated with the highest point is recorded as L. i-k Here, i represents different sets of flight paths, k represents different flight paths, and the flight length associated with each flight path is confirmed, with the confirmed flight length marked as CD. i-k .
4. The intelligent transportation scheduling method based on aerial robots according to claim 3, characterized in that, In step two, the specific method for selecting the optimal path is as follows: Adopted by: Bz i-k =L i-k ×C1+CD i-k ×C2 confirms the standard feature Bz associated with the corresponding flight path. i-k C1 and C2 are preset fixed coefficient factors. Based on the different standard features associated with different flight paths, the minimum value is selected from several sets of standard features associated with a single set of flight paths. The flight path associated with the minimum value is taken as the optimal path and marked in the corresponding set of flight paths.
5. The intelligent transportation scheduling method based on aerial robots according to claim 1, characterized in that, In step three, the specific method for confirming the running time associated with the optimal path is as follows: From the confirmed optimal path, determine the slope associated with adjacent path points. Combined with the preset horizontal plane, determine the angle between adjacent path points and the horizontal plane. This angle is the confirmed slope. From the preset full-load flight parameters, determine the flight speed associated with the corresponding slope. Then, average the confirmed flight speeds to determine the average speed. Use the determined average speed as the flight speed of the current optimal path. Based on the flight length and flight speed marked within the optimal path, determine the flight time associated with the corresponding optimal path. Then, the optimal path is confirmed in reverse: taking the scheduling endpoint as the transportation starting point and the transportation starting point as the scheduling endpoint, the slope associated with adjacent path points is confirmed, and the flight speed associated with the corresponding slope is confirmed from the preset empty flight parameters. The flight speed is averaged and the average speed is confirmed. Combined with the flight length of the optimal path and the average speed, the empty time associated with the return of the corresponding optimal path is confirmed. The flight time and idle time associated with a single aerial robot are summed to determine the transportation time associated with that aerial robot.
6. The intelligent transportation scheduling method based on aerial robots according to claim 1, characterized in that, In step four, the specific method for confirming the optimal scheduling logic is as follows: Mark the required scheduling volume as DL, and mark the transportation volume associated with each group of aerial robots as YL. Use DL÷YL=Xz to determine the total demand for aerial robots Xz. Based on the different transportation times associated with different aerial robots, several aerial robots are randomly selected until the total number of selected aerial robots is consistent with Xz. Each aerial robot is not limited to being selected once. The transportation time associated with each selection process is summed to determine the total transportation time. The determined total running time is recorded as the process characteristic of the corresponding selection process. Based on the different process characteristics associated with different selected processes, the minimum value is selected, the selected process associated with the minimum value is recorded as the optimal process, and the scheduling method associated with the optimal process is recorded as the optimal scheduling logic and executed.
7. The intelligent transportation scheduling method based on aerial robots according to claim 6, characterized in that, If Xz is not an integer, remove the decimal places and add 1 to confirm the total required quantity Xz.
8. An intelligent transportation scheduling system based on aerial robots, the system operating according to any one of claims 1-7, characterized in that, include: The feature confirmation end confirms the scheduling destination from the input scheduling information, then confirms the transportation starting point of different aerial robots, and confirms the flight path set associated with different aerial robots based on the marked transportation starting point and scheduling destination. The path selection end identifies the path characteristics associated with different flight paths from a single set of flight paths based on the different flight path sets associated with different aerial robots, and then selects the optimal path from the single set of flight paths based on the different path characteristics associated with different flight paths. The time confirmation terminal confirms the transportation time associated with each optimal path based on the different optimal paths confirmed in each set of different flight paths and according to the preset flight parameters. The scheduling logic confirmation end confirms the scheduling quantity required by the scheduling destination from the scheduling information, then simultaneously confirms the total demand of aerial robots based on the transportation quantity associated with each group of aerial robots, and then determines and executes the optimal scheduling logic based on the transportation time associated with different aerial robots.