CN116700286A - Path planning method, device, equipment and medium based on interpolation algorithm - Google Patents
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Abstract
The application discloses a path planning method, a device, equipment and a medium based on an interpolation algorithm, wherein the path planning method based on the interpolation algorithm comprises the steps of collecting task points in a target area, acquiring point cloud data information of the task points and constructing a point cloud data network; the working mode of the sweeping robot is set to be a first working mode and a second working mode; judging whether the collected point cloud data needs to be subjected to denoising treatment or not based on the working mode of the sweeping robot; the point cloud data denoising processing is realized by analyzing task points through a point cloud data network constructed based on the point cloud data information; selecting a path based on an interpolation algorithm according to the point cloud data network; the interpolation algorithm is realized through a cubic spline interpolation function; the optimized path, namely the optimal path, is obtained by evaluation and compensation processing based on the selected path, and the method provided by the application can meet the manual mode of the sweeping robot with short path, low energy consumption and high efficiency.
Description
Technical Field
The application relates to the technical field of path planning of sweeping robots, in particular to a path planning method, device, equipment and medium based on an interpolation algorithm.
Background
Along with the gradual maturity of intelligent house technique, sweep robot plays indispensable effect in production life, current robot of sweeping is influenced by various factors such as house equipment for sweep robot's path planning ability is poor, the condition of sweeping is repeated to the path planning based on regional map environment modeling that calculated amount is big and causes easily, time consuming is long and work efficiency is low, need remodel when meetting unknown environment, calculation cost is high, environmental suitability is poor, based on above-mentioned condition, the path optimization problem of sweeping robot needs to be solved urgently, the cost is reduced, and efficiency of sweeping is improved.
Disclosure of Invention
Aiming at the problems existing in the prior art, the application provides a path planning method, a device, equipment and a medium based on an interpolation algorithm.
The embodiment of the application discloses the following technical scheme:
in a first aspect, the present application provides a path planning method based on an interpolation algorithm, the method comprising:
s01, collecting task points in a target area, acquiring point cloud data information of the task points, and constructing a point cloud data network;
s02, setting a first working mode and a second working mode of the sweeping robot;
the first working mode is full-coverage cleaning;
the second working mode is targeted cleaning;
s03, judging whether the collected point cloud data needs to be subjected to denoising processing or not based on the working mode of the sweeping robot;
the point cloud data denoising processing is realized by analyzing task points through a point cloud data network constructed based on the point cloud data information;
s04, selecting a path based on an interpolation algorithm according to the point cloud data network;
the interpolation algorithm is realized through a cubic spline interpolation function;
s05, performing evaluation and compensation processing based on the selected path to obtain the optimal target path.
Further, the step S01 is to collect task points in the target area, obtain point cloud data information of the task points, obtain data information including three-dimensional coordinate information by using a sensor mounted on the sweeping robot, digitize the condition information of the task points, and transmit the condition information to a memory for storage, thereby constructing a point cloud data network.
Further, the first workpiece mode is full coverage cleaning, namely cleaning in the whole range is performed based on the constructed point cloud data network; the second working mode is target cleaning, namely local cleaning is carried out according to a target obstacle.
Further, for the step S03, determining whether the denoising process of the point cloud data is required includes:
based on a set working mode of the sweeping robot, when the first working mode is executed, denoising processing is not needed; when the second working mode is executed, denoising processing is needed to be carried out on the collected point cloud data;
specifically, the denoising process includes:
1) Analyzing the distribution number of task points in a preset area around each target task point, wherein the preset area around each target task point is determined based on a circumference range of a first preset length from the target task point;
2) If the number of task point distribution in the preset area around the target task point is larger than a second preset value, determining the target task point as a core task point;
3) Selecting a core task point for analysis, marking the core task points in a preset area around the core task point as a first core task point, and marking the core task points outside the preset area around the core task point as a second core task point;
4) Reserving the first core task point and task points in a preset area around the first core task point;
5) Repeating 4) -5) until all the core task point analysis is completed, and acquiring all the reserved task points.
And planning the path in the second working mode based on the reserved task points.
Further, the step S04 is to select a path based on an interpolation algorithm; the interpolation algorithm is realized by a cubic spline interpolation function and comprises the following steps:
(1) K interpolation task points are selected on the constructed point cloud data network;
(2) Based on the combination of the K interpolation task points and coordinates of a departure task point A and a target task point B of the sweeping robot, two sequences I and J of the horizontal and vertical coordinates are obtained;
(3) Calculating a cubic spline interpolation function S (X), S (Y) corresponding to the abscissa and the ordinate;
(4) And solving numerical information corresponding to the corresponding cubic spline interpolation function to obtain point data information of the corresponding interpolation task and path information.
Further, when the interpolation task point is changed arbitrarily, the selected path is changed accordingly.
Further, the step S05 is to perform evaluation and compensation processing based on the selected path to obtain an optimal target path; the evaluation compensation includes:
(0) Acquiring point cloud data information of all task points on the path, wherein the point cloud data information comprises a departure task point, a process point and a target task point;
(1) Selecting any task point in the path to be set as an evaluation task point;
(2) Judging the importance of the evaluation task point;
(3) The importance of the evaluation task points is judged by calculating the distance between the departure task point and each evaluation task point on the path and the obstacle, if the distance is greater than or equal to a first preset distance value, the point is a first evaluation task point, and if the distance is smaller than the first preset distance value, the point is a second evaluation task point and can be deleted; the first preset distance is determined based on a point cloud data network;
(4) Iterating until all evaluation task points are calculated, obtaining all first evaluation task point data sets, calculating evaluation distances corresponding to all first evaluation task points, and screening and sorting to obtain minimum evaluation distances;
(5) Substituting the minimum value of the evaluation distance into the point cloud data network to obtain data information of a task point corresponding to the minimum value of the evaluation distance, namely, the point is an important task point;
(6) And starting repeating by taking other task points as new starting task points until the starting task points are connected with target task points, obtaining all important task points, and connecting all the important task points on the path to obtain the optimal path.
In a second aspect, the present application provides a path planning apparatus based on an interpolation algorithm, the apparatus comprising:
the point cloud data collection module is used for collecting task points in the target area, acquiring point cloud data information of the task points and constructing a point cloud data network;
the setting module is used for setting a first working mode and a second working mode of the sweeping robot;
the first working mode is full-coverage cleaning; the second working mode is targeted cleaning;
the judging module is used for judging whether the collected point cloud data needs to be subjected to denoising treatment or not based on the working mode of the sweeping robot;
the point cloud data denoising processing is realized by analyzing task points through a point cloud data network constructed based on the point cloud data information;
the path selecting module is used for selecting a path based on an interpolation algorithm according to the point cloud data network;
the interpolation algorithm is realized through a cubic spline interpolation function;
and the optimal path module is used for carrying out evaluation and compensation processing based on the selected path to obtain an optimal target path.
In a third aspect, the present application also provides an apparatus for path planning based on an interpolation algorithm, the apparatus comprising a memory for storing a computer program and a processor for running the computer program to cause the computer apparatus to perform the path planning method based on the interpolation algorithm described above;
in a fourth aspect, the present application also provides a medium for path planning based on an interpolation algorithm, the medium storing computer program instruction codes, the computer program instructions when executed implementing the path planning method based on the interpolation algorithm.
The path planning method, device, equipment and medium based on the interpolation algorithm have the following beneficial effects: (1) According to the path planning based on the interpolation algorithm, according to the characteristic of cubic spline interpolation, when facing complex and changeable obstacles, the path planning method can update and adjust the path in time, can reserve the previous optimal path planning based on the storage function of the device and the medium, provides guidance for the next re-planning, saves time and improves the cleaning efficiency during working;
(2) According to the application, the point cloud data is adopted for processing, so that the equipment related information in the home area is accurately and efficiently collected and stored, a multi-scale point cloud data network is formed, the discrete task point information is uniformly managed, the advantages of high resolution, digitalization and the like are formed, real-time, effective and accurate three-dimensional information is provided for cleaning work, and accurate data support is provided for path planning;
(3) The sweeping robot is divided into two working modes, and in the first working mode, namely full-coverage sweeping, the sweeping is carried out in the whole range, so that the sweeping robot meets daily requirements, the cleaning effect is high, the manpower labor is reduced, and the application of intelligent home is promoted; in the second working mode, namely, local cleaning is performed according to the target obstacle, denoising is performed on the currently collected point cloud data, task point information needing path planning is screened out, redundant information is removed, the operation time of path planning is reduced, a local cleaning area is limited, cleaning path planning is performed more specifically, the accuracy and the precision of path planning are improved, and the cleaning refinement level of the cleaning robot is enhanced;
(4) According to the path planning method, the selected path is optimized to the greatest extent through evaluation and compensation, so that an optimal cleaning path can be found in any scene, the cleaning effects of short path time, low energy consumption and high safety are achieved, the path planning cost of the sweeping robot is reduced, the technical problem of repeated cleaning is solved, the environment is not required to be modeled in advance based on an interpolation algorithm, the calculated amount is reduced, the robustness and the accuracy of path planning are greatly improved, and the method has great significance in improving the intelligent level of the sweeping robot.
Drawings
FIG. 1 is a flow chart of the steps of a path planning apparatus based on an interpolation algorithm according to an embodiment of the present application;
FIG. 2 is a flowchart of the steps of a path planning method based on an interpolation algorithm according to an embodiment of the present application;
fig. 3 is a flowchart of denoising processing based on point cloud data in an embodiment of the present application.
Detailed Description
The technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, but not all embodiments, and all other embodiments obtained by those skilled in the art without making any inventive effort based on the embodiments of the present application are within the scope of protection of the present application.
The application discloses a path planning method based on an interpolation algorithm, which comprises the following steps:
s01, collecting task points in a target area, acquiring point cloud data information of the task points, and constructing a point cloud data network; and acquiring data information including three-dimensional coordinate information by using a sensor mounted on the robot for sweeping the floor, digitizing condition information of the task points, transmitting the condition information to a memory for storage, and constructing a point cloud data network.
In the embodiment, the sensor is used for acquiring three-dimensional coordinate information and other related attributes in the target area to be detected in the space, so that the surface characteristics of the target area are converted into digital information which can be used, the target area is further specified, the acquired point cloud data has the advantages of high instantaneity, high resolution, digitization and the like, and accurate data support is provided for the subsequent construction of the point cloud data network and the determination of a final target path.
In this embodiment, the obtained point cloud data may collect, in addition to basic coordinate information (XYZ), attributes such as intensity, RGB color, semantic information of the corresponding points, so that the characteristics of the measurement object may be better represented, and by obtaining dense, large-scale and information-rich three-dimensional points, implicit representations of the geometry, position and attribute of the object may be obtained, thereby greatly enhancing accuracy and efficiency of data processing.
S02, setting a first working mode and a second working mode of the sweeping robot;
the first working mode is full-coverage cleaning;
the second working mode is targeted cleaning
The first workpiece mode is full-coverage cleaning, namely cleaning in the whole range is performed based on the constructed point cloud data network; the second working mode is target cleaning, namely local cleaning is carried out according to a target obstacle.
In this embodiment, the working mode of the sweeping robot is set to two modes, the sweeping work is performed based on actual requirements, the sweeping work is performed in all areas in the first working mode to enable the sweeping work to be comprehensive, the sweeping work in the whole area is completed, the whole cleaning is guaranteed, the purposeful sweeping work is performed in the second working mode, the purposeful sweeping work is performed according to the target area to be cleaned, the time waste of the sweeping work in a large area can be avoided, the sweeping work efficiency is improved, and the working time is reduced.
S03, judging whether the collected point cloud data needs to be subjected to denoising processing or not based on the working mode of the sweeping robot; based on a set working mode of the sweeping robot, when the first working mode is executed, denoising processing is not needed; when the second working mode is executed, denoising processing is needed to be carried out on the collected point cloud data; the point cloud data denoising processing is realized by analyzing task points through a point cloud data network constructed based on the point cloud data information;
the denoising process includes:
1) Analyzing the distribution number of task points in a preset area around each target task point, wherein the preset area around each target task point is determined based on a circumference range of a first preset length from the target task point;
2) If the number of task point distribution in the preset area around the target task point is larger than a second preset value, determining the target task point as a core task point;
3) Selecting a core task point for analysis, marking the core task points in a preset area around the core task point as a first core task point, and marking the core task points outside the preset area around the core task point as a second core task point;
4) Reserving the first core task point and task points in a preset area around the first core task point;
5) Repeating 4) -5) until all the core task point analysis is completed, and acquiring all the reserved task points.
And planning the path in the second working mode based on the reserved task points.
In this embodiment, the denoising processing of the point cloud data is implemented by analyzing task points, reserving screened task points, deleting unreserved task points, analyzing based on a preset area around a target task point, determining the preset area around the target task point based on a circle range with a first preset length from the target task point, determining the target task point as a core task point if the number of task point distribution in the preset area around the target task point is greater than a second preset value, analyzing any one of the core task points, setting the core task points in the preset area around the core task point as a first core task point, and setting the core task points outside the preset area around the core task point as a second core task point; and sequentially connecting the task points in the preset area around the task point until the core-free task point, screening out all the core task points and the task points in the preset area around the first core task point for reservation, and taking the point cloud data after noise elimination as the data support of path planning in the second working mode, so that the calculated amount of path planning in the second working mode is greatly reduced, the calculation time is saved, the data flexibility management is realized, the data processing efficiency is improved, and convenience is provided for the subsequent cleaning work.
S04, selecting a path based on an interpolation algorithm according to the point cloud data network;
the interpolation algorithm is realized through a cubic spline interpolation function; comprising the following steps:
(1) K interpolation task points are selected on the constructed point cloud data network;
(2) Based on the combination of the K interpolation task points and coordinates of a departure task point A and a target task point B of the sweeping robot, two sequences I and J of the horizontal and vertical coordinates are obtained;
(3) Calculating a cubic spline interpolation function S (X), S (Y) corresponding to the abscissa and the ordinate;
(4) And solving numerical information corresponding to the corresponding cubic spline interpolation function to obtain point data information of the corresponding interpolation task and path information.
In this embodiment, based on the characteristic of second order derivative of the function fitted by the cubic spline interpolation method, K interpolation task points are selected on the constructed point cloud data network, an arithmetic sequence M= {0, M1, M2 … MK,1}, and two sequences I and J of the abscissa and the ordinate are obtained based on the data information of the departure task point A and the target task point B of the sweeping robot and K interpolation task points, wherein A= (X) A ,Y A ),B＝(X B ,Y B )，I＝{X A ,X K1 ,X K2 ,…,X B }，J＝{Y A ,Y K1 ,Y K2 ,…,Y B And calculating a cubic spline interpolation function S (X), S (Y) corresponding to the abscissa and the ordinate, solving the corresponding value of the cubic spline interpolation function, obtaining the coordinate work and rest of the corresponding interpolation task point, bringing the coordinate information into a point cloud data network, obtaining the data information of the task point of the path based on the constructed point cloud data network, further obtaining a specific path, and re-planning a new feasible path according to the characteristic of the cubic spline interpolation by only slightly adjusting the position of the interpolation task point, thereby having stronger flexibility.
In particular, when the interpolation task point is changed arbitrarily, the selected path is changed accordingly.
In the embodiment, based on the diversity and the position uncertainty of household obstacles, the emphasis is on accurately predicting the change development trend of relevant elements in the environment under the condition of changed position environment, so that a new path is effectively planned again in time after the environment changes, the path planning based on environment modeling in the prior art needs to be modeled again when facing the condition, the calculated amount is large and the efficiency is low, but the path planning based on the interpolation algorithm slightly adjusts the position information of interpolation task points, based on the saved optimal path planning information, the path information can be modified and adjusted timely and pertinently when facing the changeable position environment scene, the path information is updated continuously, the time of the re-planning is saved, the cleaning efficiency is enhanced, and the flexibility is high.
S05, performing evaluation and compensation processing based on the selected path to obtain an optimal target path; the evaluation compensation includes:
(0) Acquiring point cloud data information of all task points on the path, wherein the point cloud data information comprises a departure task point, a process point and a target task point;
(1) Selecting any task point in the path to be set as an evaluation task point;
(2) Judging the importance of the evaluation task point;
(3) The importance of the evaluation task points is judged by calculating the distance between the departure task point and each evaluation task point on the path and the obstacle, if the distance is greater than or equal to a first preset distance value, the point is a first evaluation task point, and if the distance is smaller than the first preset distance value, the point is a second evaluation task point and can be deleted; the first preset distance is determined based on a point cloud data network;
(4) Iterating until all evaluation task points are calculated, obtaining all first evaluation task point data sets, calculating evaluation distances corresponding to all first evaluation task points, and screening and sorting to obtain minimum evaluation distances;
(5) Substituting the minimum value of the evaluation distance into the point cloud data network to obtain data information of a task point corresponding to the minimum value of the evaluation distance, namely, the point is an important task point;
(6) And starting repeating by taking other task points as new starting task points until the starting task points are connected with target task points, obtaining all important task points, and connecting all the important task points on the path to obtain the optimal path.
In this embodiment, point cloud data information of all task points on the path is acquired, including a departure task point, a process point and a target task point, and any task point is selected in the path to be set as an evaluation task point; judging the importance of the evaluation task points, judging the importance of the evaluation task points by calculating the distance between a departure task point and each evaluation task point on a path and an obstacle, wherein if the distance is greater than or equal to a first preset distance value, the point is a first evaluation task point, and if the distance is less than the first preset distance value, the point is a second evaluation task point and can be deleted; the first preset distance is determined based on a point cloud data network; obtaining all first evaluation task point data sets after all evaluation task points are calculated, calculating evaluation distances corresponding to all first evaluation task points, wherein the evaluation distances are obtained based on the distance from a departure point to the evaluation task point and the distance from the evaluation task point to a target task point, and screening and sorting the evaluation distances to obtain an evaluation distance minimum value; substituting the minimum value of the evaluation distance into a point cloud data network to obtain task point information, namely, the task point is an important task point, iterating to obtain all important task points, and connecting the important task points to obtain an optimized path, namely, an optimal path. And the path obtained based on the interpolation algorithm is optimized, so that the path planning efficiency is higher, the accuracy is high and the error is small.
The application also discloses a path planning device based on the interpolation algorithm, which comprises: the system comprises a point cloud data collection module, a setting module, a judging module, a point cloud data denoising module, a path selecting module and a path optimizing module.
The point cloud data collection module is used for collecting task points in the target area, acquiring point cloud data information of the task points and constructing a point cloud data network;
the setting module is used for setting a first working mode and a second working mode of the sweeping robot;
the first working mode is full-coverage cleaning; the second working mode is targeted cleaning;
the judging module is used for judging whether the collected point cloud data needs to be subjected to denoising treatment or not based on the working mode of the sweeping robot;
the point cloud data denoising processing is realized by analyzing task points through a point cloud data network constructed based on the point cloud data information;
the path selecting module is used for selecting a path based on an interpolation algorithm according to the point cloud data network;
the interpolation algorithm is realized through a cubic spline interpolation function;
and the optimized path module is used for carrying out evaluation and compensation processing based on the selected path to obtain an optimal target path.
The application also discloses a device and a medium for path planning based on the interpolation algorithm, wherein the device comprises a memory and a processor, the memory is used for storing a computer program, and the processor runs the computer program to enable the computer device to execute the path planning method based on the interpolation algorithm; the medium stores computer program instruction code which, when executed, implements the path planning method based on interpolation algorithm described above.
The present application is not limited to the above-described specific embodiments, and various modifications may be made by those skilled in the art without inventive effort from the above-described concepts, and are within the scope of the present application.
Claims (10)
1. The path planning method based on the interpolation algorithm is characterized by comprising the following steps:
s01, collecting task points in a target area, acquiring point cloud data information of the task points, and constructing a point cloud data network;
s02, setting a first working mode and a second working mode of the sweeping robot;
the first working mode is full-coverage cleaning;
the second working mode is targeted cleaning;
s03, judging whether the collected point cloud data needs to be subjected to denoising processing or not based on the working mode of the sweeping robot;
the point cloud data denoising processing is realized by analyzing task points through a point cloud data network constructed based on the point cloud data information;
s04, selecting a path based on an interpolation algorithm according to the point cloud data network;
the interpolation algorithm is realized through a cubic spline interpolation function;
s05, performing evaluation and compensation processing based on the selected path to obtain the optimal target path.
2. The path planning method based on interpolation algorithm according to claim 1, wherein the step S01 is to collect task points in a target area, obtain point cloud data information of the task points, obtain data information including three-dimensional coordinate information by using a sensor mounted on a robot for sweeping, digitize the condition information of the task points, and transmit the condition information to a memory for storage, thereby constructing a point cloud data network.
3. The path planning method based on interpolation algorithm according to claim 1, wherein the first workpiece mode is full coverage cleaning, namely cleaning in the whole range is performed based on a constructed point cloud data network; the second working mode is target cleaning, namely local cleaning is carried out according to a target obstacle.
4. The path planning method based on interpolation algorithm according to claim 1, wherein for S03, determining whether the point cloud data denoising process is required includes:
based on a set working mode of the sweeping robot, when the first working mode is executed, denoising processing is not needed; when the second working mode is executed, denoising processing is needed to be carried out on the collected point cloud data;
the denoising process includes:
1) Analyzing the distribution number of task points in a preset area around each target task point, wherein the preset area around each target task point is determined based on a circumference range of a first preset length from the target task point;
2) If the number of task point distribution in the preset area around the target task point is larger than a second preset value, determining the target task point as a core task point;
3) Selecting a core task point for analysis, marking the core task points in a preset area around the core task point as a first core task point, and marking the core task points outside the preset area around the core task point as a second core task point;
4) Reserving the first core task point and task points in a preset area around the first core task point;
5) Repeating 4) -5) until all the core task point analysis is completed, and acquiring all the reserved task points.
And planning the path in the second working mode based on the reserved task points.
5. The path planning method based on interpolation algorithm according to claim 1, wherein the step S04 is to select a path based on interpolation algorithm; the interpolation algorithm is realized by a cubic spline interpolation function and comprises the following steps:
(1) K interpolation task points are selected on the constructed point cloud data network;
(2) Based on the combination of the K interpolation task points and coordinates of a departure task point A and a target task point B of the sweeping robot, two sequences I and J of the horizontal and vertical coordinates are obtained;
(3) Calculating a cubic spline interpolation function S (X), S (Y) corresponding to the abscissa and the ordinate;
(4) And solving numerical information corresponding to the corresponding cubic spline interpolation function to obtain point data information of the corresponding interpolation task and path information.
6. The introduced interpolation algorithm selection path of claim 5, wherein the selection path changes when any change occurs to the interpolation task point.
7. The path planning method based on interpolation algorithm according to claim 1, wherein the step S05 is to perform evaluation compensation processing based on the selected path to obtain an optimal target path; the evaluation compensation includes:
(0) Acquiring point cloud data information of all task points on the path, wherein the point cloud data information comprises a departure task point, a process point and a target task point;
(1) Selecting any task point in the path to be set as an evaluation task point;
(2) Judging the importance of the evaluation task point;
(3) The importance of the evaluation task points is judged by calculating the distance between the departure task point and each evaluation task point on the path and the obstacle, if the distance is greater than or equal to a first preset distance value, the point is a first evaluation task point, and if the distance is smaller than the first preset distance value, the point is a second evaluation task point and can be deleted; the first preset distance is determined based on a point cloud data network;
(4) Iterating until all evaluation task points are calculated, obtaining all first evaluation task point data sets, calculating evaluation distances corresponding to all first evaluation task points, and screening and sorting to obtain minimum evaluation distances;
(5) Substituting the minimum value of the evaluation distance into the point cloud data network to obtain data information of a task point corresponding to the minimum value of the evaluation distance, namely, the point is an important task point;
(6) And starting repeating by taking other task points as new starting task points until the starting task points are connected with target task points, obtaining all important task points, and connecting all the important task points on the path to obtain the optimal path.
8. A path planning apparatus based on an interpolation algorithm, the apparatus comprising: the system comprises a point cloud data collection module, a setting module, a judging module, a point cloud data denoising module, a path selecting module and a path optimizing module.
The point cloud data collection module is used for collecting task points in the target area, acquiring point cloud data information of the task points and constructing a point cloud data network;
the setting module is used for setting a first working mode and a second working mode of the sweeping robot; the first working mode is full-coverage cleaning; the second working mode is targeted cleaning;
the judging module is used for judging whether the collected point cloud data needs to be subjected to denoising treatment or not based on the working mode of the sweeping robot; the point cloud data denoising processing is realized by analyzing task points through a point cloud data network constructed based on the point cloud data information;
the path selecting module is used for selecting a path based on an interpolation algorithm according to the point cloud data network; the interpolation algorithm is realized through a cubic spline interpolation function;
and the optimized path module is used for carrying out evaluation and compensation processing based on the selected path to obtain an optimal target path.
9. An apparatus for path planning based on an interpolation algorithm, characterized in that the apparatus comprises a memory for storing a computer program and a processor for running the computer program to cause the computer apparatus to perform the path planning method based on an interpolation algorithm according to any one of claims 1 to 7.
10. A medium of path planning based on an interpolation algorithm, characterized in that the medium has stored computer program instruction code which, when executed, implements the path planning method based on an interpolation algorithm of any one of claims 1 to 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117891261A (en) * | 2024-03-15 | 2024-04-16 | 同济大学 | Autonomous planting system, device and storage medium based on intelligent agriculture multi-machine cooperation |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117891261A (en) * | 2024-03-15 | 2024-04-16 | 同济大学 | Autonomous planting system, device and storage medium based on intelligent agriculture multi-machine cooperation |
| CN117891261B (en) * | 2024-03-15 | 2024-05-28 | 同济大学 | Autonomous planting system, device and storage medium based on intelligent agriculture multi-machine cooperation |
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