CN113050627A - Path planning method and device, mobile robot and computer storage medium - Google Patents
Path planning method and device, mobile robot and computer storage medium Download PDFInfo
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Abstract
A path planning method, a device, a mobile robot and a computer storage medium are provided, the method comprises: acquiring the current pose and the target pose of the mobile robot; obtaining a plurality of point layers according to the current pose and the target pose, wherein each point layer comprises a plurality of alternative intermediate points; performing curve fitting on alternative intermediate points in every two adjacent point layers, and determining the cost of at least part of curve segments obtained by fitting, wherein each curve segment is used for connecting the two adjacent point layers; and selecting a target curve segment between every two adjacent point layers according to the cost of the curve segment to obtain a target path from the current pose to the target pose. According to the method, the target path is obtained according to the multiple curve segments, and compared with path planning in a single-segment form, the success rate and robustness are remarkably improved.
Description
Technical Field
The invention relates to the technical field of mobile robots, in particular to a path planning method and device, a mobile robot and a computer storage medium.
Background
Automatic Guided Vehicle (AGV), Autonomous Mobile Robot (AMR), fork truck and other Mobile robots are one of the key devices of modern logistics systems, and can accurately walk and stop to a designated place according to path planning and operation requirements, so as to complete tasks such as material handling and conveying. The path planning is an important link in the operation control of the mobile robot and determines the traveling route of the mobile robot.
The relative navigation of the mobile robot means that when the target pose or the surrounding environment changes, the mobile robot needs to dynamically adjust the motion track according to the changed target pose and the surrounding environment to complete the final task. The success rate of the current relative navigation scheme of the mobile robot is low.
Disclosure of Invention
In this summary, concepts in a simplified form are introduced that are further described in the detailed description. This summary of the invention is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.
A first aspect of an embodiment of the present invention provides a path planning method, where the method includes:
acquiring the current pose and the target pose of the mobile robot;
obtaining a plurality of point layers according to the current pose and the target pose, wherein each point layer comprises a plurality of candidate intermediate points;
performing curve fitting on alternative intermediate points in every two adjacent point layers, and determining the cost of at least part of curve segments obtained by fitting, wherein each curve segment is used for connecting the two adjacent point layers;
and selecting a target curve segment between every two adjacent point layers according to the cost of the curve segment to obtain a target path from the current pose to the target pose.
In one embodiment, the curve fitting the alternative intermediate points in each two adjacent point layers includes: and respectively performing curve fitting by taking each alternative intermediate point in the previous point layer and each alternative intermediate point in the next point layer as a head end point and a tail end point to obtain a curve segment connecting the previous point layer and the next point layer.
In one embodiment, the curve fitting comprises curve fitting according to the position, orientation and curvature of the alternative intermediate points.
In one embodiment, the positions of the alternative intermediate points on each point layer are evenly distributed in that point layer.
In one embodiment, the direction of the alternative intermediate point is parallel to the direction of the target pose.
In one embodiment, determining the cost for each curve segment comprises: selecting a plurality of discrete points in each curve segment; respectively calculating the cost of each discrete point; and obtaining the cost of the curve segment according to the cost of a plurality of discrete points on the same curve segment.
In one embodiment, calculating the cost for each discrete point comprises: and calculating the cost of each discrete point according to the curvature of the discrete point, the distance between the discrete point and a central line and the distance between the discrete point and an obstacle, wherein the central line is a connecting line of the current position and the target position.
In one embodiment, calculating the cost for each discrete point based on the curvature of each discrete point, the distance between the discrete point and the centerline, and the distance between the discrete point and the obstacle comprises: and carrying out weighted summation on the curvature of each discrete point, the distance between the discrete point and the central line and the distance between the discrete point and the obstacle to obtain the cost of the discrete point.
In one embodiment, the determining the cost of at least part of the curve segments fitted comprises: eliminating curve segments colliding with obstacles and/or curve segments exceeding a preset curvature; the cost of each curve segment remaining is determined.
In one embodiment, the selecting a target curve segment between every two adjacent point layers according to the cost of the curve segment includes: and sequentially selecting target curve segments between every two adjacent point layers according to the sequence from the current pose to the target pose or the sequence from the target pose to the current pose, wherein after a previous target curve segment is determined, a next target curve segment is selected from a plurality of curve segments connected with the previous target curve segment.
In one embodiment, the selecting a next target curve segment from a plurality of curve segments connected to the previous target curve segment includes: and selecting a target intermediate point with the minimum cost from at least part of alternative intermediate points in a next point layer of the previous target curve segment according to a state transition equation to obtain the next target curve segment connecting the previous target curve segment and the target intermediate point, wherein the state transition equation is constructed according to the cost of the curve segment.
A second aspect of the embodiments of the present invention provides a path planning apparatus, where the path planning apparatus includes a storage device and a processor, where the storage device stores thereon a computer program executed by the processor, and the computer program, when executed by the processor, executes the path planning method described above.
A third aspect of the embodiments of the present invention provides a mobile robot, including a mobile robot body and the path planning apparatus described above.
A fourth aspect of the embodiments of the present invention provides a computer storage medium, where a computer program is stored on the computer storage medium, and the computer program executes the path planning method described above when running.
The path planning method, the device, the mobile robot and the computer storage medium sample a plurality of point layers between the current pose and the target pose, the target path is obtained according to a plurality of curve segments, the path form is richer, and the success rate and the robustness are obviously improved compared with the path planning in a single-segment form.
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The above and other objects, features and advantages of the present invention will become more apparent by describing in more detail embodiments of the present invention with reference to the attached drawings. The accompanying drawings are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings, like reference numbers generally represent like parts or steps.
Fig. 1 shows a schematic block diagram of an example electronic device for implementing a path planning method according to an embodiment of the invention;
FIG. 2 shows a schematic flow diagram of a path planning method according to one embodiment of the invention;
fig. 3 shows a schematic block diagram of a path planning apparatus according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, exemplary embodiments according to the present invention will be described in detail below with reference to the accompanying drawings. It is to be understood that the described embodiments are merely a subset of embodiments of the invention and not all embodiments of the invention, with the understanding that the invention is not limited to the example embodiments described herein. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the invention described herein without inventive step, shall fall within the scope of protection of the invention.
First, an example electronic device 100 for implementing a path planning method of an embodiment of the present invention is described with reference to fig. 1.
As shown in fig. 1, electronic device 100 includes one or more processors 102, one or more memory devices 104. Optionally, in some embodiments, the electronic device 100 may further include an input device 106, an output device 108, and a communication device 110. These components are interconnected by a bus system 112 and/or other form of connection mechanism (not shown). It should be noted that the components and structure of the electronic device 100 shown in fig. 1 are exemplary only, and not limiting, and the electronic device may have other components and structures as desired.
The processor 102 may be a Central Processing Unit (CPU), a Field Programmable Gate Array (FPGA) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device 100 to perform desired functions.
The input device 106 may be a device used by a user to input instructions and may include one or more of buttons, a keyboard, a mouse, a microphone, a touch screen, and the like.
The output device 108 may output various information (e.g., images or sounds) to an outside (e.g., a user), and may include one or more of a light emitting device, a display, a speaker, and the like.
The communication device 110 is configured to receive or transmit data via a network, which may specifically include a wireless network, such as WiFi, 2G, 3G, 4G, 5G, or a combination thereof. The communication device may also include a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies. When the electronic device 100 is implemented as a controller of a mobile robot, the communication device 110 is mainly responsible for data interaction between the mobile robot and the control system, such as receiving a scheduling instruction of the control system, and feeding back status information of the mobile robot to the control system.
It should be noted that the components and structure of the electronic device 100 shown in fig. 1 are only exemplary, and although the electronic device 100 shown in fig. 1 includes a plurality of different devices, some of the devices may not be necessary, some of the devices may be more, and the like, as required, and the invention is not limited thereto.
A path planning method 200 according to an embodiment of the invention is described below with reference to fig. 2. The path method 200 may be used for a mobile robot, or for a control system for a mobile robot.
As shown in fig. 2, the path planning method 200 may include the following steps:
in step S210, the current pose and the target pose of the mobile robot are acquired;
in step S220, a plurality of point layers are obtained according to the current pose and the target pose, each point layer including a plurality of candidate intermediate points;
in step S230, curve fitting is performed on the candidate intermediate points in each two adjacent point layers, and the cost of at least part of the curve segments obtained by fitting is determined, where each curve segment is used to connect the two adjacent point layers;
in step S240, a target curve segment is selected between every two adjacent point layers according to the cost of the curve segment, and a target path from the current pose to the target pose is obtained.
The path planning method 200 of the embodiment of the invention is used for path planning for a mobile robot, and the mobile robot can be applied to the fields of manufacturing, warehousing and distribution, logistics transportation and the like, and can be specifically realized as a mobile robot adopting a non-predetermined path guiding mode. Past mobile robots have typically been re-planned based on purely controlled PID or based on bezier curves. The pure control PID refers to that after the mobile robot part enters the bottom of the tray, feedback control is carried out by taking the central line of the chassis as a target. The scheme does not consider the distance between the mobile robot and the barrier, has the risk of collision, is only suitable for the situation that the current pose is very close to the target pose, cannot process the complex environment, and has low robustness. The re-planning based on the Bezier curve refers to planning a new end path by performing two-point fitting with the Bezier curve according to the current pose and the motion state of the trolley and combining the target state. The problem of the scheme is that obstacle information is not considered, path parameters obtained by fitting two points are too few, and the form is too simple, so that the problem of a scene needing obstacle avoidance is difficult to solve; for the condition that the pose change of the end point is large, the success rate of the method is low. In contrast, the path planning method 200 according to the embodiment of the present invention samples a plurality of point layers between the current pose and the target pose, fits curve segments between each adjacent point layer, and splices the target path according to the plurality of curve segments, so that the path has a richer form and more inflection points, and the success rate and robustness are significantly improved compared with the single-segment path planning.
The controller-based path planning can realize the real-time path planning in the running process of the mobile robot. The path planning method 200 of the embodiment of the present invention may be re-planning initiated in real time during the operation of the mobile robot, so as to implement relative navigation of the mobile robot. When the target pose or the surrounding environment of the mobile robot changes in the operation process, the path planning method 200 of the embodiment of the invention can be adopted to re-plan the path in real time according to the changed target pose and the surrounding environment, and dynamically adjust the motion track of the mobile robot to complete the final task. For example, when the mobile robot receives a new target pose sent by the control system, or when the original path of the mobile robot is invalid, the path planning method 200 of the present application may be triggered to plan a new target route in real time. Among them, the reasons for the path failure may include that the mobile robot collides, or the mobile robot detects an obstacle, etc.
After the path planning is triggered, step S210 is executed to obtain a current pose and a target pose of the mobile robot, where the current pose includes a current position and a current pose, and the target pose includes a target position and a target pose.
Illustratively, the target pose may be issued by the control system to the mobile robot. In particular, the control system may issue a task message to the mobile robot, for example, the task may be to inform the mobile robot to transport an item to be transported from a starting location to an end location. In some examples, when the mobile robot is at a start position of a task, a pose of an end position of the task may be taken as a target pose of the path plan. When the mobile robot is not at the starting position of the task, the pose of the starting position of the task can be used as a target pose, so that the mobile robot runs from the current position to the starting position of the task to acquire the article to be transported.
The current pose can be acquired by the mobile robot in real time. For example, different types of mobile robots may acquire the current pose in different ways, e.g., based on laser scanning, inertial navigation, image recognition, etc. Determining the current pose based on the laser module means that a laser reflection plate is installed at a predetermined position around a traveling area of the mobile robot, and the laser module of the mobile robot emits a laser beam through a laser scanner while collecting the laser beam reflected by the reflection plate, thereby determining the current pose thereof. The current pose is determined based on the inertial measurement unit, namely a gyroscope is arranged on the mobile robot, a positioning block is arranged on the ground of a driving area, and the mobile robot determines the current pose through comprehensive calculation of a gyroscope deviation signal and a traveling distance encoder and comparison and correction of ground positioning block signals. The image identification-based determination of the current pose means that the mobile robot acquires image information of the surrounding environment of the running path in real time by using a camera and compares the image information with information in an established image library of the surrounding environment of the running path to determine the current pose. The mobile robot can also adopt an image recognition method based on the two-dimension code, namely, a camera is used for scanning the ground two-dimension code, and the current pose is determined by a code scanning positioning technology. In this embodiment, various feasible manners may also be adopted to determine the current pose, and the specific manner of determining the current pose is not limited in the embodiment of the present invention.
After the current pose and the target pose of the mobile robot are obtained, step S220 is executed to obtain a plurality of point layers according to the current pose and the target pose, where each point layer includes a plurality of candidate intermediate points.
Illustratively, sampling may be performed between the current pose and the target pose to obtain a plurality of point layers arranged at intervals. The sampling can be performed in the direction of the connection line from the current position to the target position to obtain a plurality of point layers arranged at intervals, and the length direction of the point layers can be perpendicular to the direction of the connection line between the current position and the target point. Each point layer is an area expected to be passed by the mobile robot, and is a set of a plurality of candidate intermediate points, and the candidate intermediate points in the same point layer are uniformly distributed in a continuous space. In other words, each point layer represents an area through which the mobile robot is expected to pass, and each alternative intermediate point on each point layer represents a location in the area. Alternatively, the dot layer may be elongated rectangular regions arranged at intervals in the direction of the center line, and of course, the dot layer may have other shapes, such as an oval shape. Illustratively, the shape and size of the different dot layers may be the same, or the shape and size of the different dot layers may be different.
The point layers are arranged in order from far to near according to the distance from the target position, namely each point layer is provided with a uniquely determined front-driving point layer and a rear-driving point layer, the occupied spaces of different point layers are not intersected, and the mobile robot needs to pass through each point layer in sequence.
The number of point layers depends, for example, on the number of intermediate points to be passed through, or on the distance between the current position and the target position. The farther the distance is, the larger the number of dot layers, and the closer the distance is, the smaller the number of dot layers. Similarly, the spacing between adjacent layers of dots may also depend on the distance between the current location and the target location, e.g., the farther the distance, the larger the spacing, and the closer the distance, the smaller the spacing.
In step S230, curve fitting is performed on the candidate intermediate points in each two adjacent point layers, and the cost of at least part of the curve segments obtained by fitting is determined, wherein each curve segment is used for connecting the two adjacent point layers.
As described above, in the sampling region between the current position and the target position, the point layers are arranged in the order from far to near from the target position, and the finally determined target path needs to follow the spatial arrangement relationship of the point layers, that is, sequentially passes through one alternative intermediate point in each point layer, cannot jump over a certain point layer, and cannot disturb the order of passing through the point layers, so that an intermediate point fitting curve segment needs to be selected from between adjacent point layers.
For example, if k point layers are sampled between the current position and the target position, curve fitting is performed on candidate intermediate points in the first point layer and the second point layer to obtain a plurality of curve segments between the first point layer and the second point layer; and performing curve fitting on the alternative intermediate points in the second point layer and the third point layer to obtain a plurality of curve segments between the second point layer and the third point layer, and so on until a plurality of curve segments are obtained by fitting between every two adjacent point layers in the k point layers. As only one alternative intermediate point in each point layer can be finally selected to serve as the target intermediate point on the target path, the alternative intermediate points in the same point layer are not fitted with each other.
Further, fitting the curve segment requires circularly traversing all the candidate intermediate points in two adjacent point layers, and respectively taking each candidate intermediate point in the previous point layer and each candidate intermediate point in the next point layer as the head-end point fitting curve segment. That is to say, between every two adjacent point layers, the candidate intermediate points in the previous point layer and the candidate intermediate points in the next point layer are paired in a one-to-one combination manner, any two candidate intermediate points are taken as head and end points to fit a curve segment, any one candidate intermediate point in the previous point layer and each candidate intermediate point in the next point layer are fitted to obtain a curve segment, and similarly, any one candidate intermediate point in the next point layer and each candidate intermediate point in the previous point layer are also fitted to obtain a curve segment. For example, if the number of candidate intermediate points in the first point layer is m and the number of candidate intermediate points in the second point layer is n, m × n curve segments are obtained by fitting between the first point layer and the second point layer.
The information of each candidate intermediate point at least comprises the position and the direction of the candidate intermediate point, and a curve between the two points can be obtained through fitting according to the position and the direction. Further, the information of each alternative intermediate point may also include curvature to further enrich the parameters of the curve fit. And solving curve parameters by using the information of the alternative intermediate points as boundary conditions of curve fitting, namely obtaining a curve segment with the alternative intermediate points as head and tail end points. Illustratively, the curve forms that may be taken include, but are not limited to, spline curves, polynomial curves, bezier curves, and the like.
In one embodiment, after the curve segments are obtained through fitting, the curve segments colliding with the obstacles can be eliminated, so that the safety of the mobile robot running along the target path can be guaranteed to the maximum extent.
For example, whether the curve segment collides with the obstacle may be determined as follows: and judging whether each point on the contour of the mobile robot appears in the obstacle when the mobile robot runs according to the curve segment. And if any point on the outline of the mobile robot appears in the obstacle, determining that the curve segment collides with the obstacle, and rejecting the curve segment to ensure the operation safety of the mobile robot.
For example, whether each point on the mobile robot contour exists inside the obstacle can be judged through the vector product direction, specifically, a convex polygon surrounding the obstacle is determined, and for the P on the mobile robot contour, a vector between the P and each vertex of the convex polygon is obtained; and taking adjacent vectors in the anticlockwise direction or the clockwise direction for cross multiplication, and if the included angle between any two vectors is more than 180 degrees, indicating that the point P exists in the polygon. Based on the detection result, whether the pose of each discrete mobile robot on the curve segment collides with the obstacle or not can be detected, and the curve segment colliding with the obstacle is removed.
As another example, the culled curve segment may be a curve segment that exceeds a preset curvature. The curve segment exceeding the preset curvature may be a curve segment whose maximum curvature exceeds the preset curvature, or may be a curve segment whose average curvature exceeds the preset curvature. And the elimination of curve segments with curvature exceeding the standard can ensure the running stability of the mobile robot.
Illustratively, for some or all curve segments fitted between each two adjacent point layers, the cost of each curve segment is calculated. Wherein the partial curve segment may be a curve segment remaining after excluding a collision with an obstacle and/or a curve segment exceeding a preset curvature as described above. And the cost of the curve segment is used for measuring the quality of the curve segment, and finally the curve segment is selected according to the cost to be spliced, so that the optimal target path with the lowest total cost is obtained.
Considering the operation condition of the mobile robot, the advantages and disadvantages of the curve segment are represented in the aspects of avoiding obstacles, smoothing degree of the curve, distance deviating from the shortest path and the like, so in one embodiment, the calculation of the cost comprises three items: the average curvature, the average deviation from a center line and the average distance from an obstacle, wherein the center line is a connecting line between the current position and the target position.
In one embodiment, for each curve segment, the cost is calculated by: selecting a plurality of discrete points in the curve segment; respectively calculating the cost of each discrete point; and obtaining the cost of the curve segment according to the cost of a plurality of discrete points on the same curve segment.
Wherein calculating the cost for each discrete point comprises: the cost of each discrete point is calculated from its curvature, its distance from the centerline, and its distance from the obstacle. Specifically, for each discrete point, the curvature of the discrete point, the distance between the discrete point and the center line, and the distance between the discrete point and the obstacle may be weighted and summed to obtain the cost of the discrete point. Finally, the average cost of all discrete points on each curve segment can be taken as the overall cost of the curve segment.
In one embodiment, the cost of the curve segment may be stored in a node chain table manner, so as to facilitate subsequent dynamic planning.
The cost of at least part of the curve segment can be obtained by performing step S230. Then, in step S240, a target curve segment is selected between every two adjacent point layers according to the cost of the curve segment, and a target path from the current pose to the target pose is obtained. The target curve segments can be spliced to obtain the target path. Because the total cost of the target path is equal to the sum of the costs of the curve segments, the target path with the lowest total cost can be found according to the costs of the curve segments. Because the splicing process is a Ma's process without aftereffect, namely the splicing process is irrelevant to the previous state of the mobile robot, dynamic programming can be adopted for solving, and programming is carried out according to the Bellman optimality principle, so that the calculated amount is reduced.
Specifically, dynamic programming is a multi-stage decision-making optimal solution model, which adopts a bottom-up recursion mode to obtain an optimal solution of each subproblem, and further obtains an optimal solution of the original problem depending on the subproblems. That is, the original problem can be split into multiple sub-problems to solve the optimal solution, and in the process of recursion from bottom to top, each sub-problem obtained is the global optimal solution, so the original problem depending on the sub-problems is also the global optimal solution.
In the embodiment of the invention, the original problem is to obtain the target path, and the sub-problem is to obtain the optimal curve segment in the adjacent point layer. Because a certain relation exists between the sub-problems, namely the optimal curve segment between the current point layer and the next point layer depends on the curve segment between the current point layer and the previous point layer, an iterative recurrence formula, namely a state transition equation, needs to be established between the curve segments, and the solution is performed from bottom to top according to the state transition equation, so that the repeated calculation caused by the overlapping of the sub-problems is avoided, the overlapped sub-problems can be eliminated by adopting the bottom-to-top solution mode, and the calculation amount is reduced.
And the solution from bottom to top is that the problem on the upper layer and the problem on the upper layer are gradually solved according to the state equation from the subproblem which can not be continuously decomposed on the bottommost layer, and finally the optimal solution of the original problem is solved. In the embodiment of the invention, target curve segments are sequentially selected between every two adjacent point layers according to the sequence from the current pose to the target pose or the sequence from the target pose to the current pose. Wherein, after a previous target curve segment is determined, a subsequent target curve segment is selected among the plurality of curve segments connected to the previous target curve segment.
For example, in order to select an optimal subsequent target curve segment from the plurality of curve segments connected to the previous target curve segment, a target intermediate point with the smallest cost may be selected from at least some of the candidate intermediate points in the subsequent point layer of the previous target curve segment according to the state transition equation to obtain a subsequent target curve segment connecting the previous target curve segment and the target intermediate point. As described above, the state of each sub-problem can be solved according to the state transition equation, that is, the state of each sub-problem is the result of the previous stage state and the previous stage decision, so that the final result, that is, the target path with the lowest total cost, can be obtained by continuously looping iteration from bottom to top as long as three variables are defined.
In one embodiment of the invention, the state transition equations are constructed based on the cost of the curve segments. Specifically, a state transition equation may be established according to the sequence of the point layers, the candidate intermediate point with the lowest cost in each point layer is sequentially solved according to the cost of the curve segment to serve as the target intermediate point, the target curve segment is obtained according to the target intermediate point, and the target path is a path formed by connecting all the target curve segments. The state transition equation can be modeled as: r (P) ═ min { R (P ') + R (P, P ') }, where P is the current point (i.e., the current candidate intermediate point), P ' is the set of all the successors (i.e., the successor candidate intermediate points) to P, and R (P), R (P '), R (P, P ') represent the cost of P, the cost of P ' and the cost of the curve segment fitted by P and P ', respectively. The cost from the current P point to the succeeding P 'point is called the single step cost, and the cost of the P' point itself is called the succeeding cost. The physical meaning of the state transition equation is: the cost of the current point is equal to the minimum of the sum of all its successor point costs and the costs of the curve segments between the current point and the successor point.
The alternative intermediate point with the minimum cost in each point layer can be solved by recursion of the state transition equation according to the sequence of the point layers, the alternative intermediate point is used as a target intermediate point, a connecting line between the target intermediate point and the tail end of the last target curve segment is a target curve segment connecting the current point layer and the last point layer, and the target path is a path formed by connecting all the target curve segments, namely a path obtained by connecting all the target intermediate points. In the solving process, the solution may be recurred in the direction from the target position to the current position, or may be recurred in the direction from the current position to the target position, which is not limited in this embodiment of the present invention.
Based on the above description, the final target path can be planned. In the operation speed, due to the adoption of dynamic programming, large-area repeated redundant calculation is avoided, and the optimal path programming result can be output in a short time.
According to the embodiment of the invention, multi-point layer sampling is carried out between the current pose and the target pose, curve segments are obtained by fitting between the point layers, and the final target path is obtained according to the multiple curve segments, so that the path form is richer, the inflection points are more, and the success rate and the robustness are greatly improved compared with a single-segment path planning form of a Bezier curve.
In another aspect, an embodiment of the present invention further provides a path planning apparatus, and fig. 3 shows a schematic block diagram of a path planning apparatus 300 according to an embodiment of the present invention. The path planner 300 includes a storage 310 and a processor 320. Wherein the storage device 310 is used for storing program codes; the processor 320 is configured to execute the program code, and when the program code is executed, is configured to implement the path planning method described above. The path planning apparatus 300 according to the embodiment of the present invention may be implemented as a controller of a mobile robot, so that path re-planning may be performed in real time during the operation of the mobile robot. The path planning apparatus 300 may also be configured to control the mobile robot to move from the current pose to the target pose along the planned target path.
The storage 310 is a memory for storing processor-executable instructions, such as for storing processor-executable program instructions for implementing the corresponding steps in the path planning method 200 according to an embodiment of the present invention. Storage 310 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, Random Access Memory (RAM), cache memory (cache), and/or the like. The non-volatile memory may include, for example, Read Only Memory (ROM), hard disk, flash memory, etc.
The processor 320 may execute the program instructions stored by the storage device 310 to implement the functions of the embodiments of the invention described herein (implemented by the processor) and/or other desired functions, such as to perform the corresponding steps of the path planning method 200 according to the embodiments of the invention. Various applications and various data, such as various data used and/or generated by the applications, may also be stored in the computer-readable storage medium.
The processor 320 may be a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the path planner 300 to perform desired functions. The processor can execute the instructions stored in the storage device 310 to perform the path planning methods described herein. For example, processor 320 can include one or more embedded processors, processor cores, microprocessors, logic circuits, hardware Finite State Machines (FSMs), Digital Signal Processors (DSPs), or a combination thereof.
In one embodiment, the program instructions stored by the storage device 310, when executed by the processor 320, cause the path planner 300 to perform the following steps: acquiring the current pose and the target pose of the mobile robot; obtaining a plurality of point layers according to the current pose and the target pose, wherein each point layer comprises a plurality of alternative intermediate points; performing curve fitting on alternative intermediate points in every two adjacent point layers, and determining the cost of at least part of curve segments obtained by fitting, wherein each curve segment is used for connecting the two adjacent point layers; and selecting a target curve segment between every two adjacent point layers according to the cost of the curve segment to obtain a target path from the current pose to the target pose.
In one embodiment, the curve fitting the alternative intermediate points in each two adjacent point layers includes: and respectively performing curve fitting by taking each alternative intermediate point in the previous point layer and each alternative intermediate point in the next point layer as a head end point and a tail end point to obtain a curve segment connecting the previous point layer and the next point layer.
In one embodiment, the curve fitting comprises curve fitting according to the position, orientation and curvature of the alternative intermediate points.
In one embodiment, the positions of the alternative intermediate points on each point layer are evenly distributed in that point layer.
In one embodiment, the direction of the alternative intermediate point is parallel to the direction of the target pose.
In one embodiment, determining the cost for each curve segment comprises: selecting a plurality of discrete points in each curve segment; respectively calculating the cost of each discrete point; and obtaining the cost of the curve segment according to the cost of a plurality of discrete points on the same curve segment.
In one embodiment, calculating the cost for each discrete point comprises: and calculating the cost of each discrete point according to the curvature of the discrete point, the distance between the discrete point and a central line and the distance between the discrete point and an obstacle, wherein the central line is a connecting line of the current position and the target position.
In one embodiment, calculating the cost for each discrete point comprises: and carrying out weighted summation on the curvature of each discrete point, the distance between the discrete point and the central line and the distance between the discrete point and the obstacle to obtain the cost of the discrete point.
In one embodiment, the determining the cost of at least part of the curve segment comprises: eliminating curve segments colliding with obstacles and/or curve segments exceeding a preset curvature; the cost of each curve segment remaining is determined.
In one embodiment, the selecting a target curve segment between every two adjacent point layers according to the cost of the curve segment includes: and sequentially selecting target curve segments between every two adjacent point layers according to the sequence from the current pose to the target pose or the sequence from the target pose to the current pose, wherein after a previous target curve segment is determined, a next target curve segment is selected from a plurality of curve segments connected with the previous target curve segment.
In one embodiment, the selecting a next target curve segment from a plurality of curve segments connected to the previous target curve segment includes: and selecting a target intermediate point with the minimum cost from at least part of alternative intermediate points in a next point layer of the previous target curve segment according to a state transition equation to obtain the next target curve segment connecting the previous target curve segment and the target intermediate point, wherein the state transition equation is constructed according to the cost of the curve segment.
The embodiment of the invention also provides a mobile robot, which comprises a mobile robot body and a path planning device 300, wherein the path planning device 300 is used for realizing path planning for the mobile robot. Exemplarily, the mobile robot body comprises a mechanical system and a power system, wherein the mechanical system comprises a vehicle body, wheels, a steering device, a transfer device, a safety device and the like; the power system comprises a walking motor, a transplanting motor, a battery assembly, a charging device and the like. The above structure is only an example, and the mobile robot body may omit a part of the structure; the mobile robot body may also include other structures. The path planning apparatus 300 may be implemented as a controller of a mobile robot, and besides being capable of implementing path planning of the mobile robot, the path planning apparatus may also be used to control the mobile robot body to move from the current pose to the target pose according to a planned target path.
Furthermore, according to the embodiment of the present invention, a computer storage medium is also provided, on which program instructions are stored, and when the program instructions are executed by a computer or a processor, the program instructions are used to execute the corresponding steps of the path planning method 200 of the mobile robot according to the embodiment of the present invention, and the specific details thereof can be referred to above. The computer storage medium may include, for example, a memory card of a smart phone, a storage component of a tablet computer, a hard disk of a personal computer, a Read Only Memory (ROM), an Erasable Programmable Read Only Memory (EPROM), a portable compact disc read only memory (CD-ROM), a USB memory, or any combination of the above storage media. The computer-readable storage medium may be any combination of one or more computer-readable storage media.
The path planning method, the device, the mobile robot and the computer storage medium sample a plurality of point layers between the current pose and the target pose, the target path is obtained according to a plurality of curve segments, the path form is richer, the inflection points are more, and the success rate and the robustness are obviously improved compared with the path planning in a single-segment form.
Although the illustrative embodiments have been described herein with reference to the accompanying drawings, it is to be understood that the foregoing illustrative embodiments are merely exemplary and are not intended to limit the scope of the invention thereto. Various changes and modifications may be effected therein by one of ordinary skill in the pertinent art without departing from the scope or spirit of the present invention. All such changes and modifications are intended to be included within the scope of the present invention as set forth in the appended claims.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described device embodiments are merely illustrative, and for example, the division of the units is only one logical functional division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another device, or some features may be omitted, or not executed.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the invention and aiding in the understanding of one or more of the various inventive aspects. However, the method of the present invention should not be construed to reflect the intent: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
It will be understood by those skilled in the art that all of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where such features are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or other suitable processor may be used in practice to implement some or all of the functionality of some of the modules according to embodiments of the invention. The present invention may also be embodied as apparatus programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer storage media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
The above description is only for the specific embodiment of the present invention or the description thereof, and the protection scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the protection scope of the present invention. The protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (14)
1. A method of path planning, the method comprising:
acquiring the current pose and the target pose of the mobile robot;
obtaining a plurality of point layers according to the current pose and the target pose, wherein each point layer comprises a plurality of candidate intermediate points;
performing curve fitting on alternative intermediate points in every two adjacent point layers, and determining the cost of at least part of curve segments obtained by fitting, wherein each curve segment is used for connecting the two adjacent point layers;
and selecting a target curve segment between every two adjacent point layers according to the cost of the curve segment to obtain a target path from the current pose to the target pose.
2. The path planning method according to claim 1, wherein the curve fitting of the candidate intermediate points in each two adjacent point layers comprises:
and respectively performing curve fitting by taking each alternative intermediate point in the previous point layer and each alternative intermediate point in the next point layer as a head end point and a tail end point to obtain a curve segment connecting the previous point layer and the next point layer.
3. A path planning method according to claim 1 or 2, wherein the curve fitting comprises curve fitting according to the position, orientation and curvature of the candidate intermediate points.
4. A path planning method according to claim 3, characterized in that the positions of the alternative intermediate points in each point layer are evenly distributed in the point layer.
5. The path planning method according to claim 3, wherein the directions of the alternative intermediate points are parallel to the direction of the target pose.
6. The path planning method according to any one of claims 1-5, wherein determining the cost for each curve segment comprises:
selecting a plurality of discrete points in each curve segment;
respectively calculating the cost of each discrete point;
and obtaining the cost of the curve segment according to the cost of a plurality of discrete points on the same curve segment.
7. The path planning method of claim 6 wherein calculating the cost for each discrete point comprises:
and calculating the cost of each discrete point according to the curvature of the discrete point, the distance between the discrete point and a central line and the distance between the discrete point and an obstacle, wherein the central line is a connecting line of the current position and the target position.
8. The path planning method of claim 7 wherein calculating the cost for each discrete point based on the curvature of each discrete point, the distance between the discrete point and the centerline, and the distance between the discrete point and the obstacle comprises:
and carrying out weighted summation on the curvature of each discrete point, the distance between the discrete point and the central line and the distance between the discrete point and the obstacle to obtain the cost of the discrete point.
9. The path planning method according to any one of claims 1 to 8, wherein the determining the cost of at least part of the fitted curve segments comprises:
eliminating curve segments colliding with obstacles and/or curve segments exceeding a preset curvature;
the cost of each curve segment remaining is determined.
10. The path planning method according to claim 1 or 9, wherein the selecting a target curve segment between every two adjacent point layers according to the cost of the curve segment comprises:
and sequentially selecting target curve segments between every two adjacent point layers according to the sequence from the current pose to the target pose or the sequence from the target pose to the current pose, wherein after a previous target curve segment is determined, a next target curve segment is selected from a plurality of curve segments connected with the previous target curve segment.
11. The method according to claim 10, wherein selecting the next target curve segment from the plurality of curve segments connected to the previous target curve segment comprises:
and selecting a target intermediate point with the minimum cost from at least part of alternative intermediate points in a next point layer of the previous target curve segment according to a state transition equation to obtain the next target curve segment connecting the previous target curve segment and the target intermediate point, wherein the state transition equation is constructed according to the cost of the curve segment.
12. A path planner comprising a storage device and a processor, the storage device having stored thereon a computer program for execution by the processor, the computer program, when executed by the processor, performing the path planning method according to any of claims 1-11.
13. A mobile robot comprising a mobile robot body and a path planning apparatus according to claim 12.
14. A computer storage medium, characterized in that the computer storage medium has stored thereon a computer program which, when run, performs a path planning method according to any one of claims 1-11.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113492411A (en) * | 2021-09-10 | 2021-10-12 | 季华实验室 | Robot grabbing path planning method and device, electronic equipment and storage medium |
| CN114347041A (en) * | 2022-02-21 | 2022-04-15 | 汕头市快畅机器人科技有限公司 | Group robot control and pattern generation method |
| WO2022183790A1 (en) * | 2021-03-02 | 2022-09-09 | 北京旷视机器人技术有限公司 | Path planning method and apparatus, mobile robot, storage medium, and program |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110035087A1 (en) * | 2009-08-10 | 2011-02-10 | Samsung Electronics Co., Ltd. | Method and apparatus to plan motion path of robot |
| CN105549597A (en) * | 2016-02-04 | 2016-05-04 | 同济大学 | Unmanned vehicle dynamic path programming method based on environment uncertainty |
| 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 |
| CN109521763A (en) * | 2017-09-18 | 2019-03-26 | 百度（美国）有限责任公司 | The path optimization based on constraint smoothing spline for automatic driving vehicle |
| CN109542106A (en) * | 2019-01-04 | 2019-03-29 | 电子科技大学 | A kind of paths planning method under mobile robot multi-constraint condition |
| CN110908386A (en) * | 2019-12-09 | 2020-03-24 | 中国人民解放军军事科学院国防科技创新研究院 | Layered path planning method for unmanned vehicle |
| CN111338346A (en) * | 2020-03-05 | 2020-06-26 | 中国第一汽车股份有限公司 | Automatic driving control method and device, vehicle and storage medium |
| CN112393728A (en) * | 2020-10-23 | 2021-02-23 | 浙江工业大学 | Mobile robot path planning method based on A-algorithm and RRT-algorithm |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106114507B (en) * | 2016-06-21 | 2018-04-03 | 百度在线网络技术（北京）有限公司 | Local path planning method and device for intelligent vehicle |
| CN107664993A (en) * | 2016-07-29 | 2018-02-06 | 法乐第(北京)网络科技有限公司 | A kind of paths planning method |
| CN110262488B (en) * | 2019-06-18 | 2021-11-30 | 重庆长安汽车股份有限公司 | Automatic driving local path planning method, system and computer readable storage medium |
| CN111290385B (en) * | 2020-02-19 | 2023-10-03 | 达闼机器人股份有限公司 | Robot path planning method, robot, electronic equipment and storage medium |
| CN112099493B (en) * | 2020-08-31 | 2021-11-19 | 西安交通大学 | Autonomous mobile robot trajectory planning method, system and equipment |
| CN113050627A (en) * | 2021-03-02 | 2021-06-29 | 北京旷视机器人技术有限公司 | Path planning method and device, mobile robot and computer storage medium |
-
2021
- 2021-03-02 CN CN202110231697.3A patent/CN113050627A/en active Pending
- 2021-11-25 WO PCT/CN2021/133235 patent/WO2022183790A1/en active Application Filing
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110035087A1 (en) * | 2009-08-10 | 2011-02-10 | Samsung Electronics Co., Ltd. | Method and apparatus to plan motion path of robot |
| CN105549597A (en) * | 2016-02-04 | 2016-05-04 | 同济大学 | Unmanned vehicle dynamic path programming method based on environment uncertainty |
| 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 |
| CN109521763A (en) * | 2017-09-18 | 2019-03-26 | 百度（美国）有限责任公司 | The path optimization based on constraint smoothing spline for automatic driving vehicle |
| CN109542106A (en) * | 2019-01-04 | 2019-03-29 | 电子科技大学 | A kind of paths planning method under mobile robot multi-constraint condition |
| CN110908386A (en) * | 2019-12-09 | 2020-03-24 | 中国人民解放军军事科学院国防科技创新研究院 | Layered path planning method for unmanned vehicle |
| CN111338346A (en) * | 2020-03-05 | 2020-06-26 | 中国第一汽车股份有限公司 | Automatic driving control method and device, vehicle and storage medium |
| CN112393728A (en) * | 2020-10-23 | 2021-02-23 | 浙江工业大学 | Mobile robot path planning method based on A-algorithm and RRT-algorithm |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2022183790A1 (en) * | 2021-03-02 | 2022-09-09 | 北京旷视机器人技术有限公司 | Path planning method and apparatus, mobile robot, storage medium, and program |
| CN113492411A (en) * | 2021-09-10 | 2021-10-12 | 季华实验室 | Robot grabbing path planning method and device, electronic equipment and storage medium |
| CN113492411B (en) * | 2021-09-10 | 2021-11-30 | 季华实验室 | Robot grabbing path planning method and device, electronic equipment and storage medium |
| CN114347041A (en) * | 2022-02-21 | 2022-04-15 | 汕头市快畅机器人科技有限公司 | Group robot control and pattern generation method |
| CN114347041B (en) * | 2022-02-21 | 2024-03-08 | 汕头市快畅机器人科技有限公司 | Group robot control and pattern generation method |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2022183790A1 (en) | 2022-09-09 |
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