CN112015186A - Robot path planning method and device with social attributes and robot - Google Patents
Robot path planning method and device with social attributes and robot Download PDFInfo
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Abstract
The invention discloses a robot path planning method with social attributes, a device and a robot, wherein the method comprises the following steps: detecting the target position of the pedestrian in the robot sensing area relative to the robot laser coordinate system according to the laser point cloud data; constructing a semi-elliptical pedestrian cost map by taking the target position as a center and the current position from the pedestrian to the robot as a long axis direction, wherein the cost value of a grid closer to the center of the pedestrian is larger in the pedestrian cost map; and (4) carrying out path search on the pedestrian cost map by adopting a preset algorithm to establish an optimal driving path for the robot. The optimal path generated by the method has high flexibility and good trafficability, and can be distinguished from the pedestrian and the common barrier to be treated in the process of obstacle avoidance, and the pedestrian can be selected to detour when the pedestrian is relatively far away from the pedestrian, so that the requirement of the pedestrian on a walking comfortable space is met, the robot is better integrated into the human environment, and the application range of the robot and the use experience of a user are improved.
Description
[ technical field ] A method for producing a semiconductor device
The invention relates to the field of intelligent robots, in particular to a robot path planning method and device with social attributes and a robot.
[ background of the invention ]
With the rapid development of mobile robot technology in recent years, scenes of robot work gradually appear in life, such as: airport guidance, catering, hospital disinfection, office building distribution and the like. In these scenarios, the robot needs to have safety, stability and motion intelligence at the same time. At present, service robot in the trade basically reaches the requirement in the aspect of security and stability, and is less strong in the aspect of motion intellectuality, mainly shows that the robot can independently plan out a shortest path when going to the destination to can keep away the barrier safely in the motion process, but keep away the barrier in-process and do not distinguish people and ordinary object, often the robot just begins to stop or detour near the nearer distance of people that can appear, and this kind of motion action can cause the uncomfortable to people.
[ summary of the invention ]
The invention provides a robot path planning method and device with social attributes and a robot, and solves the technical problem that the user experience is influenced because pedestrians and common objects are not distinguished in the robot path planning in the prior art.
The technical scheme for solving the technical problems is as follows: a robot path planning method with social attributes comprises the following steps:
s1, acquiring laser point cloud data, and detecting the target position of the pedestrian in the robot sensing area relative to the robot laser coordinate system according to the laser point cloud data;
s2, constructing a semi-elliptical pedestrian cost map by taking the target position as a center and the current position of the pedestrian to the robot as a long axis direction, wherein the cost value of a grid which is closer to the center of the pedestrian is larger in the pedestrian cost map;
and S3, performing path search on the pedestrian cost map by adopting a preset algorithm, and establishing an optimal driving path for the robot.
In a preferred embodiment, the detecting a target position of a pedestrian in a robot sensing area relative to a robot laser coordinate system according to the laser point cloud data specifically includes the following steps:
s101, identifying at least one human leg in the laser point cloud data by adopting a template matching method, and generating a first position coordinate of the human leg under a robot laser coordinate system;
s102, acquiring first position coordinates corresponding to all the human legs respectively, calculating the distance between any two human legs, and associating the two human legs closest to each other as the human legs of the same pedestrian;
and S103, taking the central positions of the two legs of the same pedestrian as the target position of the pedestrian relative to the laser coordinate system of the robot.
In a preferred embodiment, the identifying at least one human leg in the laser point cloud data by using a template matching method and generating a first position coordinate of the human leg under a robot laser coordinate system specifically includes the following steps:
s1011, acquiring laser point cloud data, and performing clustering matching on the laser point cloud data and a preset human leg template to generate a plurality of laser point clusters for representing different human legs;
s1012, calculating the mass center of the laser spot cluster, and recording as a first position coordinate of a human leg corresponding to the laser spot cluster in a robot laser coordinate system;
the preset human leg template comprises the width of a single leg and the distance between the two legs when the two legs are parallel, the sum of the widths of the two legs when the two legs are closed and the width of the single leg when the two legs are staggered.
In a preferred embodiment, the constructing a semi-elliptical pedestrian cost map by using the target position as the center and the current position of the pedestrian to the robot as the major axis direction specifically includes the following steps:
s201, with the target position of the pedestrian relative to a robot laser coordinate system as a center, the current position of the pedestrian to the robot is a long axis direction, the width of a single leg is set as a semi-long axis, the distance between two legs when the two legs are parallel is set as a semi-short axis, and a semi-elliptical pedestrian cost map is constructed;
s202, assigning a value to the cost value of each grid in the pedestrian cost map by adopting a preset formula, wherein the preset formula is as follows:
wherein A represents a preset maximum cost value, (m)x,my) Representing each grid in the pedestrian cost map in an elliptical coordinate system xpHypGrid coordinates of the bottom.
In a preferred embodiment, the method for searching a path on a cost map of a robot by using a preset algorithm to establish an optimal driving path for the robot specifically includes the following steps:
s301, searching a minimum cost estimation value from the current position to the target position according to an A-x algorithm and the cost value of each grid in the pedestrian cost map;
s302, determining that the search path of the minimum cost estimation value is the optimal driving path from the current position to the target position.
A second aspect of the embodiments of the present invention provides a robot path planning apparatus with social attributes, including a location obtaining module, a map building module, and a path searching module,
the position acquisition module is used for acquiring laser point cloud data and detecting the target position of a pedestrian in a robot sensing area relative to a robot laser coordinate system according to the laser point cloud data;
the map building module is used for building a semi-elliptical pedestrian cost map by taking the target position as a center and the current position of the pedestrian to the robot as a long axis direction, wherein the cost value of a grid which is closer to the center of the pedestrian is larger in the pedestrian cost map;
the path searching module is used for searching paths on the pedestrian cost map by adopting a preset algorithm and establishing an optimal driving path for the robot.
In a preferred embodiment, the position obtaining module specifically includes:
the human leg position identification unit is used for identifying at least one human leg in the laser point cloud data by adopting a template matching method and generating a first position coordinate of the human leg under a robot laser coordinate system;
the association unit is used for acquiring first position coordinates corresponding to all the human legs respectively, calculating the distance between any two human legs and associating the two human legs closest to the distance with the human legs of the same pedestrian;
and the target position acquisition unit is used for taking the central positions of two human legs of the same pedestrian as the target positions of the pedestrian relative to the laser coordinate system of the robot.
In a preferred embodiment, the human leg position identification unit specifically includes:
the device comprises a clustering unit, a processing unit and a control unit, wherein the clustering unit is used for acquiring laser point cloud data, and performing clustering matching on the laser point cloud data and a preset human leg template to generate a plurality of laser point clusters for representing different human legs;
the first calculating unit is used for calculating the mass center of the laser spot cluster and recording as a first position coordinate of a human leg corresponding to the laser spot cluster under a robot laser coordinate system;
the preset human leg template comprises the width of a single leg and the distance between the two legs when the two legs are parallel, the sum of the widths of the two legs when the two legs are closed and the width of the single leg when the two legs are staggered.
In a preferred embodiment, the map building module specifically includes:
the construction unit is used for constructing a semi-elliptical pedestrian cost map by taking the target position of the pedestrian relative to a robot laser coordinate system as a center, taking the current position of the pedestrian to the robot as a long axis direction, setting the width of a single leg as a semi-long axis, and setting the distance between two parallel legs as a semi-short axis;
the assignment unit is used for assigning the cost value of each grid in the pedestrian cost map by adopting a preset formula, wherein the preset formula is as follows:
wherein A represents a preset maximum cost value, (m)x,my) For pedestrian cost map each grid in elliptical coordinate system xpHypGrid coordinates of the bottom.
In a preferred embodiment, the path searching module specifically includes:
the second calculation unit is used for searching a minimum cost estimation value from the current position to the target position according to the A-x algorithm and the cost value of each grid in the pedestrian cost map;
and the path generating unit is used for determining the search path of the minimum cost estimation value as the optimal driving path from the current position to the target position.
A third aspect of the embodiments of the present invention provides a robot, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the robot path planning method with social attributes described above when executing the computer program.
A fourth aspect of embodiments of the present invention provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of the above-described robot path planning method with social attributes.
The invention provides a robot path planning method, a device and a robot with social attributes, wherein the position of a pedestrian relative to the robot is detected through laser data, then a semi-elliptical cost map is generated by taking the position of the pedestrian as a center and the position from the pedestrian to the robot as a main axis, and path search is carried out on the semi-elliptical cost map, so that an optimal path is formed for the robot.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
[ description of the drawings ]
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a schematic flowchart of a robot path planning method with social attributes provided in embodiment 1;
fig. 2 is a schematic diagram of a preset human leg template in the robot path planning method provided in embodiment 1;
fig. 3 is a schematic diagram of a pedestrian cost map in the robot path planning method provided in embodiment 1;
fig. 4 is a schematic structural diagram of a robot path planning apparatus having social attributes according to embodiment 2;
fig. 5 is a schematic circuit diagram of a controller provided in embodiment 3.
[ detailed description ] embodiments
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. 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 invention.
It should be noted that, if not conflicted, the various features of the embodiments of the invention may be combined with each other within the scope of protection of the invention. Additionally, while functional block divisions are performed in apparatus schematics, with logical sequences shown in flowcharts, in some cases, steps shown or described may be performed in sequences other than block divisions in apparatus or flowcharts. The terms "first", "second", "third", and the like used in the present invention do not limit data and execution order, but distinguish the same items or similar items having substantially the same function and action.
The robot of embodiments of the present invention may be configured in any suitable shape to perform a particular business function operation, for example, the robot of embodiments of the present invention may be a cleaning robot, a pet robot, a handling robot, a nursing robot, and the like. The cleaning robot includes, but is not limited to, a sweeping robot, a dust collecting robot, a mopping robot, a floor washing robot, and the like.
The robot generally includes a housing, a sensor unit, a drive wheel assembly, a memory assembly, and a controller. The housing may be substantially circular in shape, and in some embodiments, the housing may be substantially oval, triangular, D-shaped, cylindrical, or otherwise shaped.
The sensor unit is used for collecting some motion parameters of the robot and various data of the environment space. In some embodiments, the sensor unit comprises a lidar mounted above the housing at a mounting height above a top deck height of the housing, the lidar being for detecting an obstacle distance between the robot and an obstacle. In some embodiments, the sensor unit may also include an Inertial Measurement Unit (IMU), a gyroscope, a magnetic field meter, an accelerometer or speedometer, an optical camera, and so forth.
The driving wheel component is arranged on the shell and drives the robot to move on various spaces, and in some embodiments, the driving wheel component comprises a left driving wheel, a right driving wheel and an omnidirectional wheel, and the left driving wheel and the right driving wheel are respectively arranged on two opposite sides of the shell. The left and right drive wheels are configured to be at least partially extendable and retractable into the bottom of the housing. The omni-directional wheel is arranged at the position, close to the front, of the bottom of the shell and is a movable caster wheel which can rotate 360 degrees horizontally, so that the robot can flexibly steer. The left driving wheel, the right driving wheel and the omnidirectional wheel are arranged to form a triangle, so that the walking stability of the robot is improved. Of course, in some embodiments, the driving wheel component may also adopt other structures, for example, the omni wheel may be omitted, and only the left driving wheel and the right driving wheel may be left to drive the robot to normally walk.
In some embodiments, the robot is further configured with a cleaning component and/or a storage component that is mounted within the receiving slot to accomplish cleaning tasks, delivery tasks, and the like.
The controller is respectively and electrically connected with the left driving wheel, the right driving wheel, the omnidirectional wheel and the laser radar. The controller is used as a control core of the robot and is used for controlling the robot to walk, retreat and some business logic processing.
In some embodiments, the controller may be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a single chip, ar (acorn riscmachine) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination of these components. Also, the controller may be any conventional processor, controller, microcontroller, or state machine. A controller may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP, and/or any other such configuration.
In some embodiments, during the movement of the robot, the controller uses SLAM (simultaneous localization and mapping) technology to construct a map and a position according to the environmental data. The controller instructs the robot to completely traverse an environmental space through a full coverage path planning algorithm based on the established map and the position of the robot. For example, during the robot traversal, the sensor unit acquires an image of a traversal region, wherein the image of the traversal region may be an image of the entire traversal region or an image of a local traversal region in the entire traversal region. The controller generates a map from the image of the traversal area, the map having indicated an area that the robot needs to traverse and coordinate locations at which obstacles located in the traversal area are located. After each location or area traversed by the robot, the robot marks that the location or area has been traversed based on the map. In addition, as the obstacle is marked in a coordinate mode in the map, when the robot passes, the distance between the robot and the obstacle can be judged according to the coordinate point corresponding to the current position and the coordinate point related to the obstacle, and therefore the robot can pass around the obstacle. Similarly, after the position or the area is traversed and marked, when the next position of the robot moves to the position or the area, the robot makes a strategy of turning around or stopping traversing based on the map and the mark of the position or the area.
It will be appreciated that the controller may also identify traversed locations or areas, or identify obstacles, in a variety of ways to develop a control strategy that meets product needs.
Referring to fig. 1, a schematic flow chart of a robot path planning method with social attributes is provided for embodiment 1 of the present invention, as shown in fig. 1, the method includes the following steps:
and S1, acquiring laser point cloud data, and detecting the target position of the pedestrian in the robot sensing area relative to the robot laser coordinate system according to the laser point cloud data.
The sequence points or curves connecting the starting point position and the end point position are called paths, a strategy for forming the paths is called path planning, the path planning can be divided into global path planning based on prior complete information and local path planning based on sensor information according to the degree of grasp on environment information, wherein the global path means that the robot searches a path from the starting point position to the end point position in the environment with the obstacle according to one or more performance indexes, and the path can pass through the obstacle. The embodiment firstly judges whether pedestrians exist nearby and positions of the pedestrians relative to a laser coordinate system of the robot according to laser point cloud data of a laser radar on the robot, and specifically comprises the following steps:
s101, identifying at least one human leg in the laser point cloud data by adopting a template matching method, and generating a first position coordinate of the human leg under a robot laser coordinate system. This embodiment at first needs to discern the pedestrian, here adopt the mode of people leg matching to carry out pedestrian's discernment, preset people leg template earlier, single leg width a1 and two leg interval b when two legs are parallel, two leg width sum c and two leg width a2 when two legs are crisscross when two legs are drawn together and so on, as shown in fig. 2, then based on the thought of clustering, should predetermine people leg template and laser point cloud data and match, thereby obtain and predetermine a plurality of laser point clusters of people leg different of expression that people leg template matched with, and the information of each laser point cluster, include: the position of the center of mass, the number of the included points, the direction and the length of the main axis, the direction and the length of the secondary axis and the like, and the first position coordinate of the human leg can be calculated through the information of the point clusters. When the laser radar arranged on the robot is a two-dimensional laser radar, the method specifically comprises the following steps:
s1011, obtaining and filtering the laser point cloud data, thereby eliminating the influence of environmental noise and dynamic factors, removing objects as thin as table legs by adopting a local minimization algorithm, and then performing cluster matching on the processed laser point cloud data, namely a two-dimensional laser point cloud image and a preset human leg template to generate a plurality of laser point clusters for representing different human legs. Preferably, the adopted clustering method is a Mean-shift method, and the Mean-shift method can carry out self-adaptive clustering on the two-dimensional point set or improve the efficiency by using a fast algorithm based on block calculation.
And S1012, calculating the mass center of the laser spot cluster, and recording as the first position coordinate of the corresponding human leg of the laser spot cluster in the laser coordinate system of the robot.
If the laser radar arranged on the robot is a 3-dimensional laser radar, ground point cloud data in three-dimensional laser point cloud data acquired by the three-dimensional laser radar needs to be removed, pedestrians are identified from the remaining non-ground point cloud data, the non-ground point cloud data can be projected to the ground, the two-dimensional image is formed, and then the pedestrians are identified, or clustering segmentation is directly carried out on the three-dimensional non-ground point cloud data to identify the pedestrians, or a depth image is established according to the non-ground point cloud data, and the pedestrians are segmented and identified based on the depth image.
And then S102 is executed, the first position coordinates corresponding to all the human legs are obtained, the distance between any two human legs is calculated, and the two human legs closest to each other are related to the human leg of the same pedestrian.
And S103, taking the central positions of the two legs of the same pedestrian as the target position of the pedestrian relative to the laser coordinate system of the robot.
Then, by taking the target position as a center and the current position from the pedestrian to the robot as a long axis direction, constructing a semi-elliptical pedestrian cost map, specifically comprising the following steps:
s201, with a target position of the pedestrian relative to a laser coordinate system of the robot as a center, a current position of the pedestrian to the robot being a long axis direction, and a width of a single leg being set as a semi-long axis, and a distance between two legs when the two legs are parallel being set as a semi-short axis, constructing a semi-elliptical pedestrian cost map, as shown in fig. 3;
s202, assigning a value to the cost value of each grid in the pedestrian cost map by adopting a preset formula, wherein the preset formula is as follows:
wherein A represents a preset maximum cost value, (m)x,my) Representing each grid in the pedestrian cost map in an elliptical coordinate system xpHypGrid coordinates such that the more closely the grid is centered on the pedestrian, the greater the cost value.
The grid map is that the environment area is divided into a series of grids, the grids with different colors or pixels represent different landmark marks, the marks can express the rough situation of the ground in the environment space, and the robot can know the accessible area, the inaccessible area, the accessible area with more obstacles and the like through the grid map. Marking a cost value of the grid corresponding to each area in the pedestrian cost map, wherein the cost value is used for indicating the degree of the area corresponding to the grid interfering the passing of the robot, and the higher the cost value is, the larger the interference of the area corresponding to the grid to the passing of the robot is represented; the lower the cost value, the less the traffic disturbance of the area corresponding to the representative grid to the robot. The embodiment adopts the above formula to assign values, and the closer to the central grid of the pedestrian, the larger the cost value is, so that the robot bypasses the pedestrian at a proper distance, and the walking comfort level of the pedestrian is improved.
And then executing S3, performing path search on the pedestrian cost map by adopting a preset algorithm, and establishing an optimal driving path for the robot, wherein the optimal driving path can be generated by adopting Dijkstra, A, D or D lite algorithm. The preferred embodiment uses the a-algorithm, which is a typical heuristic search algorithm in artificial intelligence, and constrains the search process by selecting an appropriate estimation function. The expression form of the A-algorithm estimation function is as follows:
F(n)＝G(n)+H(n)；
where f (n) is the minimum cost estimate from the start node to the target node through node n, g (n) is the actual cost of the searched path from the start node to node n, and h (n) is the cost estimate from node n to the target node. When the robot plans a path from the starting node to the target node, a path corresponding to the minimum cost estimation value from the starting node to the target node can be obtained by calculating the cost value of the area from the starting node to the target node and then according to the A-x algorithm and the cost value obtained by calculation, wherein the path is the optimal path from the starting node to the target node, so that the passing performance of the robot is improved, and the user experience is improved. The method specifically comprises the following steps of,
and S301, searching a minimum cost estimation value from the current position to the target position according to the A-x algorithm and the cost value of each grid in the pedestrian cost map. The robot can move from the current position to the target position through a plurality of paths, but the cost values of all the paths are different, when the robot plans the paths, the cost value of the grid corresponding to each region is firstly determined, the sum of the cost values of the regions occupied by all the paths is calculated, then the minimum cost estimation value from the current position to the target position is determined according to an A-x algorithm, and all the regions of the minimum cost estimation value are determined.
S302, determining that the search path of the minimum cost estimation value is the optimal driving path from the current position to the target position.
The embodiment provides a robot path planning method with social attributes, the position of a pedestrian relative to a robot is detected through laser data, then a semi-elliptical cost map is generated by taking the position of the pedestrian as the center and taking the position of the pedestrian to the robot as the main axis, and then path search is carried out on the semi-elliptical cost map, so that an optimal path is generated for the robot.
It should be noted that, in the foregoing embodiments, a certain order does not necessarily exist between the foregoing steps, and it can be understood by those skilled in the art from the description of the embodiments of the present invention that, in different embodiments, the foregoing steps may have different execution orders, that is, may be executed in parallel, may also be executed in an exchange manner, and the like.
As another aspect of the embodiments of the present invention, an embodiment of the present invention further provides a robot path planning apparatus having social attributes. The robot path planning device may be a software module, where the software module includes a plurality of instructions, and the instructions are stored in a memory in the electronic tilt, and the processor may access the memory and call the instructions to execute the instructions, so as to complete the robot path planning method with social attributes set forth in the above embodiments.
In some embodiments, the robot path planning apparatus may also be built by hardware devices, for example, the robot path planning apparatus may be built by one or more than two chips, and each chip may work in coordination with each other to complete the robot path planning method with social attributes described in the above embodiments. For another example, the robot path planning apparatus may also be constructed by various logic devices, such as a general processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a single chip, an arm (acorn RISC machine) or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or any combination of these components.
Fig. 4 is a schematic structural diagram of a robot path planning apparatus having social attributes according to embodiment 2 of the present invention, which includes a position acquisition module 100, a mapping module 200, and a path search module 300,
the position acquisition module 100 is configured to acquire laser point cloud data and detect a target position of a pedestrian in a robot sensing area relative to a robot laser coordinate system according to the laser point cloud data;
the map construction module 200 is configured to construct a semi-elliptical pedestrian cost map by using the target position as a center and the current position of the pedestrian to the robot as a long axis direction, wherein a cost value of a grid closer to the center of the pedestrian in the pedestrian cost map is larger;
the path searching module 300 is configured to perform path searching on the pedestrian cost map by using a preset algorithm, and establish an optimal driving path for the robot.
In a preferred embodiment, the position obtaining module 100 specifically includes:
a human leg position identification unit 101, configured to identify at least one human leg in the laser point cloud data by using a template matching method, and generate a first position coordinate of the human leg in a robot laser coordinate system;
the association unit 102 is configured to acquire first position coordinates corresponding to all the human legs, calculate a distance between any two human legs, and associate two human legs closest to each other as the human leg of the same pedestrian;
and the target position acquisition unit 103 is used for taking the central positions of two human legs of the same pedestrian as the target positions of the pedestrian relative to the robot laser coordinate system.
In a preferred embodiment, the human leg position identification unit 101 specifically includes:
the clustering unit 1011 is used for acquiring laser point cloud data, and performing clustering matching on the laser point cloud data and a preset human leg template to generate a plurality of laser point clusters used for representing different human legs;
a first calculating unit 1012, configured to calculate a centroid of the laser spot cluster, and record as a first position coordinate of a leg corresponding to the laser spot cluster in a robot laser coordinate system;
the preset human leg template comprises the width of a single leg and the distance between the two legs when the two legs are parallel, the sum of the widths of the two legs when the two legs are closed and the width of the single leg when the two legs are staggered.
In a preferred embodiment, the map building module 200 specifically includes:
a construction unit 201, configured to construct a semielliptical pedestrian cost map by taking a target position of the pedestrian relative to a robot laser coordinate system as a center, a current position of the pedestrian to the robot is a long axis direction, a width of a single leg is set as a semilong axis, and a distance between two parallel legs is set as a semishort axis;
an assigning unit 202, configured to assign a value to the cost value of each grid in the pedestrian cost map by using a preset formula, where the preset formula is:
wherein A represents a preset maximum cost value, (m)x,my) For pedestrian cost map each grid in elliptical coordinate system xpHypGrid coordinates of the bottom.
In a preferred embodiment, the path searching module 300 specifically includes:
a second calculating unit 301, configured to search for a minimum cost estimation value from the current position to the target position according to an a-x algorithm and a cost value of each grid in the pedestrian cost map;
a path generating unit 302, configured to determine that the search path of the minimum cost estimation value is an optimal driving path from the current location to the target location.
In some embodiments, the robot path planning apparatus further includes a control module 400 for controlling the robot to move from the current position to the target position according to the optimal travel path.
It should be noted that the robot path planning apparatus can execute the robot path planning method provided by the embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method. For technical details that are not described in detail in the embodiment of the robot path planning apparatus, reference may be made to the robot path planning method provided in the embodiment of the present invention.
Fig. 5 is a schematic circuit structure diagram of a controller according to embodiment 3 of the present invention. As shown in fig. 5, the controller includes one or more processors 61 and a memory 62. In fig. 5, one processor 61 is taken as an example.
The processor 61 and the memory 62 may be connected by a bus or other means, such as the bus connection in fig. 5.
The memory 62 is a non-volatile computer-readable storage medium, and can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as program instructions/modules corresponding to the robot path planning method in the embodiment of the present invention. The processor 61 executes various functional applications and data processing of the robot path planning apparatus by running the nonvolatile software program, instructions and modules stored in the memory 62, that is, the functions of the robot path planning method provided by the above method embodiment and the modules or units of the above apparatus embodiment are realized.
The memory 62 may include high speed random access memory and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some embodiments, the memory 62 may optionally include memory located remotely from the processor 61, and these remote memories may be connected to the processor 61 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The program instructions/modules are stored in the memory 62 and, when executed by the one or more processors 61, perform the robot path planning method in any of the method embodiments described above.
Embodiments of the present invention also provide a non-volatile computer storage medium storing computer-executable instructions, which are executed by one or more processors, such as one of the processors 61 in fig. 5, so that the one or more processors can execute the robot path planning method in any of the above method embodiments.
Embodiments of the present invention further provide a computer program product, which includes a computer program stored on a non-volatile computer-readable storage medium, where the computer program includes program instructions, and when the program instructions are executed by an electronic device, the electronic device is caused to execute any one of the robot path planning methods.
The above-described embodiments of the apparatus or device are merely illustrative, wherein the unit modules described as separate parts may or may not be physically separate, and the parts displayed as module units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network module units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a general hardware platform, and certainly can also be implemented by hardware. Based on such understanding, the above technical solutions substantially or contributing to the related art may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; within the idea of the invention, also technical features in the above embodiments or in different embodiments may be combined, steps may be implemented in any order, and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A robot path planning method with social attributes is characterized by comprising the following steps:
s1, acquiring laser point cloud data, and detecting the target position of the pedestrian in the robot sensing area relative to the robot laser coordinate system according to the laser point cloud data;
s2, constructing a semi-elliptical pedestrian cost map by taking the target position as a center and the current position of the pedestrian to the robot as a long axis direction, wherein the cost value of a grid which is closer to the center of the pedestrian is larger in the pedestrian cost map;
and S3, performing path search on the pedestrian cost map by adopting a preset algorithm, and establishing an optimal driving path for the robot.
2. The method for planning robot path with social attributes according to claim 1, wherein the step of detecting the target position of the pedestrian in the robot sensing area relative to the robot laser coordinate system according to the laser point cloud data includes the following steps:
s101, identifying at least one human leg in the laser point cloud data by adopting a template matching method, and generating a first position coordinate of the human leg under a robot laser coordinate system;
s102, acquiring first position coordinates corresponding to all the human legs respectively, calculating the distance between any two human legs, and associating the two human legs closest to each other as the human legs of the same pedestrian;
and S103, taking the central positions of the two legs of the same pedestrian as the target position of the pedestrian relative to the laser coordinate system of the robot.
3. The method for planning a robot path with social attributes according to claim 2, wherein the step of identifying at least one human leg in the laser point cloud data by using a template matching method and generating a first position coordinate of the human leg in a robot laser coordinate system comprises the following steps:
s1011, acquiring laser point cloud data, and performing clustering matching on the laser point cloud data and a preset human leg template to generate a plurality of laser point clusters for representing different human legs;
s1012, calculating the mass center of the laser spot cluster, and recording as a first position coordinate of a human leg corresponding to the laser spot cluster in a robot laser coordinate system;
the preset human leg template comprises the width of a single leg and the distance between the two legs when the two legs are parallel, the sum of the widths of the two legs when the two legs are closed and the width of the single leg when the two legs are staggered.
4. The method for planning a robot path according to claim 3, wherein the step of constructing a semi-elliptical pedestrian cost map by using the target position as a center and the current position of the pedestrian to the robot as a major axis direction specifically comprises the following steps:
s201, with the target position of the pedestrian relative to a robot laser coordinate system as a center, the current position of the pedestrian to the robot as a long axis direction, the width of a single leg as a semi-long axis, and the distance between two legs when the two legs are parallel as a semi-short axis, constructing a semi-elliptical pedestrian cost map;
s202, assigning a value to the cost value of each grid in the pedestrian cost map by adopting a preset formula, wherein the preset formula is as follows:
wherein A represents a preset maximum cost value, (m)x,my) Representing each grid in the pedestrian cost map in an elliptical coordinate system xpHypGrid coordinates of the bottom.
5. The method for planning the robot path with the social attribute according to any one of claims 1 to 4, wherein the method for performing the path search on the robot cost map by using the preset algorithm to establish the optimal driving path for the robot specifically comprises the following steps:
s301, searching a minimum cost estimation value from the current position to the target position according to an A-x algorithm and the cost value of each grid in the pedestrian cost map;
s302, determining that the search path of the minimum cost estimation value is the optimal driving path from the current position to the target position.
6. A robot path planning device with social attributes is characterized by comprising a position acquisition module, a map construction module and a path search module,
the position acquisition module is used for acquiring laser point cloud data and detecting the target position of a pedestrian in a robot sensing area relative to a robot laser coordinate system according to the laser point cloud data;
the map building module is used for building a semi-elliptical pedestrian cost map by taking the target position as a center and the current position of the pedestrian to the robot as a long axis direction, wherein the cost value of a grid which is closer to the center of the pedestrian is larger in the pedestrian cost map;
the path searching module is used for searching paths on the pedestrian cost map by adopting a preset algorithm and establishing an optimal driving path for the robot.
7. The device for planning a robot path with social attributes according to claim 6, wherein the location acquiring module specifically comprises:
the human leg position identification unit is used for identifying at least one human leg in the laser point cloud data by adopting a template matching method and generating a first position coordinate of the human leg under a robot laser coordinate system;
the association unit is used for acquiring first position coordinates corresponding to all the human legs respectively, calculating the distance between any two human legs and associating the two human legs closest to the distance with the human legs of the same pedestrian;
and the target position acquisition unit is used for taking the central positions of two human legs of the same pedestrian as the target positions of the pedestrian relative to the laser coordinate system of the robot.
8. The device for planning a robot path with social attributes according to claim 7, wherein the map building module specifically comprises:
the construction unit is used for constructing a semi-elliptical pedestrian cost map by taking the target position of the pedestrian relative to a robot laser coordinate system as a center, taking the current position of the pedestrian to the robot as a long axis direction, setting the width of a single leg as a semi-long axis, and setting the distance between two parallel legs as a semi-short axis;
the assignment unit is used for assigning the cost value of each grid in the pedestrian cost map by adopting a preset formula, wherein the preset formula is as follows:
wherein A represents a preset maximum cost value, (m)x,my) For pedestrian cost map each grid in elliptical coordinate system xpHypGrid coordinates of the bottom.
9. A robot comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the steps of the method according to any of the claims 1 to 5 are implemented when the computer program is executed by the processor.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 5.
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