Obstacle avoidance method, path planning method, electronic equipment, robot and storage medium

Abstract

The invention discloses an obstacle avoidance method, a path planning method, electronic equipment, a robot and a storage medium. The avoidance method comprises the following steps: determining a first grid layer in real time according to a navigation map at a plurality of different moments; clustering a plurality of adjacent first pixels in the first grid layer into a target grid to obtain a second grid layer; determining current state information of the dynamic obstacle according to the target grid; and predicting the state information of the dynamic obstacle at the next moment according to the plurality of pieces of current state information corresponding to different moments. The path planning method comprises the following steps: determining a first grid layer in real time according to a navigation map at a plurality of different moments; clustering a plurality of adjacent first pixels in the first grid layer into a target grid to obtain a second grid layer; determining current state information of the dynamic obstacle according to the target grid; predicting the state information of the dynamic obstacle at the next moment according to the plurality of pieces of current state information corresponding to different moments; and determining a moving path according to the current state information and the state information at the next moment.

Description

Technical Field

[0001] This application relates to the field of robot technology, and in particular to an obstacle avoidance method, a path planning method, an electronic device, a robot, and a storage medium. Background Art

[0002] A mobile robot is a robot that can move autonomously and can freely move in an environment and perform various tasks such as carrying, distribution, and guidance. The mobile robot has strong mobility and can work in different environments, such as homes, offices, factories, hospitals, etc.

[0003] In related technologies, a mobile robot usually uses a short-range sensor such as a millimeter-wave radar to detect nearby obstacles. However, the detection distance of the sensor is relatively short. When the moving speed of the mobile robot or a dynamic obstacle is relatively fast, there may be a risk that the mobile robot fails to make a timely decision and collides with the dynamic obstacle. Summary of the Invention

[0004] The obstacle avoidance method, path planning method, electronic device, robot, and storage medium provided by the embodiments of this application can solve or partially solve the above deficiencies in the prior art or other deficiencies in the prior art.

[0005] According to an embodiment of the first aspect of this application, a robot obstacle avoidance method includes:

[0006] At multiple different times, a first grid layer is determined in real time according to a navigation map, and the first grid layer includes a plurality of first pixels representing dynamic obstacles;

[0007] A plurality of adjacent first pixels in the first grid layer are clustered into target grids to obtain a second grid layer; and

[0008] The current state information of the dynamic obstacle is determined according to the target grid.

[0009] According to an embodiment of this application, the navigation map includes a static layer and a first dynamic layer. The static layer includes a plurality of second pixels representing static obstacles, and the first dynamic layer includes a plurality of the first pixels and a plurality of the second pixels.

[0010] According to an embodiment of this application, determining a first grid layer in real time according to a navigation map at multiple different times includes:

[0011] Obtaining the navigation map at the current moment; and

[0012] Comparing the static layer and the first dynamic layer to generate the first grid layer.

[0013] According to an embodiment of the present application, the target grid is the grid with the smallest area among all the grids that can cover multiple adjacent first pixels.

[0014] According to an embodiment of the present application, the current state information includes the movement center position and size information of the dynamic obstacle.

[0015] According to an embodiment of the present application, the size information includes the maximum distance from the movement center position to the contour of the target grid.

[0016] A robot path planning method provided according to the second aspect implementation manner of the present application includes:

[0017] Determine a first grid layer in real time at multiple different times according to a navigation map, where the first grid layer includes multiple first pixels representing dynamic obstacles;

[0018] Cluster multiple adjacent first pixels in the first grid layer into target grids to obtain a second grid layer;

[0019] Determine the current state information of the dynamic obstacle according to the target grid;

[0020] Predict the next moment state information of the dynamic obstacle according to the current state information corresponding to multiple different times; and

[0021] Determine the movement path of the robot according to the current state information and the next moment state information.

[0022] According to an embodiment of the present application, the navigation map includes a static layer and a first dynamic layer, the static layer includes multiple second pixels representing static obstacles, and the first dynamic layer includes multiple first pixels and multiple second pixels.

[0023] According to an embodiment of the present application, determining a first grid layer in real time at multiple different times according to a navigation map includes:

[0024] Obtain the navigation map at the current moment; and

[0025] Compare the static layer and the first dynamic layer to generate the first grid layer.

[0026] According to an embodiment of the present application, determining the movement path of the robot according to the current state information and the next moment state information includes:

[0027] Construct a second dynamic layer according to the next moment state information;

[0028] Generate an expansion layer according to the static layer, the second dynamic layer and the second grid layer;

[0029] Generate a fusion map based on the static layer, the second dynamic layer, the second grid layer, and the dilation layer; and

[0030] Determine the movement path based on the fusion map.

[0031] According to an embodiment of the present application, the target grid is the grid with the smallest area among all the grids that can cover multiple adjacent first pixels.

[0032] According to an embodiment of the present application, the current state information all includes the movement center position and size information of the dynamic obstacle.

[0033] According to an embodiment of the present application, predicting the state information of the dynamic obstacle at the next moment based on the current state information corresponding to multiple different moments includes:

[0034] Determine the moving speed and moving direction of the dynamic obstacle according to multiple movement center positions and the moments corresponding to the movement center positions;

[0035] Determine the predicted radius of the dynamic obstacle according to multiple size information; and

[0036] Determine the state information at the next moment according to the movement center position, the moving speed, the moving direction, and the predicted radius at the current moment.

[0037] According to an embodiment of the present application, the size information includes the maximum distance from the movement center position to the contour of the target grid.

[0038] According to an embodiment of the present application, the predicted radius is the maximum value among multiple maximum distances.

[0039] An electronic device according to an embodiment of the third aspect of the present application includes:

[0040] At least one processor; and,

[0041] A memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the robot obstacle avoidance method according to the first aspect of the present application and / or the robot path planning method according to the second aspect of the present application.

[0042] A robot according to an embodiment of the fourth aspect of the present application includes a robot body and the electronic device according to the second aspect of the present application, and the electronic device is disposed on the robot body.

[0043] A computer-readable storage medium provided according to the fifth aspect of the present application stores a computer program. When the computer program is executed by a processor, it implements the robot obstacle avoidance method described in the first aspect of the present application and / or the robot path planning method described in the second aspect of the present application.

[0044] In the robot obstacle avoidance method provided by the embodiments of the present application, by clustering multiple first pixels representing dynamic obstacles into target grids, the current state information of the dynamic obstacles can be determined with the help of the target grids. Furthermore, by using the current state information corresponding to different moments, the state information of the dynamic obstacles at the next moment can be predicted. Thus, it can be avoided that the robot collides with the dynamic obstacles due to untimely decision-making.

[0045] In the robot path planning method provided by the embodiments of the present application, by clustering multiple first pixels representing dynamic obstacles into target grids, the current state information of the dynamic obstacles can be determined with the help of the target grids. Furthermore, by using the current state information corresponding to different moments, the state information of the dynamic obstacles at the next moment can be predicted. Thus, a moving path can be planned in advance for the robot according to the state information at the next moment and the current state information, avoiding the robot from colliding with the dynamic obstacles due to untimely decision-making, thereby improving the intelligence and safety of the robot.

[0046] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it used to limit the scope of the present disclosure. Other features of the present disclosure will become easily understandable through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] By reading the detailed description of the non-limiting embodiments with reference to the following drawings, other features, objects, and advantages of the present application will become more apparent. The drawings are used to better understand the solution and do not constitute a limitation to the present application.

[0048] In the drawings:

[0049] Figure 1 is a schematic flowchart of the robot obstacle avoidance method according to an embodiment of the present application;

[0050] Figure 2 is a schematic flowchart of the robot path planning method according to an embodiment of the present application;

[0051] Figure 3 is a schematic diagram of the first grid layer according to an embodiment of the present application;

[0052] Figure 4 is a schematic diagram of the second grid layer according to an embodiment of the present application;

[0053] Figure 5And Figure 6 is a schematic diagram for determining current status information according to an embodiment of the present application;

[0054] Figure 7 is a schematic diagram of a second dynamic layer according to an embodiment of the present application;

[0055] Figure 8 is a schematic diagram of a movement path according to an embodiment of the present application;

[0056] Figure 9 is a schematic diagram of a movement path in the prior art; and

[0057] Figure 10 is a block diagram of an electronic device for implementing the robot obstacle avoidance method according to an embodiment of the present application.

[0058] Reference numerals:

[0059] 100, first grid layer; 101, first pixel; 110, second grid layer;

[0060] 111, target grid; 120, second dynamic layer; 130, fusion map;

[0061] 131, dynamic obstacle; 132, static obstacle; 133, prediction area;

[0062] 200, electronic device; 201, calculation unit; 202, memory (ROM);

[0063] 203, memory (RAM); 204, bus; 205, I / O interface;

[0064] 206, input unit; 207, output unit; 208, storage unit;

[0065] 209, communication unit. Detailed implementation manners

[0066] The following describes exemplary embodiments of the present application with reference to the accompanying drawings. Various details of the embodiments of the present application are included to facilitate understanding, and they should be considered merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Similarly, descriptions of well-known functions and structures are omitted below for clarity and conciseness.

[0067] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present application will be described in detail below with reference to the drawings and in conjunction with the embodiments.

[0068] As Figure 1 、Figures 3 to 7 As shown in the figure, an embodiment of the present application provides a robot obstacle avoidance method 1000, and the robot obstacle avoidance method includes the following steps:

[0069] S100. At multiple different times, determine the first grid layer 100 in real time according to the navigation map, and the first grid layer 100 includes a plurality of first pixels 101 representing dynamic obstacles 131;

[0070] S110. Cluster a plurality of adjacent first pixels 101 in the first grid layer 100 into target grids 111 to obtain a second grid layer 110;

[0071] S120. Determine the current state information of the dynamic obstacle 131 according to the target grid 111;

[0072] S130. Predict the next moment state information of the dynamic obstacle 131 according to the current state information corresponding to multiple different times.

[0073] In the embodiment of the present application, by clustering a plurality of first pixels 101 representing dynamic obstacles 131 into target grids 111, the current state information of the dynamic obstacle 131 can be determined with the help of the target grids 111, and then the next moment state information of the dynamic obstacle 131 can be predicted by using the current state information corresponding to multiple different times. Thus, it is possible to prevent the robot from colliding with the dynamic obstacle 131 due to untimely decision-making.

[0074] In addition, as Figures 2 to 8 shown in the figure, an embodiment of the present application further provides a robot path planning method 2000, and the path planning method includes the following steps:

[0075] S200. At multiple different times, determine the first grid layer 100 in real time according to the navigation map, and the first grid layer 100 includes a plurality of first pixels 101 representing dynamic obstacles 131;

[0076] S210. Cluster a plurality of adjacent first pixels 101 in the first grid layer 100 into target grids 111 to obtain a second grid layer 110;

[0077] S220. Determine the current state information of the dynamic obstacle 131 according to the target grid 111;

[0078] S230. Predict the next moment state information of the dynamic obstacle 131 according to the current state information corresponding to multiple different times;

[0079] S240. Determine the moving path of the robot according to the current state information and the next moment state information.

[0080] In the embodiments of the present application, by clustering a plurality of first pixels 101 representing the dynamic obstacle 131 into the target grid 111, the current state information of the dynamic obstacle 131 can be determined with the aid of the target grid 111. Furthermore, by using the current state information corresponding to multiple different moments, the state information of the dynamic obstacle 131 at the next moment can be predicted. Thus, a movement path can be planned in advance for the robot according to the state information at the next moment and the current state information, avoiding the robot from colliding with the dynamic obstacle 131 due to untimely decision-making, thereby improving the intelligence and safety of the robot.

[0081] In some embodiments, the navigation map includes a static layer (not shown) and a first dynamic layer (not shown). The static layer includes a plurality of second pixels representing the static obstacle 132, and the first dynamic layer includes a plurality of first pixels 101 and a plurality of second pixels. In other words, the first dynamic layer includes both the first pixels 101 representing the dynamic obstacle 131 and the second pixels representing the static obstacle 132, while the static layer only includes the second pixels representing the static obstacle 132. Among them, the dynamic obstacle 131 generally refers to an obstacle that can move, such as a vehicle, a pedestrian, an animal, etc. The static obstacle 132 generally refers to an obstacle that remains stationary within a certain time period, such as a wall, a table, a vehicle parked by the roadside, etc.

[0082] In some embodiments, the step of determining the first grid layer 100, i.e., step S100 or step S200, may include: obtaining the navigation map at the current moment; comparing the static layer and the first dynamic layer to generate the first grid layer 100. Since the first dynamic layer includes both the first pixels 101 representing the dynamic obstacle 131 and the second pixels representing the static obstacle 132, while the static layer only includes the second pixels representing the static obstacle 132, by comparing the first dynamic layer and the static layer, the second pixels representing the static obstacle 132, which are the same pixels as those in the static layer in the first dynamic layer, can be removed to obtain the first grid layer 100. As Figure 3 shown, the first grid layer 100 only includes the first pixels 101 representing the dynamic obstacle 131.

[0083] In some embodiments, the step of obtaining the second grid layer 110, i.e., step S110 or step S210, may include: using a clustering algorithm to cluster a plurality of adjacent first pixels 101 in the first grid layer 100 into the target grid 111, and the target grid 111 can cover the above-mentioned adjacent plurality of first pixels 101. Among them, the shape of the target grid 111 can be a rectangle, a polygon, a circle or an ellipse, and the present application does not make any limitation thereto. Further, the target grid 111 can be the grid with the smallest area among all the grids that can cover the adjacent plurality of first pixels 101.

[0084] In some embodiments, the step of determining the current state information of the dynamic obstacle 131, i.e., step S120 or step S220, may include: determining the movement center position of the dynamic obstacle 131 according to the target grid 111; determining the size information of the dynamic obstacle 131 according to the target grid 111 and the movement center position. Wherein, the size information may include the maximum distance from the movement center position to the contour of the target grid 111. For example, as Figure 5 shown, the movement center position of the dynamic obstacle 131 can be determined as point P1 according to the target grid 111 first, and then the maximum distance R1 from point P1 to the contour of the target grid 111 can be determined by combining the contour shape of the target grid 111 and the movement center position P1.

[0085] In some embodiments, the step of predicting the state information of the dynamic obstacle 131 at the next moment, i.e., step S130 or step S230, may include: determining the moving speed and moving direction of the dynamic obstacle 131 according to the movement center positions corresponding to multiple different moments and the moments corresponding to the movement center positions; determining the predicted radius of the dynamic obstacle 131 according to multiple maximum distances; wherein, the predicted radius may be the maximum value of the multiple maximum distances; determining the state information at the next moment according to the movement center position, moving speed, moving direction and predicted radius at the current moment. Wherein, the state information at the next moment may include the movement center position of the dynamic obstacle 131 at the next moment and the size information at the next moment.

[0086] In some embodiments, the step of determining the moving path of the robot, i.e., step S240, may include: constructing a second dynamic layer 120 according to the state information at the next moment; generating an inflated layer (not shown) according to the static layer, the second dynamic layer 120 and the second grid layer 110; generating a fusion map 130 according to the static layer, the second dynamic layer 120, the second grid layer 110 and the inflated layer; determining the moving path according to the fusion map 130.

[0087] Taking the navigation map including a static layer and a first dynamic layer as an example, the robot path planning method of the embodiments of the present application will be illustrated by examples.

[0088] Assume that the current moment is T1. Then, the navigation map can be obtained at T1, and the static layer of the navigation map is compared with the first dynamic layer. Since the first dynamic layer includes both the first pixels 101 representing the dynamic obstacle 131 and the second pixels representing the static obstacle 132, while the static layer only includes the second pixels representing the static obstacle 132, by comparing the first dynamic layer and the static layer, the second pixels in the first dynamic layer that are the same as those in the static layer, i.e., the second pixels representing the static obstacle 132, are removed, and the first grid layer 100 at T1 can be obtained. The clustering algorithm is used to cluster multiple adjacent first pixels 101 in the first grid layer 100 at T1 into the target grid 111; as Figure 5 shown, according to the target grid 111, the movement center position of the dynamic obstacle 131 at T1 is determined as point P1; combining the contour shape of the target grid 111 at T1 and the movement center position P1, the maximum distance from point P1 to the contour of the target grid 111 can be determined as R1. Since the navigation map is usually updated in real time, at T2, the navigation map needs to be obtained again, and the static layer of the navigation map is compared with the first dynamic layer again. By comparing the static layer and the first dynamic layer of the navigation map, the first grid layer 100 at T2 can be generated. The clustering algorithm is used to cluster multiple adjacent first pixels 101 in the first grid layer 100 into the target grid 111; as Figure 6 shown, according to the target grid 111, the movement center position of the dynamic obstacle 131 at T2 is determined as point P2; combining the contour shape of the target grid 111 at T2 and the movement center position P2, the maximum distance from point P2 to the contour of the target grid 111 can be determined as R2. According to the movement center position P1 of the dynamic obstacle 131 at T1, the movement center position P2 of the dynamic obstacle 131 at T2, and the time difference between T1 and T2, the moving speed and moving direction of the dynamic obstacle 131 can be determined. For example, T2 - T1 = ΔT, v = (P2 - P1) / ΔT, where v is the moving speed of the dynamic obstacle 131. In addition, in order to improve the response speed, ΔT = 1 / n, where n is the update frequency of the navigation map. Compare the relative magnitudes of the maximum distance R1 at T1 and the maximum distance R2 at T2, and take the maximum value Rmax(R1, R2) of R1 and R2 as the predicted radius Rx of the dynamic obstacle 131. Take the previously determined predicted radius Rx as the size information of the dynamic obstacle 131. According to the movement center position P2 of the dynamic obstacle 131 at the current moment, i.e., T2, and the moving speed, the distance between the movement center position P2 of the dynamic obstacle 131 at the current moment, i.e., T2, and its movement center position at the next moment, i.e., T3, can be determined. For example, ΔL = P2 + v*ΔT, where ΔL is the distance between P3 and P2. As Figure 7As shown, based on this distance and the moving direction of the previously determined dynamic obstacle 131, the movement center position P3 of the dynamic obstacle 131 at the next moment, i.e., at time T3, can be determined. Thus, through the above steps, the state information of the dynamic obstacle 131 at the next moment can be determined, and based on this state information at the next moment, the second dynamic layer 120 can be constructed. Thus, as Figure 8 shown, based on the static layer, the second dynamic layer 120, and the second grid layer 110, an inflated layer can be generated. Furthermore, by superimposing the static layer, the second dynamic layer 120, the second grid layer 110, and the inflated layer together, the fused map 130 can be obtained. The fused map 130 shows both the static obstacle 132 and the dynamic obstacle 131 at the current moment, and also shows the predicted area 133 that the dynamic obstacle 131 will reach at the next moment. Thus, the movement path determined based on the fused map 130 can not only avoid the dynamic obstacle 131 at the current moment, but also avoid the predicted area 133 that the dynamic obstacle 131 will reach at the next moment, preventing the robot from colliding with the dynamic obstacle 131 due to untimely decision-making, thereby improving the intelligence and safety of the robot.

[0089] See Figure 8 and Figure 9 shown, compared with the movement path L2 planned by the prior art, the movement path L1 planned by the robot avoidance method of the embodiment of the present application is more reasonable and can prevent the robot from colliding with the dynamic obstacle 131 due to untimely decision-making.

[0090] It should be noted that, in the embodiments of the present application, the state information of the dynamic obstacle 131 at the next moment can be determined not only by the above method, but also by two or more pieces of current state information. The present application does not limit this. For example, assume that the current moment is T4. The moving center position and the maximum distance of the moving obstacle determined at the current moment are P4 and R4 respectively. The moving center positions of the dynamic obstacle 131 determined at the previous moments T1, T2, and T3 are P1, P2, and P3 respectively, and the maximum distances determined at the same time are R1, R2, and R3 respectively. If the motion state of the dynamic obstacle 131 is a uniformly variable rectilinear motion, the acceleration of the dynamic obstacle 131 can be calculated according to the moving center positions of the dynamic obstacle 131 from the moment T1 to the moment T4, and then the moving distance and the moving direction of the dynamic obstacle 131 from the current moment T4 to the next moment T5 can be predicted according to the moving center position P4 at the moment T4 and the above acceleration. Thus, the moving center position P5 of the dynamic obstacle 131 at the next moment can be predicted. If the motion state of the dynamic obstacle 131 is a variable-speed motion, the velocity curve of the dynamic obstacle 131 can be fitted according to the moving center positions of the dynamic obstacle 131 from the moment T1 to the moment T4, and then the moving distance and the moving direction of the dynamic obstacle 131 from the current moment T4 to the next moment T5 can be calculated by integration.

[0091] Embodiments of the present application further provide an electronic device, which includes at least one processor and a memory communicatively connected to the at least one processor. The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the above-mentioned robot obstacle avoidance method and / or robot path planning method.

[0092] Furthermore, embodiments of the present application further provide a robot, which includes a robot body and the above-mentioned electronic device, and the electronic device is arranged on the robot body.

[0093] Embodiments of the present application further provide a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the above-mentioned robot obstacle avoidance method and / or robot path planning method are implemented.

[0094] Figure 10 A schematic block diagram of an exemplary controller, namely the electronic device 200, that can be used to implement the embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers. The electronic device can also represent various forms of mobile devices that can run computing programs. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0095] AsFigure 10 As shown in Figure 10 , the electronic device 200 includes a computing unit 201, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 202 or a computer program loaded from a storage unit 208 into a random access memory (RAM) 203. In the RAM 203, various programs and data required for the operation of the electronic device 200 can also be stored. The computing unit 201, the ROM 202, and the RAM 203 are connected to each other via a bus 204. An input / output (I / O) interface 205 is also connected to the bus 204.

[0096] Multiple components in the electronic device 200 are connected to the I / O interface 205, including: an input unit 206, such as a touch screen, etc.; an output unit 207, such as various types of displays, speakers, etc.; a storage unit 208, such as a disk, etc.; and a communication unit 209, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 209 allows the electronic device 200 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0097] The computing unit 201 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 201 include but are not limited to a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 201 executes the various methods and processes described above, such as the robot obstacle avoidance method. For example, in some embodiments, the robot obstacle avoidance method can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as the storage unit 208. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 200 via the ROM 202 and / or the communication unit 209. When the computer program is loaded into the RAM 203 and executed by the computing unit 201, one or more steps of the robot obstacle avoidance method described above can be executed. Alternatively, in other embodiments, the computing unit 201 can be configured to execute the robot obstacle avoidance method in any other appropriate way (e.g., by means of firmware).

[0098] The various embodiments of the systems and techniques described above in this specification can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on a chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which may be a special-purpose or general-purpose programmable processor that receives data and instructions from, and transmits data and instructions to, a storage system, at least one input device, and at least one output device.

[0099] The program code for implementing the methods of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that the program codes, when executed by the processor or controller, cause the functions / operations specified in the flowchart illustrations and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on the remote machine or server.

[0100] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0101] It should be understood that various forms of the flowcharts shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in this disclosure can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and no limitations are imposed herein.

[0102] The above specific embodiments do not limit the scope of protection of the present disclosure. Those skilled in the art should understand that various modifications, combinations, sub - combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present disclosure shall be included within the scope of protection of the present disclosure.

Claims

1. A robot obstacle avoidance method, characterized in that, Comprising: At multiple different times, determining a first grid layer in real time according to a navigation map, the first grid layer including a plurality of first pixels representing dynamic obstacles; Clustering a plurality of adjacent first pixels in the first grid layer into target grids to obtain a second grid layer; Determining current state information of the dynamic obstacle according to the target grid; And Predicting next moment state information of the dynamic obstacle according to the current state information corresponding to multiple different moments.

2. The robot obstacle avoidance method according to claim 1, wherein The navigation map includes a static layer and a first dynamic layer, the static layer including a plurality of second pixels representing static obstacles, and the first dynamic layer including a plurality of the first pixels and a plurality of the second pixels.

3. The robot obstacle avoidance method according to claim 2, wherein, Determining a first grid layer in real time according to a navigation map at multiple different times includes: Obtaining the navigation map at the current moment; and Comparing the static layer and the first dynamic layer to generate the first grid layer.

4. The robot obstacle avoidance method according to claim 1, wherein, The target grid is the grid with the smallest area among all grids that can cover a plurality of adjacent first pixels.

5. The robot obstacle avoidance method according to any one of claims 1 to 4, wherein, The current state information all includes the moving center position and size information of the dynamic obstacle.

6. The robot obstacle avoidance method according to claim 5, wherein, Predicting next moment state information of the dynamic obstacle according to the current state information corresponding to multiple different moments includes: Determining the moving speed and moving direction of the dynamic obstacle according to a plurality of the moving center positions and the moments corresponding to the moving center positions; Determining the predicted radius of the dynamic obstacle according to a plurality of the size information; and Determining the next moment state information according to the moving center position, the moving speed, the moving direction and the predicted radius at the current moment.

7. The robot obstacle avoidance method according to claim 6, wherein, The size information includes the maximum distance from the moving center position to the contour of the target grid.

8. The robot obstacle avoidance method according to claim 7, wherein, The predicted radius is the maximum value among a plurality of the maximum distances.

9. A robot path planning method, characterized in that, Comprising: At multiple different times, determining a first grid layer in real time according to a navigation map, the first grid layer including a plurality of first pixels representing dynamic obstacles; Clustering a plurality of adjacent first pixels in the first grid layer into target grids to obtain a second grid layer; Determining current state information of the dynamic obstacle according to the target grid; Predicting next moment state information of the dynamic obstacle according to the current state information corresponding to multiple different moments; And Determining a moving path of the robot according to the current state information and the next moment state information.

10. The robot path planning method according to claim 9, wherein, The navigation map includes a static layer and a first dynamic layer, the static layer including a plurality of second pixels representing static obstacles, and the first dynamic layer including a plurality of the first pixels and a plurality of the second pixels.

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