CN115328174A - Trajectory planning method, electronic device and storage medium - Google Patents
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- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0214—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
Abstract
The application discloses a trajectory planning method, an electronic device and a computer-readable storage medium. The method comprises the following steps: determining a local target point under the current pose; predicting a first track of a dynamic barrier under the current pose; planning a local path from the current pose to a local target point based on the first track; and planning the speed of moving from the current pose to the local target point along the local path to obtain a second track. By the method, the robustness of the trajectory planning can be improved.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a trajectory planning method, an electronic device, and a computer-readable storage medium.
Background
Trajectory planning techniques for robots typically include path planning and trajectory planning. Path planning of a robot in a dynamic scene cannot ensure safe driving of the robot due to lack of a time dimension. The time dimension of the trajectory planning is increased on the basis of the path planning, and the safety is higher compared with the path planning.
However, one possible way of trajectory planning is path + velocity planning, i.e. generating a geometric path and then distributing velocities over the path to generate a spatio-temporal trajectory. However, in the prior art, the robustness of the trajectory planning in this feasible manner is not high.
Disclosure of Invention
The application provides a track planning method, electronic equipment and a computer-readable storage medium, which can solve the problem of low robustness of track planning.
In order to solve the technical problem, the application adopts a technical scheme that: a trajectory planning method is provided. The method comprises the following steps: determining a local target point under the current pose; predicting a first track of a dynamic barrier under a current pose; planning a local path from the current pose to a local target point based on the first track; and planning the speed of moving from the current pose to the local target point along the local path to obtain a second track.
In order to solve the technical problem, the application adopts a technical scheme that: a trajectory planning method is provided. The method comprises the following steps: determining a local target point under the current pose; predicting a first track of the dynamic barrier under the current pose, wherein the first track is in a probability distribution mode; and planning a second track from the current pose to the local target point based on the probability distribution.
In order to solve the above technical problem, another technical solution adopted by the present application is: an electronic device is provided, which comprises a processor and a memory connected with the processor, wherein the memory stores program instructions; the processor is configured to execute the program instructions stored by the memory to implement the above-described method.
In order to solve the above technical problem, the present application adopts another technical solution: there is provided a computer readable storage medium storing program instructions that when executed are capable of implementing the above method.
Through the mode, the trajectory planning comprises a local path planning part and a speed planning part, and the motion trajectory (first trajectory) of the dynamic obstacle is considered in the local path planning, so that the probability of spatial conflict between the planned local path and the dynamic obstacle can be reduced, the probability of collision between the dynamic obstacle and the process of moving to a local target point along the local path can be reduced, the trajectory planning can further cope with the interference of the dynamic obstacle, and the robustness of the trajectory planning is improved.
Drawings
FIG. 1 is a schematic flow chart diagram illustrating an embodiment of a trajectory planning method of the present application;
FIG. 2 is a schematic flow chart diagram illustrating another embodiment of a trajectory planning method of the present application;
FIG. 3 is a schematic flow chart diagram illustrating a trajectory planning method according to another embodiment of the present application;
FIG. 4 is a schematic contour diagram of a first track;
FIG. 5 is a schematic diagram of a spatiotemporal map;
FIG. 6 is a schematic flow chart diagram illustrating a trajectory planning method according to another embodiment of the present application;
FIG. 7 is a schematic structural diagram of an embodiment of an electronic device of the present application;
FIG. 8 is a schematic structural diagram of an embodiment of a computer-readable storage medium of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "first", "second" and "third" in this application are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any indication of the number of technical features indicated. Thus, a feature defined as "first," "second," or "third" may explicitly or implicitly include at least one of the feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless explicitly specifically limited otherwise.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those skilled in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments without conflict.
Fig. 1 is a schematic flowchart of an embodiment of a trajectory planning method according to the present application. It should be noted that, if the result is substantially the same, the flow sequence shown in fig. 1 is not limited in this embodiment. As shown in fig. 1, the present embodiment may include:
s11: and determining a local target point under the current pose.
The local target point may be a point in the global path. A local map under the current pose can be obtained; and intercepting the local target point from the global path based on the local map. The local map contains environmental information observed by the robot in real time at the current pose. The global path is a pre-planned path from the starting point to the end point.
The scene where the robot is located may be a dynamic scene, and the position of the static obstacle in the dynamic scene may change due to human movement and the like, so that the local target point may fall within the static obstacle. And if the local target point falls in the static barrier, setting a relaxation amount, and searching the nearest feasible point in the relaxation range of the local target point to serve as a local planning target point. If no feasible target point exists in the relaxation range, the global path is re-planned to adapt to the change of the scene, and the local target point is re-determined based on the re-planned global path.
S12: and predicting a first track of the dynamic obstacle at the current pose.
The time length of the robot moving from the current pose to the local target point is t, and the first track of the dynamic obstacle may be a track of the dynamic obstacle within the time length t. The first trajectory may be in the form of a determined trajectory or a probability distribution of the trajectory. A determined trajectory of the dynamic obstacle over the duration T can be obtained by deterministic prediction. Alternatively, the probability distribution of the trajectory of the dynamic obstacle over the duration T may be derived from uncertainty predictions. The probability distribution can measure the uncertainty of the dynamic obstacle motion. The probability distribution may be, but is not limited to, a gaussian distribution.
S13: and planning a local path from the current pose to the local target point based on the first track.
It can be understood that when the first track is considered in planning the local path, the local path far away from the dynamic obstacle as far as possible can be planned, and the probability of collision between the robot and the dynamic obstacle is reduced. The algorithm according to which the local path is planned may be a, hybird a, dijkstra, etc. For the description of the specific algorithms a, hybird a, dijkstra, please refer to the related prior art, which is not repeated herein.
S14: and planning the speed of moving from the current pose to the local target point along the local path to obtain a second track.
Through the implementation of the embodiment, the trajectory planning of the method comprises a local path planning part and a speed planning part, and the motion trajectory (first trajectory) of the dynamic obstacle is considered in the local path planning, so that the probability of spatial collision between the planned local path and the dynamic obstacle can be reduced, the probability of collision between the dynamic obstacle and the process of moving to a local target point along the local path can be reduced, the trajectory planning can further cope with the interference of the dynamic obstacle, and the robustness of the trajectory planning is improved.
Fig. 2 is a schematic flow chart of another embodiment of the trajectory planning method of the present application. It should be noted that, if the result is substantially the same, the flow sequence shown in fig. 2 is not limited in this embodiment. In this embodiment, S12 is further expanded, and the form of the first trajectory is a gaussian probability distribution, as shown in fig. 2, the embodiment may include:
s21: a navigable area in the local map is determined.
The local map can be segmented to obtain the passable area. The segmentation may include binarization segmentation, edge segmentation, and the like. For example, a local map is divided into two, and a division result is obtained, in which an area with a pixel value of "-1" constitutes an impassable area (i.e., an area where a static obstacle is located), and an area with a pixel value of "0" constitutes a passable area.
It will be appreciated that methods that rely solely on global paths to avoid static obstacles are not applicable in dynamic scenarios. Therefore, in the embodiment, the area where the static barrier is located is determined through the local map observed at the current pose, and the local path planning is performed based on the area where the static barrier is located, so that the excessive dependence on the global path can be avoided, and the dynamic path planning method is suitable for a dynamic scene.
S22: and determining the passing cost of the adjacent point of the current pose in the passable area.
The pass cost includes at least a distance cost between the point of adjacency and the first trajectory.
It can be understood that the local map is divided into a plurality of grid points (pixel points), and in the process of local path planning, before the robot moves to the next step, cost calculation is performed on the feasible grid points (called as adjacent points) adjacent to the current pose, so as to determine how to move to the next step until the local target point is reached.
The passable area defines the adjacent points which can be reached by moving along the current pose, and the cost required for moving from the current pose to each expandable adjacent point, namely the passage cost, can be calculated. The expandable adjacent point can be an accessible adjacent point, an adjacent point which can be accessed and conforms to the kinematic constraint of the vehicle, and the like.
In the form of a first trajectory having a Gaussian distribution
For example, the calculation of the distance cost is explained. The calculation formula for determining the distance cost between the neighboring point and the first trajectory is as follows:
wherein n represents the number of dynamic obstacles,
the distance cost is expressed, x and y respectively represent the coordinates of the adjacent points in the directions of the horizontal axis and the vertical axis,
、
the first trajectory of the ith dynamic obstacle is represented as the mean of the corresponding gaussian distribution in the horizontal axis and the vertical axis respectively,
、
respectively represents the variance of the corresponding Gaussian distribution of the first track of the ith dynamic obstacle in the directions of the horizontal axis and the vertical axis,
representing the weight of the ith dynamic obstacle.
Taking the example of planning the local path by using hybird A algorithm, when the local path which accords with the vehicle kinematics is planned by using the hybird A algorithm, the maximum turning angle of the vehicle is set to be
The discrete sampling number of the turning angle is
The step length is L. When the target adjacent point is determined, discrete sampling is carried out on the turning angle which can be completed by the robot according to the current pose of the robot, and the adjacent point which accords with the kinematic constraint is generated according to the stepping length L and the like. When the passage cost of the adjacent point is calculated, the passage cost can also comprise the cost of a moving path from the starting point to the current pose
Heuristic cost from current pose to end point
And the change cost of the course angle from the current pose to the adjacent point
At least one of (a). For example, the calculation formula of the traffic cost is as follows:
wherein the content of the first and second substances,
the cost of the passage is represented by,
、
、
and
respectively represent
、
、
And
the weight of (c).
S23: and determining target adjacent points reached in the next step based on the traffic cost so as to obtain an initial local path from the current pose to the local target point.
For example, the adjacent point with the smallest traffic cost is used as the target adjacent point.
S24: and smoothing the initial local path to obtain a final local path.
A feasible channel (Corridor) can be determined for the path based on the initial local path, the feasible channel being bounded by static obstacles; the initial local path may be curve-fitted within the feasible channel to obtain a final local path. The final local path may be a bezier curve, a polynomial curve, or the like.
The final local path may be solved with the minimum sum of the distance of the final local path from the first trajectory and the curvature of the final local path as an optimization objective (soft constraint). In addition, in order to obtain the final local path, hard constraints can be constructed based on collision constraints, kinematic constraints and continuity of the segmented Bezier curve of the robot, so that the final local path can be effectively executed by the bottom layer within a safe range.
For example, the optimization function for solving the final local path basis is as follows:
wherein m represents the number of the segmented Bezier curves,
a smoothing term is represented, which may be represented by curvature,
the distance of the final local path from the first trajectory is represented, i.e. the optimization objective is to plan a smooth final local path away from the dynamic obstacle. Constraint of equality
Representing kinematic constraints and components of a robotContinuity constraints, inequalities, of the Bezier curve segment
Representing the collision constraints of the robot, it corresponds to the control points of the bezier curve being within the feasible channel Corridor.
Further, in step S13, the first trajectory may be considered or may not be considered when the speed planning is performed. Please refer to the related art, which is not described herein, for a specific description of the velocity planning when the first trajectory is not considered.
It is understood that the local path planned based on the first trajectory may not completely avoid all dynamic obstacles, i.e. there may be dynamic obstacles where the first trajectory has a spatial conflict with the local path. For this reason, the first trajectory may be considered in speed planning, that is, the second trajectory may be obtained based on the speed of the first trajectory planning to move from the current pose to the local target point along the local path, so as to avoid the dynamic obstacle in which the first trajectory has a spatial conflict with the local path. As described in detail below:
fig. 3 is a schematic flowchart of another embodiment of the trajectory planning method of the present application. It should be noted that, if the result is substantially the same, the flow sequence shown in fig. 3 is not limited in this embodiment. The present embodiment is a further extension to S13, where the form of the first trajectory is a probability distribution, as shown in fig. 2, the present embodiment may include:
s31: the contour of the first trajectory is determined below a probability threshold.
For example, if the probability threshold is 90%, the determined contour is the contour of the first trajectory with a 90% probability.
It will be appreciated that the probability threshold limits the degree of uncertainty in the motion of the dynamic obstacle and accordingly the first trajectory is applied to the velocity planning, limiting the degree of conservatism of the second trajectory.
S32: a contour of the first trajectory is determined that has a spatial conflict with the local path.
S33: the local path and the contour of the first trajectory with spatial conflict therewith are mapped to a spatio-temporal map.
S34: and searching a space-time map to obtain an initial speed curve.
The process of obtaining the initial velocity profile is similar to the process of obtaining the initial local path, except that the spread of the abutment points is required to satisfy the kinematic constraints of the robot. That is, the acceleration range of the robot is set under the hybird a algorithm
The number of samples is
Step time of
. And when the adjacent point is expanded, sampling in an acceleration range according to the current speed of the robot to obtain a target speed, calculating a second track according to kinematics and dynamics, and discarding the expansion of the adjacent point if the second track meets a static obstacle in the space-time map.
S35: and smoothing the initial speed curve to obtain a final speed curve.
The process of smoothing the initial velocity profile is similar to the process of smoothing the initial local path. For example, the final velocity curve is obtained by solving by using the acceleration/jerk value as an optimization target and using the continuity constraint of the segmented bezier curve, the speed/acceleration limit interval, the dynamics constraint and the collision constraint as soft constraints.
S31 to S35 will be described with reference to fig. 4 to 5:
fig. 4 is a schematic outline view of the first track. In fig. 4, a denotes a robot body, B denotes a dynamic obstacle, and B1 denotes an outline of the first trajectory at the probability threshold.
FIG. 5 is a schematic diagram of a spatiotemporal map. In the left diagram of fig. 5, a denotes a robot body, a denotes a local path, V1 denotes a dynamic obstacle 1, V1 denotes a first trajectory of the dynamic obstacle 1, V2 denotes a dynamic obstacle 2, and V2 denotes a first trajectory of the dynamic obstacle 2. The right side diagram is a space-time map, the horizontal axis t represents time, and the vertical axis S represents shiftDynamic displacement, x unf Representing dynamically constrained unreachable regions, x obs1 Denotes the mapping result of v1, x obs2 The mapping result of v2 is shown, and C represents the final velocity curve.
Fig. 6 is a schematic flowchart of a trajectory planning method according to another embodiment of the present application. It should be noted that, if the result is substantially the same, the flow sequence shown in fig. 6 is not limited in this embodiment. As shown in fig. 6, the present embodiment may include:
s41: and determining a local target point under the current pose.
A local map under the current pose can be obtained; and intercepting the local target point from the global path based on the local map.
S42: and predicting a first track of the dynamic obstacle under the current pose.
The first trajectory is in the form of a probability distribution.
S43: and planning a second track from the current pose to the local target point based on the probability distribution.
Local paths from the current pose to the local target points may be planned based on the probability distribution; and planning the speed of moving from the current pose to the local target point along the local path based on the probability distribution to obtain a second track.
For other detailed descriptions of the present embodiment, please refer to the previous embodiment, which is not repeated herein.
Through the implementation of the embodiment, the first track of the dynamic obstacle is considered when the motion track (the second track) of the robot is planned, so that the second track can avoid the dynamic obstacle, the track planning can deal with the interference of the dynamic obstacle, and the robustness of the track planning is improved. And because the form of the first track is probability distribution rather than a definite track, the motion uncertainty of the dynamic barrier can be measured, and the corresponding conservative degree is given to track planning.
Fig. 7 is a schematic structural diagram of an embodiment of the electronic device of the present application. As shown in fig. 7, the electronic device includes a processor 21, and a memory 22 coupled to the processor 21.
Wherein the memory 22 stores program instructions for implementing the method of any of the embodiments described above; processor 21 is operative to execute program instructions stored by memory 22 to implement the steps of the above-described method embodiments. The processor 21 may also be referred to as a CPU (Central Processing Unit). The processor 21 may be an integrated circuit chip having signal processing capabilities. The processor 21 may also be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
FIG. 8 is a schematic structural diagram of an embodiment of a computer-readable storage medium of the present application. As shown in fig. 8, the computer readable storage medium 30 of the embodiment of the present application stores program instructions 31, and the program instructions 31 implement the method provided by the above-mentioned embodiment of the present application when executed. The program instructions 31 may form a program file stored in the computer-readable storage medium 30 in the form of a software product, so that a computer device (which may be a personal computer, a server, or a network device) or a processor (processor) executes all or part of the steps of the method according to the embodiments of the present application. And the aforementioned computer-readable storage medium 30 includes: various media capable of storing program codes, such as a usb disk, a mobile hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, or terminal devices, such as a computer, a server, a mobile phone, and a tablet.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a unit is merely a logical division, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit. The above are only embodiments of the present application, and not intended to limit the scope of the present application, and all equivalent structures or equivalent processes performed by the present application and the contents of the attached drawings, which are directly or indirectly applied to other related technical fields, are also included in the scope of the present application.
Claims (13)
1. A trajectory planning method, comprising:
determining a local target point under the current pose;
predicting a first track of a dynamic obstacle under the current pose;
planning a local path from the current pose to the local target point based on the first trajectory;
and planning the speed of moving from the current pose to the local target point along the local path to obtain a second track.
2. The method of claim 1, wherein the determining the local target point at the current pose comprises:
acquiring a local map under the current pose;
and intercepting the local target point from a global path based on the local map.
3. The method of claim 2, wherein planning a local path from the current pose to the local target point based on the first trajectory comprises:
determining a passable area in the local map;
determining a passage cost of an adjacent point of the current pose in the passable area, wherein the passage cost at least comprises a distance cost between the adjacent point and the first track;
determining target adjacent points reached in the next step based on the traffic cost to obtain an initial local path from the current pose to the local target points;
and smoothing the initial local path to obtain a final local path.
4. The method of claim 3, wherein the first trajectory is in the form of a probability distribution.
5. The method of claim 4, wherein the probability distribution is a Gaussian distribution, and wherein the calculation formula for determining the distance cost between the neighboring point and the first trajectory is as follows:
wherein the content of the first and second substances,
representing the distance cost, x and y represent the coordinates of the adjacent point in the horizontal axis and the vertical axis respectively,
、
the first trajectory of the ith dynamic obstacle is represented as the mean of the corresponding gaussian distribution in the horizontal axis and the vertical axis respectively,
、
respectively representing the variance of the first trajectory of the ith dynamic obstacle in the directions of the horizontal axis and the vertical axis,
representing a weight of the ith dynamic obstacle.
6. The method of claim 3, wherein the smoothing the initial local path to obtain a final local path comprises:
determining a feasible channel based on the initial local path;
and performing curve fitting on the initial local path in the feasible channel to obtain a final local path.
7. The method of claim 6, wherein said curve fitting said initial local path within said feasible channel to obtain a final local path comprises:
and solving to obtain the final local path by taking the minimum sum of the distance between the final local path and the first track and the curvature of the final local path as an optimization target.
8. The method of claim 1, wherein the planning the velocity of the movement from the current pose to the local target point along the local path, resulting in a second trajectory, comprises:
and planning the speed of moving from the current pose to the local target point along the local path based on the first track to obtain the second track.
9. The method of claim 8, wherein the first trajectory is a probability distribution, and wherein planning the velocity from the current pose to the local target point based on the first trajectory comprises:
determining a contour of the first trajectory under a probability threshold;
determining an outline of the first trajectory having a spatial conflict with the local path;
mapping the local path and the contour of the first trajectory with spatial conflict thereto to a spatio-temporal map;
searching the space-time map to obtain an initial speed curve;
and smoothing the initial speed curve to obtain a final speed curve.
10. A trajectory planning method, comprising:
determining a local target point under the current pose;
predicting a first track of the dynamic barrier under the current pose, wherein the first track is in a probability distribution form;
planning a second trajectory from the current pose to the local target point based on the probability distribution.
11. The method of claim 10, wherein planning a second trajectory from the current pose to the local target point based on the probability distribution comprises:
planning a local path from the current pose to the local target point based on the probability distribution;
planning a velocity of the movement from the current pose to the local target point along the local path based on the probability distribution to obtain the second trajectory.
12. An electronic device comprising a processor, a memory coupled to the processor, wherein,
the memory stores program instructions;
the processor is configured to execute the program instructions stored by the memory to implement the method of any of claims 1-11.
13. A computer-readable storage medium, characterized in that the storage medium stores program instructions that, when executed, implement the method of any of claims 1-11.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103558856A (en) * | 2013-11-21 | 2014-02-05 | 东南大学 | Service mobile robot navigation method in dynamic environment |
| CN106774347A (en) * | 2017-02-24 | 2017-05-31 | 安科智慧城市技术（中国）有限公司 | Robot path planning method, device and robot under indoor dynamic environment |
| CN109885891A (en) * | 2019-01-24 | 2019-06-14 | 中国科学院合肥物质科学研究院 | A kind of intelligent vehicle GPU accelerates method for planning track parallel |
| CN111552296A (en) * | 2020-05-14 | 2020-08-18 | 宁波智能装备研究院有限公司 | Local smooth track planning method based on curved cylindrical coordinate system |
| CN113359757A (en) * | 2021-06-30 | 2021-09-07 | 湖北汽车工业学院 | Improved hybrid A-x algorithm unmanned vehicle path planning and trajectory tracking method |
| CN114047759A (en) * | 2021-11-08 | 2022-02-15 | 航天科工微电子系统研究院有限公司 | Local path planning method based on DWA and artificial potential field fusion |
| CN114647246A (en) * | 2022-03-24 | 2022-06-21 | 重庆长安汽车股份有限公司 | Local path planning method and system for time-space coupling search |
-
2022
- 2022-10-14 CN CN202211262323.9A patent/CN115328174A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103558856A (en) * | 2013-11-21 | 2014-02-05 | 东南大学 | Service mobile robot navigation method in dynamic environment |
| CN106774347A (en) * | 2017-02-24 | 2017-05-31 | 安科智慧城市技术（中国）有限公司 | Robot path planning method, device and robot under indoor dynamic environment |
| CN109885891A (en) * | 2019-01-24 | 2019-06-14 | 中国科学院合肥物质科学研究院 | A kind of intelligent vehicle GPU accelerates method for planning track parallel |
| CN111552296A (en) * | 2020-05-14 | 2020-08-18 | 宁波智能装备研究院有限公司 | Local smooth track planning method based on curved cylindrical coordinate system |
| CN113359757A (en) * | 2021-06-30 | 2021-09-07 | 湖北汽车工业学院 | Improved hybrid A-x algorithm unmanned vehicle path planning and trajectory tracking method |
| CN114047759A (en) * | 2021-11-08 | 2022-02-15 | 航天科工微电子系统研究院有限公司 | Local path planning method based on DWA and artificial potential field fusion |
| CN114647246A (en) * | 2022-03-24 | 2022-06-21 | 重庆长安汽车股份有限公司 | Local path planning method and system for time-space coupling search |
Non-Patent Citations (1)
| Title |
|---|
| 刘召等: "基于行人轨迹预测的全向移动机器人路径规划", 《计算机仿真》 * |
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