CN114578808A - Path planning method, electronic device, computer program product, and storage medium - Google Patents
Path planning method, electronic device, computer program product, and storage medium Download PDFInfo
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Abstract
The application discloses a path planning method, an electronic device, a computer program product and a storage medium, wherein the path planning method comprises the following steps: acquiring motion state information of the robot at different positions to obtain a plurality of pieces of motion state information; planning a plurality of motion trajectories based on the plurality of motion state information and the target position of the robot; evaluating the plurality of motion tracks based on at least one preset evaluation rule, and selecting an adaptive motion track; and controlling the robot to move based on the adaptive motion trail. By the method, the motion trail of the robot can be planned according to the motion state information of the robot, and the motion efficiency of the robot is improved.
Description
Technical Field
The present application relates to the field of robotics, and in particular, to a path planning method, an electronic device, a computer program product, and a storage medium.
Background
With the rapid development of computer, sensor, artificial intelligence and other technologies, robots are becoming more and more perfect and widely used in the fields of home service, logistics, detection, industry and the like. The path planning is one of key technologies in the field of robots, and in a dynamic scene facing an unknown environment, the robot needs to search for an obstacle-free path in an obstacle environment so as to perform safe and efficient mobile walking.
Disclosure of Invention
To solve the above technical problem, the present application provides a path planning method, an electronic device, a computer program product, and a storage medium.
In order to solve the above problems, the first technical solution provided by the present application is: a method of path planning is provided, the method comprising:
acquiring motion state information of the robot at different positions to obtain a plurality of pieces of motion state information;
planning a plurality of motion tracks based on the plurality of motion state information and the target position of the robot;
evaluating the plurality of motion tracks based on at least one preset evaluation rule, and selecting an adaptive motion track;
and controlling the robot to move based on the adaptive motion trail.
In order to solve the above technical problem, a second technical solution provided by the present application is: there is provided 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 path planning method as described above.
In order to solve the above technical problem, a third technical solution provided by the present application is: there is provided a computer program product comprising computer program instructions for causing a computer to implement a path planning method as described above.
In order to solve the above technical problem, a fourth technical solution provided by the present application is: there is provided a computer readable storage medium having stored thereon program instructions which, when executed, implement a path planning method as described above.
The application provides a path planning method, electronic equipment, a computer program product and a storage medium, wherein the path planning method obtains a plurality of motion state information by obtaining the motion state information of a robot at different positions; planning a plurality of motion tracks based on the plurality of motion state information and the target position of the robot; evaluating the plurality of motion tracks based on a preset evaluation rule, and selecting an adaptive motion track; and controlling the robot to move based on the adaptive motion trail. By the method, the motion track of the robot can be planned according to the motion state information of the robot, and the motion track is evaluated according to a plurality of preset evaluation rules to select the most suitable motion track, so that the motion efficiency of the robot can be effectively improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts. Wherein:
fig. 1 is a schematic flowchart of a first embodiment of a path planning method provided in the present application;
fig. 2 is a schematic flow chart of a second embodiment of a path planning method provided in the present application;
FIG. 3 is a schematic illustration of the acquisition of multiple accelerations of FIG. 2;
fig. 4 is a schematic flow chart of a third embodiment of a path planning method provided in the present application;
FIG. 5 is a schematic illustration of the acquisition of a plurality of variable accelerations of FIG. 4;
fig. 6 is a schematic flowchart of a fourth embodiment of a path planning method provided in the present application;
fig. 7 is a schematic flow chart of a fifth embodiment of a path planning method provided in the present application;
fig. 8 is a schematic flowchart of a sixth embodiment of a path planning method provided in the present application;
fig. 9 is a schematic flowchart of a seventh embodiment of a path planning method provided in the present application;
FIG. 10 is a schematic structural diagram of an embodiment of an electronic device provided in the present application;
FIG. 11 is a schematic structural diagram of an embodiment of a computer-readable storage medium provided in the present application.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, embodiments accompanying the present application are described in detail below with reference to the accompanying drawings. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. It should be further noted that, for the convenience of description, only some of the structures related to the present application are shown in the drawings, not all of the structures. 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", etc. in this application are used to distinguish between different objects and not to describe a particular order. Furthermore, the terms "include" and "are provided," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
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. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
A Dynamic Window Approach (DWA) is a local trajectory planning algorithm, and the DWA plans a local trajectory of a robot by acquiring a plurality of groups of speeds and simulating a motion trajectory generated by uniform motion of the robot in a period of time in the future to acquire an optimal motion trajectory.
The inventor researches and discovers that when the DWA algorithm is used for planning the path of the robot, the uniform motion model is adopted when the motion track of the robot is predicted, the possibility that the robot moves in an accelerating or decelerating state is ignored, the predicted motion track cannot more comprehensively express the future motion state of the robot, and the further application of the DWA algorithm is limited.
Referring to fig. 1, fig. 1 is a schematic flow chart diagram of a first embodiment of a path planning method provided in the present application. The path planning method is applied to an electronic device, and the electronic device can be a server or a system formed by the server and the electronic device in a mutual matching mode; the electronic equipment can also be arranged in the robot to plan the motion trail of the robot.
As shown in fig. 1, the path planning method for a robot of the present application may be applied to an electronic device, and specifically includes the following steps:
s11: and acquiring the motion state of the robot to obtain a plurality of motion state information.
Specifically, in the embodiment of the application, the motion state of the robot is acquired to obtain the historical motion state and/or the current motion state of the robot, and a plurality of pieces of motion state information of the robot are acquired based on the historical motion state and/or the current motion state of the robot, where the motion state information is motion state information that may exist in a future period of time of the robot. The motion state information includes, but is not limited to, position information, speed variation information, sampling time, etc. of the robot, wherein the speed information includes, but is not limited to, linear speed and/or angular speed, etc.
S12: and planning a plurality of motion tracks based on the plurality of motion state information and the target position of the robot.
Acquiring an electronic map to determine a global planning route of the robot according to the current position and the target position of the robot; after the information of a plurality of motion states of the robot is obtained, the motion state of the robot can be predicted, the motion trail of the robot at the local position on the global planning route is planned, and a plurality of planned motion trails are obtained.
Optionally, simulation time may be set in the electronic device, where the simulation time is a time for planning a motion trajectory, and the simulation time may be adjusted according to an application scenario of the robot, and if the simulation time is too long, the planned motion trajectory is likely to deviate from a globally planned route, and if the simulation time is too short, the robot may vibrate due to frequent path planning.
S13: and evaluating the plurality of motion tracks based on at least one preset evaluation rule, and selecting the adaptive motion track.
After obtaining the plurality of planned motion trajectories, evaluating the plurality of motion trajectories based on a preset evaluation rule to select a motion trajectory adapted to the preset evaluation rule.
Specifically, the preset evaluation rule may include an azimuth evaluation function, a globally planned route fitting degree, a weight parameter, and the like, wherein the azimuth evaluation function is used for evaluating whether the movement track faces the target position, the globally planned route fitting degree is used for limiting the fitting degree of the movement track of the robot on the globally planned route, and the weight parameter is used for controlling the speed of the robot; the preset evaluation rule may further include rules such as whether an obstacle exists on the motion trajectory, a distance between the motion trajectory and the obstacle, and the preset evaluation rule is not specifically limited herein. Based on a preset evaluation rule, the adaptive motion trail is selected, the adaptive motion trail can ensure that the robot can reasonably avoid obstacles and move continuously when moving, the robot can reach a target position at a higher speed, the oscillation phenomenon of the robot in the moving process is reduced as much as possible, and the moving efficiency of the robot is improved.
S14: and controlling the robot to move based on the adaptive motion trail.
After the adaptive motion trail is selected, the robot can be controlled to move according to the adaptive motion trail. And planning each local motion track of the global planning route until the robot reaches the target position in the process of reaching the target position.
In the embodiment of the application, the motion state of the robot is obtained, and a plurality of pieces of motion state information are obtained; planning a plurality of motion tracks based on the plurality of motion state information and the target position of the robot; evaluating the plurality of motion tracks based on at least one preset evaluation rule, and selecting an adaptive motion track; and controlling the robot to move based on the adaptive motion trail. By the method, the motion track of the robot can be planned according to the motion state information of the robot, and the motion efficiency of the robot is improved.
Referring to fig. 2-4, fig. 2 is a schematic flow chart of a second embodiment of a path planning method provided by the present application, fig. 3 is a schematic diagram of acquiring multiple accelerations in fig. 2, and fig. 4 is a schematic diagram of acquiring multiple variable accelerations in fig. 2. As shown in fig. 2, the motion state information includes a speed variation parameter, and the step S11 further includes the steps of:
s21: and constructing a parameter sampling space.
After a plurality of speed change parameters of the robot in a future period of time are obtained, a parameter sampling space is constructed. Optionally, in order to improve the feasibility of the robot motion, the sampling range of the speed variation parameter may be limited. The limiting condition may be, but is not limited to, one or more of a speed limit of the robot, a motor torque limit of the robot, an obstacle avoidance limit of the robot, and the like.
Alternatively, the speed variation parameter may be, but is not limited to, acceleration and/or variable acceleration.
In an embodiment, when the speed change parameter is an acceleration, and the parameter sampling space is an acceleration sampling space, in order to reduce sampling workload of the electronic device and improve sampling efficiency of the electronic device, the acceleration sampling space may include three accelerations, i.e., acceleration a, acceleration-a, and acceleration 0, and the electronic device may sample three motion states, i.e., acceleration, deceleration, and uniform velocity, of the robot at each sampling time. Specifically, in order to avoid obstacle avoidance difficulty caused by excessive acceleration of the robot, the acceleration may be, but is not limited to, 1.
In another embodiment, when the speed variation parameter is a variable acceleration and the parameter sampling space is a variable acceleration sampling space, in order to reduce the sampling workload of the electronic device and improve the sampling efficiency of the electronic device, the variable acceleration sampling space may include three variable accelerations, namely variable acceleration j, variable acceleration-j and 0, and the electronic device may sample motion states of the robot, such as variable acceleration, uniform acceleration, and uniform speed, at each sampling time.
S22: sampling is carried out in the parameter sampling space according to each preset sampling time, and speed change parameters of a plurality of preset sampling periods are obtained.
In the constructed parameter sampling space, samples of a plurality of speed change parameters are acquired according to each preset sampling time so as to obtain the speed change parameters of a plurality of preset sampling periods. The preset sampling time can be set according to the application scene and the daily motion condition of the robot.
In the embodiment of the application, a parameter sampling space is constructed; sampling is carried out in the parameter sampling space according to each preset sampling time, and speed change parameters of a plurality of preset sampling periods are obtained. The path planning method obtains the speed change condition of the robot based on a plurality of speed change parameters obtained by sampling in a parameter sampling space, simulates the motion trail of the robot in a period of time in the future based on the speed change condition, and the obtained motion trail comprises the accelerated or decelerated motion of the robot.
Optionally, after obtaining the speed variation parameters of a plurality of preset sampling periods, the path planning method further includes: and converting the speed change parameters of the plurality of preset sampling periods into the speed parameters of the plurality of preset sampling periods, and planning the motion trail based on the speed parameters.
Specifically, in an embodiment, when the speed variation parameter is an acceleration, a speed variation graph of a plurality of preset sampling periods may be plotted based on a plurality of accelerations, as shown in fig. 3. According to the speed change graphs of a plurality of preset sampling periods acquired by the electronic equipment, the motion condition of the robot on the local route can be simulated, so that the motion track of the robot on the local route can be calculated.
In another embodiment, when the speed variation parameter is a variable acceleration, the acceleration variation maps of a plurality of preset sampling periods may be correspondingly plotted based on the variable accelerations, and further the speed variation maps of the plurality of preset sampling periods may be plotted, as shown in fig. 4. According to the acquired speed change graphs of a plurality of preset sampling periods, the motion condition of the robot on the local route can be simulated, so that the motion track of the robot on the local route can be calculated.
As shown in the speed change diagram of fig. 5, compared with the acceleration sampling, the acceleration sampling is performed to obtain a richer robot motion trajectory, and the motion trajectory includes not only the acceleration or deceleration motion of the robot, but also the forward and backward motion, so that the robot can flexibly move when facing an abrupt obstacle, and the flexibility of the robot motion is effectively improved. In a specific embodiment, acceleration sampling or variable acceleration sampling can be selected according to a robot application scene and a past motion condition, when the application scene of the robot is complex, the variable acceleration sampling can be performed, and when the application scene of the robot is simple, the acceleration sampling can be performed in order to reduce a calculation process.
Referring to fig. 5, fig. 5 is a schematic flow chart of a third embodiment of the path planning method provided in the present application. As shown in fig. 5, the preset evaluation rule includes a curvature evaluation rule, and the step S13 includes the steps of:
s31: and fitting each motion track to obtain an arc spline of the motion track.
And after the plurality of motion tracks are obtained, evaluating the plurality of motion tracks based on a preset evaluation rule so as to select the optimal motion track most suitable for the preset evaluation rule. Specifically, each motion trajectory is fitted to obtain a circular-arc spline corresponding to the motion trajectory.
S32: and mapping the track points of the motion track into the arc spline, and calculating the curvature of each track point in the arc spline.
And after the arc spline is obtained, mapping the track points of the motion trail into the arc spline, and calculating the curvature of each track point in the arc spline.
S33: the maximum curvature and/or the maximum rate of change of curvature is obtained from the curvatures of all the trace points.
After the curvatures of all track points corresponding to the motion track are obtained, the curvature change rate is calculated based on the curvatures of the front track point and the rear track point, and the maximum curvatures and/or the maximum curvature change rate of all track points are obtained.
S34: and evaluating the plurality of motion tracks based on the maximum curvature and/or the maximum curvature change rate, and selecting the motion track with the maximum curvature or the minimum maximum curvature change rate.
In one embodiment, the maximum curvature of the motion track is obtained and evaluated, and the maximum curvature is used as a curvature cost parameter of the motion track; in another embodiment, the maximum curvature change rate of the motion track is obtained and evaluated, and the maximum curvature change rate is used as the curvature cost parameter of the motion track.
After the curvature cost parameters of the plurality of motion tracks are obtained, the motion track with the minimum curvature cost parameter, namely the motion track with the minimum maximum curvature and/or maximum curvature change rate, can be selected, so that a relatively smooth track in the plurality of motion tracks can be screened out.
In the embodiment of the application, each motion track is fitted to obtain an arc spline of the motion track; mapping the track points of the motion track into the arc spline, and calculating the curvature of each track point in the arc spline; acquiring the maximum curvature and/or the maximum curvature change rate from the curvatures of all track points; and evaluating the plurality of motion tracks based on the maximum curvature and/or the maximum curvature change rate, and selecting the motion track with the minimum maximum curvature and/or the maximum curvature change rate. By the method, the relatively smooth track in the motion track can be screened out by calculating the maximum curvature and/or the maximum curvature change rate of the motion track, and the smoothness of the motion of the robot is improved.
Referring to fig. 6, fig. 6 is a schematic flow chart of a fourth embodiment of the path planning method provided in the present application. As shown in fig. 6, the speed variation parameter includes a variable acceleration, the preset sampling period includes a first sampling period and a second sampling period, the preset evaluation rule includes a speed evaluation rule, and the step S13 further includes the following steps:
s41: and acquiring a first variable acceleration of the first sampling period and a second variable acceleration of the second sampling period.
Specifically, each motion track comprises a plurality of preset sampling periods, each preset sampling period comprises a first sampling period and a second sampling period, the first sampling period and the second sampling period are front and back sampling periods, and a first variable acceleration of the first sampling period and a second variable acceleration of the second sampling period are obtained.
S42: and outputting a speed cost parameter of each preset sampling period based on the first variable acceleration, the second variable acceleration and the speed evaluation rule.
And after the first variable acceleration and the second variable acceleration are obtained, a speed evaluation rule is obtained, and when the first variable acceleration and the second variable acceleration are matched with the speed evaluation rule, a speed cost parameter of each preset sampling period is output.
S43: and acquiring the average speed cost parameter of the motion trail based on the speed cost parameters of all the preset sampling periods.
And acquiring the average value of the speed cost parameters of all the preset sampling periods based on the speed cost parameters of all the preset sampling periods of the motion trail, and taking the average value as the average speed cost parameter of the motion trail.
S44: and evaluating the plurality of motion tracks based on the average speed cost parameter of the motion tracks, and selecting the motion track with the minimum average speed cost parameter.
And obtaining average speed cost parameters of the plurality of motion tracks, evaluating the plurality of motion tracks, and selecting the motion track with the minimum average speed cost parameter to screen out the track with relatively smooth speed change in the plurality of motion tracks.
In the embodiment of the application, a first variable acceleration of a first sampling period and a second variable acceleration of a second sampling period are obtained; outputting a speed cost parameter of each preset sampling period based on the first variable acceleration, the second variable acceleration and a speed evaluation rule; acquiring an average speed cost parameter of the motion trail based on the speed cost parameters of all preset sampling periods; and evaluating the plurality of motion tracks based on the average speed cost parameter of the motion tracks, and selecting the motion track with the minimum average speed cost parameter. By the method, the track with relatively smooth speed change in the motion track can be screened out by calculating the variable acceleration change condition of each preset sampling period in the motion track, and the smoothness of the motion of the robot is further improved.
Further, the speed evaluation rule includes: in response to the first variable acceleration and the second variable acceleration being first values, presetting a speed cost parameter of a sampling period as the first values; in response to that the first variable acceleration or the second variable acceleration is a first numerical value, presetting a speed cost parameter of a sampling period as a first speed cost parameter; and in response to the fact that the first variable acceleration and the second variable acceleration are not both the first value, presetting the speed cost parameter of the sampling period as a first speed cost parameter, wherein the first speed cost parameter is smaller than the second speed cost parameter.
In this embodiment, the first value may be, but is not limited to, 0, the first speed cost parameter may be, but is not limited to, 100, and the second speed cost parameter may be, but is not limited to, 200, and the user may adjust the first value, the first speed cost parameter, and the second speed cost parameter according to an application scenario of the robot, where the values of the first value, the first speed cost parameter, and the second speed cost parameter are not specifically limited.
Specifically, when the first variable acceleration and the second variable acceleration are 0, the accelerations in the first sampling period and the second sampling period of the motion trajectory are not changed, that is, the robot may be in a uniform velocity or uniform acceleration motion state, and at this time, the velocity cost parameter of the preset sampling period is 0.
When the first variable acceleration or the second variable acceleration is 0, the robot in the first sampling period or the second sampling period of the motion trajectory is in a variable-speed motion state, that is, the robot may undergo a process from acceleration to a constant speed, from the constant speed to acceleration, from deceleration to the constant speed, or from the constant speed to deceleration in the first sampling period and the second sampling period, and at this time, the speed cost parameter of the preset sampling period is 100.
When the first variable acceleration and the second variable acceleration are not 0, the robot in the first sampling period and the second sampling period of the motion trajectory is in a variable-speed motion state, that is, the robot may undergo a process from acceleration to deceleration and from deceleration to acceleration in the first sampling period and the second sampling period, and at this time, the speed cost parameter of the preset sampling period is 200.
After the speed cost parameter of each preset sampling period of the motion track is obtained, the average speed cost parameter of the motion track can be obtained, and the motion track is evaluated based on the average speed cost parameters of all the motion tracks, so that the motion track with relatively smooth speed change is screened out.
Referring to fig. 7, fig. 7 is a schematic flow chart of a fifth embodiment of a path planning method provided in the present application. As shown in fig. 7, the preset evaluation rule includes an effective distance evaluation rule, and the step S13 further includes the steps of:
s51: and acquiring the linear distance between the starting point position and the target position of the robot.
And after the plurality of motion tracks are obtained, evaluating the plurality of motion tracks based on a preset evaluation rule so as to select the optimal motion track most suitable for the preset evaluation rule. Specifically, the linear distance between the starting position and the target position of the robot may be acquired.
S52: and acquiring the arc length of the motion trail, and calculating the difference value between the arc length and the linear distance.
And after the linear distance of the robot is obtained, further obtaining the arc lengths of the plurality of motion tracks, and calculating the difference value between the arc lengths of the plurality of motion tracks and the linear distance.
S53: and calculating the ratio of the difference value of the arc length and the straight line distance to the arc length to obtain the distance cost parameter of the motion trail.
Calculating the ratio of the difference between the arc length and the linear distance of the plurality of motion tracks to the arc length, and taking the ratio as the distance cost parameter of the plurality of motion tracks, as shown in the following formula:
wherein, costlAs a distance cost parameter,/abIs the linear distance between the starting position and the target position, dabIs the arc length of the motion trajectory.
S54: and evaluating the plurality of motion tracks based on the distance cost parameters of the motion tracks, and selecting the motion track with the minimum distance cost parameter.
And evaluating the plurality of motion tracks based on the obtained distance cost parameters of the plurality of motion tracks so as to screen out tracks with high motion efficiency.
In the embodiment of the application, the linear distance between the starting point position and the target position of the robot is obtained; acquiring the arc length of the motion trail, and calculating the difference value between the arc length and the linear distance; and calculating the ratio of the difference value of the arc length and the straight line distance to the arc length to obtain the distance cost parameter of the motion trail. By the method, the track with high motion efficiency in the motion track can be screened out by calculating the arc length of the motion track and the linear distance between the starting point position and the target position, and the motion efficiency of the robot is improved.
Optionally, the method for evaluating multiple motion trajectories of the robot may be a combination of the fourth embodiment, the fifth embodiment, and the sixth embodiment of the path planning method of the present application to screen out a motion trajectory with good smoothness and high efficiency, or may be any combination of the fourth embodiment, the fifth embodiment, and the sixth embodiment of the present application.
Referring to fig. 8, fig. 8 is a schematic flow chart of a path planning method according to a sixth embodiment of the present application. As shown in fig. 8, the path planning method further includes the following steps:
s61: the method comprises the steps of obtaining a motion map of the robot and obtaining position information of the robot.
Specifically, when planning a path of a robot, a motion map of the robot needs to be acquired, wherein the motion map includes position information of a plurality of obstacles; the size of the robot and the current position information of the robot are also acquired.
S62: and planning a global motion path based on the motion map, the position information and the target position.
Based on the acquired motion map, the position information and the target position, a global motion path from the starting point position to the target position of the robot can be planned.
S63: and establishing a robot motion model based on the global motion path.
Based on the obtained global motion path, taking the current position of the robot as a center, extracting a plurality of dynamic key points in the global motion path, and establishing a robot motion model. And after a robot motion model is obtained, local path planning is performed on a plurality of dynamic key points for a plurality of times.
S64: and based on the robot motion model, obtaining a motion track matched with a preset evaluation rule, and controlling the robot to move.
Based on a robot motion model, an acceleration sampling space or a variable acceleration sampling space is constructed and sampled, the collected acceleration or variable acceleration is converted into a speed change diagram of the robot in a period of time in the future, the speed is substituted into the robot motion model, simulation in a preset time is carried out, and then a plurality of motion tracks of each dynamic key point are planned. And according to a preset evaluation rule, obtaining a motion track adapted to the preset evaluation rule, namely the optimal motion track of the dynamic key point, controlling the robot to move according to the motion track of the dynamic key point, and planning the path of the next dynamic key point until the robot reaches a target position.
In the embodiment of the application, a motion map of the robot is obtained, and position information of the robot is obtained; planning a global motion path based on the motion map, the position information and the target position; establishing a robot motion model based on the global motion path; and based on the robot motion model, obtaining a motion track matched with a preset evaluation rule, and controlling the robot to move. By the method, the local motion trail of the robot can be planned by constructing the acceleration sampling space or the variable acceleration sampling space, so that the motion trail of the robot is enriched, and the motion flexibility of the robot is improved.
Specifically, the path planning method is applied to electronic equipment, and the electronic equipment can be arranged in the robot or external equipment. Optionally, the electronic device may be built in a robot, when the robot obtains a cleaning instruction, an indoor map and current position information are obtained, a global motion path is planned, a motion model is established, an acceleration sampling space or a variable acceleration sampling space is constructed to perform acceleration or variable acceleration sampling, a plurality of groups of local motion tracks are simulated by using the sampled speed, a matched local motion track is selected, and the robot is controlled to move according to the planned local motion track. The electronic device can also be applied to robots needing path planning, such as other household robots, service robots and the like, and the application scene of the electronic device is not particularly limited.
Referring to fig. 9-10, fig. 9 is a schematic structural diagram of a first embodiment of an electronic device provided in the present application, and fig. 10 is a schematic structural diagram of a second embodiment of the electronic device provided in the present application. As shown in fig. 9, the electronic device comprises a memory 52 and a processor 51 connected to each other.
The memory 52 is used for storing program instructions for implementing the path planning method according to any of the above embodiments.
The processor 51 is operative to execute program instructions stored in the memory 52.
The processor 51 may also be referred to as a CPU (Central Processing Unit). The processor 51 may be an integrated circuit chip having the processing capability for signaling. The processor 51 may also be a general purpose processor, a Digital Signaling 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.
The storage 52 may be a memory bank, a TF card, etc., and may store all information in the electronic device, including the input raw data, the computer program, the intermediate operation results, and the final operation results. It stores and retrieves information based on the location specified by the controller. With the memory, the string matching prediction device has a memory function, and normal operation can be guaranteed. The memory of the string matching prediction device can be classified into a main memory (internal memory) and an auxiliary memory (external memory) according to the use, and also into an external memory and an internal memory. The external memory is usually a magnetic medium, an optical disk, or the like, and can store information for a long period of time. The memory refers to a storage component on the main board, which is used for storing data and programs currently being executed, but is only used for temporarily storing the programs and the data, and the data is lost when the power is turned off or the power is cut off.
As shown in fig. 10, in one embodiment, an electronic device is provided that includes a sampling module 53, a planning module 54, an evaluation module 55, and a control module 56. The sampling module 53 is configured to obtain a motion state of the robot, and obtain a plurality of pieces of motion state information according to the motion state of the robot; the planning module 54 is configured to plan a plurality of motion trajectories based on the plurality of motion state information and a target position of the robot; the evaluation module 55 is configured to evaluate the plurality of motion trajectories based on at least one preset evaluation rule, and select an adaptive motion trajectory; the control module 56 is used for controlling the robot to move based on the adapted motion track.
In the several embodiments provided in the present application, it should be understood that the disclosed method and apparatus may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of modules or units 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.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.
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 integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a system server, a network device, or the like) or a processor (processor) to execute all or part of the steps of the method of the embodiments of the present application.
Referring to fig. 11, fig. 11 is a schematic structural diagram of an embodiment of a computer-readable storage medium provided in the present application. The computer readable storage medium of the present application stores a program file 61 capable of implementing all the above path planning methods, where the program file 61 may be stored in the storage medium in the form of a software product, and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) to execute all or part of the steps of the methods of the embodiments of the present application. The aforementioned storage device includes: various media capable of storing program codes, such as a usb disk, a portable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, or electronic devices, such as a computer, a server, a mobile phone, and a tablet.
In one embodiment, a computer program product or computer program is provided that includes computer instructions stored in a computer readable storage medium. The computer instructions are read by a processor of a computer device from a computer-readable storage medium, and the computer instructions are executed by the processor to cause the computer device to perform the steps in the above-mentioned method embodiments.
In addition, if the above functions are implemented in the form of software functions and sold or used as a standalone product, the functions may be stored in a storage medium readable by a mobile terminal, that is, the present application also provides a storage device storing program data, which can be executed to implement the method of the above embodiments, the storage device may be, for example, a usb disk, an optical disk, a server, etc. That is, the present application may be embodied as a software product, which includes several instructions for causing an intelligent terminal to perform all or part of the steps of the methods described in the embodiments.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing mechanisms, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and the scope of the preferred embodiments of the present application includes additional implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
The logic and/or steps represented in the flowcharts or otherwise described herein, such as an ordered listing of executable instructions that can be viewed as implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device (e.g., a personal computer, server, network device, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions). For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
The above description is only for the purpose of illustrating embodiments of the present application and is not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application or are directly or indirectly applied to other related technical fields, are also included in the scope of the present application.
Claims (11)
1. A method of path planning for a robot, the method comprising:
acquiring the motion state of the robot to obtain a plurality of pieces of motion state information;
planning a plurality of motion tracks based on the plurality of motion state information and the target position of the robot;
evaluating the plurality of motion tracks based on at least one preset evaluation rule, and selecting an adaptive motion track;
and controlling the robot to move based on the adaptive motion trail.
2. The path planning method according to claim 1,
the motion state information includes a speed change parameter, and the obtaining of the motion state of the robot and the obtaining of the plurality of motion state information includes:
constructing a parameter sampling space;
sampling is carried out in the parameter sampling space according to each preset sampling time, and the speed change parameters of a plurality of preset sampling periods are obtained.
3. The path planning method according to claim 2, wherein the obtaining the speed variation parameters for a plurality of preset sampling periods comprises:
and converting the speed change parameters of the preset sampling periods into the speed parameters of the preset sampling periods, and planning the motion trail based on the speed parameters.
4. The path planning method according to claim 3, wherein the preset evaluation rule comprises a curvature evaluation rule, wherein,
the evaluating the plurality of motion trajectories based on at least one preset evaluation rule, and the selecting the adapted motion trajectory comprises:
fitting each motion track to obtain an arc spline of the motion track;
mapping the track points of the motion trail into the arc spline, and calculating the curvature of each track point in the arc spline;
obtaining the maximum curvature and/or the maximum curvature change rate from the curvatures of all the track points;
and evaluating the plurality of motion tracks based on the maximum curvature and/or the maximum curvature change rate, and selecting the motion track with the maximum curvature and/or the minimum curvature change rate.
5. The path planning method according to claim 3, wherein the speed variation parameter comprises a variable acceleration, the preset sampling period comprises a first sampling period and a second sampling period, and the preset evaluation rule comprises a speed evaluation rule, wherein,
the evaluating the plurality of motion trajectories based on at least one preset evaluation rule, and the selecting the adapted motion trajectory comprises:
acquiring a first variable acceleration of the first sampling period and a second variable acceleration of the second sampling period;
outputting a speed cost parameter of each preset sampling period based on the first variable acceleration, the second variable acceleration and the speed evaluation rule;
acquiring an average speed cost parameter of the motion trail based on the speed cost parameters of all the preset sampling periods;
and evaluating the plurality of motion tracks based on the average speed cost parameter of the motion tracks, and selecting the motion track with the minimum average speed cost parameter.
6. The path planning method according to claim 5,
the speed evaluation rule includes:
responding to the first variable acceleration and the second variable acceleration as first values, wherein the speed cost parameter of the preset sampling period is the first value;
responding to the first variable acceleration or the second variable acceleration as a first numerical value, wherein the speed cost parameter of the preset sampling period is a first speed cost parameter;
and in response to that the first variable acceleration and the second variable acceleration are not both a first value, the speed cost parameter of the preset sampling period is a second speed cost parameter, wherein the first speed cost parameter is smaller than the second speed cost parameter.
7. The path planning method according to claim 3, wherein the preset evaluation rule comprises an effective distance evaluation rule, wherein,
the evaluating the plurality of motion trajectories based on at least one preset evaluation rule, and the selecting the adapted motion trajectory comprises:
acquiring a linear distance between the starting position of the robot and the target position;
acquiring the arc length of the motion track, and calculating the difference between the arc length and the linear distance;
calculating the ratio of the difference value of the arc length and the linear distance to the arc length to obtain a distance cost parameter of the motion track;
and evaluating the plurality of motion tracks based on the distance cost parameters of the motion tracks, and selecting the motion track with the minimum distance cost parameter.
8. The path planning method according to claim 1, characterized in that the path planning method comprises:
acquiring a motion map of the robot and acquiring position information of the robot;
planning a global motion path based on the motion map, the position information and the target position;
establishing a robot motion model based on the global motion path;
and acquiring a motion track adapted to the preset evaluation rule based on the robot motion model, and controlling the robot to move.
9. An electronic device comprising a processor, a memory coupled to the processor, wherein,
the memory stores program instructions;
the processor is used for executing the program instructions stored in the memory to realize the path planning method of any one of claims 1 to 8.
10. A computer program product comprising computer program instructions for causing a computer to implement the path planning method of any one of claims 1 to 8.
11. A computer-readable storage medium storing program instructions which, when executed, implement the path planning method of any one of claims 1 to 8.
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| CN202210024091.7A CN114578808A (en) | 2022-01-10 | 2022-01-10 | Path planning method, electronic device, computer program product, and storage medium |
| PCT/CN2022/117288 WO2023130755A1 (en) | 2022-01-10 | 2022-09-06 | Path planning method, electronic device, computer program product and storage medium |
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| CN202210024091.7A CN114578808A (en) | 2022-01-10 | 2022-01-10 | Path planning method, electronic device, computer program product, and storage medium |
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