WO2020125500A1 - 机器人避障控制方法、装置及终端设备 - Google Patents

机器人避障控制方法、装置及终端设备 Download PDF

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Publication number
WO2020125500A1
WO2020125500A1 PCT/CN2019/124353 CN2019124353W WO2020125500A1 WO 2020125500 A1 WO2020125500 A1 WO 2020125500A1 CN 2019124353 W CN2019124353 W CN 2019124353W WO 2020125500 A1 WO2020125500 A1 WO 2020125500A1
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Prior art keywords
obstacle
robot
data
obstacle avoidance
infrared sensor
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PCT/CN2019/124353
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English (en)
French (fr)
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程俊
高向阳
郭海光
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中国科学院深圳先进技术研究院
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Publication of WO2020125500A1 publication Critical patent/WO2020125500A1/zh

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions

Definitions

  • the invention belongs to the technical field of robot control, and particularly relates to a robot obstacle avoidance control method, device, terminal device and computer-readable storage medium.
  • the functions of robots are becoming more and more abundant.
  • Existing robots can already realize functions such as voice interaction, visual detection, and obstacle avoidance control.
  • the obstacle avoidance control function of the robot can be realized by a vibration sensor or an ultrasonic sensor. Although this method has low production cost, the accuracy is low; the current obstacle avoidance control function of the robot can also be realized by visual technology, although this method is accurate Higher, but the production cost is higher. None of the above-mentioned methods can simultaneously have the advantages of low production cost and high accuracy, which leads to the widespread use of robots.
  • embodiments of the present invention provide a robot obstacle avoidance control method, device, terminal device, and computer-readable storage medium to solve the problem that the prior art cannot implement the robot obstacle avoidance control function on the basis of low production cost Of high accuracy.
  • a first aspect of an embodiment of the present invention provides a robot obstacle avoidance control method.
  • the robot includes at least: a first infrared sensor, a second infrared sensor, a camera, a current sensor, and an ultrasonic sensor.
  • the first infrared sensor is installed on On the front left of the robot, the second infrared sensor is installed on the front right of the robot and includes:
  • Obtain obstacle detection data which includes: first infrared sensor data, second infrared sensor data, camera data, current sensor data, ultrasonic sensor data;
  • a second aspect of an embodiment of the present invention provides a robot obstacle avoidance control device.
  • the robot includes at least: a first infrared sensor, a second infrared sensor, a camera, a current sensor, and an ultrasonic sensor.
  • the first infrared sensor is mounted on On the front left of the robot, the second infrared sensor is installed on the front right of the robot and includes:
  • the obstacle detection data acquisition unit is used to acquire obstacle detection data, and the obstacle detection data includes: first infrared sensor data, second infrared sensor data, camera data, current sensor data, and ultrasonic sensor data;
  • a robot obstacle avoidance decision acquisition unit configured to obtain a robot obstacle avoidance decision based on the obstacle detection data
  • the robot obstacle avoidance control unit is used to control the robot obstacle avoidance according to the robot obstacle avoidance decision.
  • a third aspect of the embodiments of the present invention provides a terminal device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program To realize the steps of the robot obstacle avoidance control method as described above.
  • a fourth aspect of the embodiments of the present invention provides a computer-readable storage medium that stores a computer program, characterized in that, when the computer program is executed by a processor, the robot can avoid obstacles Control method steps.
  • the beneficial effects of the embodiments of the present invention are: by acquiring obstacle detection data, the obstacle detection data includes: infrared sensor data, camera data, current sensor data, ultrasonic sensor data; according to the obstacle detection data Obtain the robot obstacle avoidance decision; control the robot obstacle avoidance according to the robot obstacle avoidance decision.
  • the robot obstacle avoidance control function is realized through infrared sensor data, camera data, current sensor data, and ultrasonic sensor data, which can improve the accuracy of the robot obstacle avoidance control function . That is, on the basis of low production cost, high accuracy of the robot obstacle avoidance control function can be realized.
  • FIG. 1 is a schematic flowchart of a robot obstacle avoidance control method according to an embodiment of the present invention
  • FIG. 2 is a schematic diagram of a pre-trained obstacle detection data fusion model provided by an embodiment of the present invention
  • FIG. 3 is a schematic structural diagram of a robot obstacle avoidance control device according to an embodiment of the present invention.
  • FIG. 4 is a schematic diagram of a terminal device provided by an embodiment of the present invention.
  • the term “if” may be interpreted as “when” or “once” or “in response to determination” or “in response to detection” depending on the context .
  • the phrase “if determined” or “if [described condition or event] is detected” may be interpreted in the context to mean “once determined” or “in response to a determination” or “once detected [described condition or event ]” or “In response to detection of [the described condition or event]”.
  • the terminals described in the embodiments of the present application include but are not limited to other portable devices such as mobile phones, laptop computers, or tablet computers with touch-sensitive surfaces (eg, touch screen displays and/or touch pads). It should also be understood that, in some embodiments, the device described above is not a portable communication device, but a desktop computer with a touch-sensitive surface (eg, touch screen display and/or touch pad).
  • the terminal including a display and a touch-sensitive surface is described.
  • the terminal may include one or more other physical user interface devices such as a physical keyboard, mouse, and/or joystick.
  • the terminal supports various applications, such as one or more of the following: drawing applications, presentation applications, word processing applications, website creation applications, disk burning applications, spreadsheet applications, game applications, phone applications Programs, video conferencing applications, email applications, instant messaging applications, exercise support applications, photo management applications, digital camera applications, digital camera applications, web browsing applications, digital music player applications and /Or digital video player application.
  • applications such as one or more of the following: drawing applications, presentation applications, word processing applications, website creation applications, disk burning applications, spreadsheet applications, game applications, phone applications Programs, video conferencing applications, email applications, instant messaging applications, exercise support applications, photo management applications, digital camera applications, digital camera applications, web browsing applications, digital music player applications and /Or digital video player application.
  • Various applications that can be executed on the terminal can use at least one common physical user interface device such as a touch-sensitive surface.
  • One or more functions of the touch-sensitive surface and corresponding information displayed on the terminal may be adjusted and/or changed between applications and/or within the corresponding applications.
  • the common physical architecture of the terminal eg, touch-sensitive surface
  • FIG. 1 shows a schematic flowchart of a first robot obstacle avoidance control method provided by an embodiment of the present application, and the details are as follows:
  • the robot includes at least a first infrared sensor, a second infrared sensor, a camera, a current sensor, and an ultrasonic sensor.
  • the first infrared sensor is installed at the front left of the robot, and the second infrared sensor is installed at the front right of the robot.
  • the first infrared sensor is installed at the front left of the robot
  • the second infrared sensor is installed at the front right of the robot.
  • they can be installed at the corresponding positions of the robot according to actual needs.
  • first infrared sensor second infrared sensor
  • camera current sensor
  • ultrasonic sensor are all low-cost devices.
  • Step S11 Obtain obstacle detection data, where the obstacle detection data includes: first infrared sensor data, second infrared sensor data, camera data, current sensor data, and ultrasonic sensor data.
  • the step S11 includes: if the first infrared sensor does not detect a target signal, then set the first infrared sensor data to preset first infrared sensor data, and the target signal is a substance other than the robot The radiated infrared signal; if the second infrared sensor does not detect the target signal, the second infrared sensor data is set as the preset second infrared sensor data.
  • the first infrared sensor is likely not to detect the target signal corresponding to the target; similarly, if the target is on the left side of the robot, the second infrared sensor is likely not to detect the target Corresponding to the target signal.
  • the presence of the obstacle can be analyzed according to the preset first infrared sensor data or/and the preset second infrared sensor data The situation improves the accuracy of robot obstacle avoidance control.
  • the robot further includes a motor.
  • the step S11 includes: if the current sensor does not detect the target current signal, set the current sensor data to the preset current sensor data, and the target current signal is a motor current signal greater than the preset motor current threshold.
  • the current sensor when the current sensor is not hindered by external forces, the motor current is equal to or less than the preset motor current threshold, the current sensor does not detect the target current signal, and the presence of the obstacle can be analyzed according to the preset current sensor data, which improves the robot's avoidance The accuracy of barrier control.
  • Step S12 Obtain a robot obstacle avoidance decision based on the obstacle detection data.
  • step S12 includes:
  • A1. Obtain obstacle information fusion data according to the obstacle detection data and a pre-trained obstacle information data fusion model.
  • the A1 includes: extracting obstacle characteristics of the obstacle detection data; obtaining obstacle information data according to the obstacle characteristics, the data format of the obstacle information data is a vector; according to the obstacle information data and pre-training Obstacle detection data fusion model to obtain obstacle information fusion data.
  • the obstacle feature of the obstacle detection data is extracted, and the obstacle feature includes at least one of the following: presence or absence of obstacle, obstacle orientation, obstacle distance, and obstacle shape.
  • the obstacle information data is obtained according to the predetermined obstacle information data determination rule and the obstacle characteristics, and preferably, the obstacle information fusion data is obtained according to the obstacle information data and the pre-trained obstacle detection data fusion model.
  • the obstacle features of the obstacle detection data are extracted, and the obstacle features include: presence or absence of obstacles, obstacle orientation, obstacle distance, and obstacle shape.
  • the corresponding obstacle feature is extracted based on the ultrasonic sensor data, assuming that the obstacle feature corresponding to the ultrasonic sensor data is that there is an irregular shaped obstacle at a distance of 0.5 meters from the robot, and the obstacle is in the positive position of the robot In front, according to the predetermined obstacle information data determination rule, it can be obtained that the obstacle information data corresponding to the ultrasonic sensor data is (1,2,0.5,2).
  • Figure 2 shows an example of a pre-trained obstacle detection data fusion model.
  • X1, X2, X3, X4, and X5 respectively represent obstacle information data corresponding to the first infrared sensor data, obstacle information data corresponding to the second infrared sensor data, obstacle information data corresponding to the camera data, and obstacles corresponding to the current sensor data Obstacle information data corresponding to information data and ultrasonic sensor data, according to the functional relationship Obtain barrier information fusion data.
  • the first infrared sensor data, the second infrared sensor data, the camera data, the current sensor data, and the ultrasonic sensor data cover common content and have different characteristics, obtain the obstacle information data according to the above obstacle detection data, and then according to the obstacle information data Obtain obstacle information fusion data with a pre-trained obstacle detection data fusion model, which retains the connection between the first infrared sensor data, the second infrared sensor data, the camera data, the current sensor data, and the ultrasonic sensor data , Improve the accuracy of obstacle information fusion data, improve the accuracy of robot obstacle avoidance control function.
  • the A2 includes: judging whether there is an obstacle according to the obstacle information fusion data; correspondingly, the decision to obtain the robot obstacle avoidance according to the obstacle information fusion data specifically: if there is an obstacle, then according to The obstacle detection data obtains the robot obstacle avoidance decision.
  • the obstacle information fusion data is analyzed to obtain the obstacle information fusion data analysis result, and whether there is an obstacle is determined according to the obstacle information fusion data analysis result; correspondingly, the robot obstacle avoidance decision is obtained according to the obstacle information fusion data Specifically, if there is an obstacle, the robot obstacle avoidance decision is obtained according to the obstacle detection data.
  • the robot obstacle avoidance decision is obtained based on the obstacle detection data, the scientificity of the robot obstacle avoidance decision can be improved, which is beneficial to the robot's accurate obstacle avoidance.
  • obtaining the robot's obstacle avoidance decision based on the obstacle detection data includes: if there is an obstacle, obtaining the distance between the obstacle and the robot based on the obstacle information fusion data; The distance obtains the robot obstacle avoidance decision.
  • the obtaining a robot obstacle avoidance decision according to the distance includes: determining whether the distance is less than a preset distance threshold; if the distance is less than a preset distance threshold, generating a first robot obstacle avoidance decision, the first A robot obstacle avoidance decision includes decision information of the robot retreating; if the distance is not less than a preset distance threshold, a second robot obstacle avoidance decision is generated according to the position of the obstacle, and the second robot obstacle avoidance decision includes instructing the robot to The direction away from the obstacle is the decision information of the moving direction.
  • the distance is 0.2 meters and the preset distance threshold is 0.5 meters, it is determined that the distance is less than the preset distance threshold, and then a first robot obstacle avoidance decision is generated, and the first robot obstacle avoidance decision includes a robot backwards decision information.
  • Step S13 Control the robot to avoid obstacles according to the robot obstacle avoidance decision.
  • the entire robot is controlled to rotate 180 degrees according to the first robot obstacle avoidance decision, and then move in a direction after turning 180 degrees.
  • the obstacle detection data includes: infrared sensor data, camera data, current sensor data, ultrasonic sensor data; obtaining obstacle avoidance decision of the robot based on the obstacle detection data; according to the robot
  • the obstacle avoidance decision controls the robot to avoid obstacles. Due to the relatively low cost of infrared sensors, cameras, current sensors, ultrasonic sensors and other devices, the robot obstacle avoidance control function is realized through infrared sensor data, camera data, current sensor data, and ultrasonic sensor data, which can improve the accuracy of the robot obstacle avoidance control function . That is, on the basis of low production cost, high accuracy of the robot obstacle avoidance control function can be realized.
  • FIG. 3 shows a schematic structural diagram of a robot obstacle avoidance control device provided by an embodiment of the present application. For ease of description, only parts related to the embodiment of the present application are shown.
  • the robot includes at least a first infrared sensor, a second infrared sensor, a camera, a current sensor, and an ultrasonic sensor.
  • the first infrared sensor is installed at the front left of the robot, and the second infrared sensor is installed at the front right of the robot.
  • the first infrared sensor is installed at the front left of the robot
  • the second infrared sensor is installed at the front right of the robot.
  • they can be installed at the corresponding positions of the robot according to actual needs.
  • the robot obstacle avoidance control device includes: an obstacle detection data acquisition unit 31, a robot obstacle avoidance decision acquisition unit 32, and a robot obstacle avoidance control unit 33.
  • the obstacle detection data acquiring unit 31 is configured to acquire obstacle detection data, and the obstacle detection data includes: first infrared sensor data, second infrared sensor data, camera data, current sensor data, and ultrasonic sensor data.
  • the obstacle detection data acquisition unit 31 is configured to: if the first infrared sensor does not detect a target signal, set the first infrared sensor data to preset first infrared sensor data, and the target signal is to divide the Infrared signals radiated by substances outside the robot; if the second infrared sensor does not detect the target signal, then the second infrared sensor data is set as the preset second infrared sensor data.
  • the robot further includes a motor.
  • the obstacle detection data obtaining unit 31 is configured to: if the current sensor does not detect a target current signal, set the current sensor data to preset current sensor data, and the target current signal is a motor current signal greater than a preset motor current threshold.
  • the robot obstacle avoidance decision obtaining unit 32 is configured to obtain a robot obstacle avoidance decision according to the obstacle detection data.
  • the robot obstacle avoidance decision acquisition unit 32 includes: an obstacle information fusion data acquisition module and a decision acquisition module.
  • the obstacle information fusion data acquisition module is used to acquire obstacle information fusion data according to the obstacle detection data and a pre-trained obstacle information data fusion model.
  • the decision acquisition module is used to obtain a robot obstacle avoidance decision based on the obstacle information fusion data.
  • the obstacle information fusion data acquisition module is specifically configured to: extract obstacle characteristics of the obstacle detection data; obtain obstacle information data according to the obstacle characteristics, and the data format of the obstacle information data is a vector; according to the Obstacle information data and pre-trained obstacle detection data fusion model to obtain obstacle information fusion data.
  • the robot obstacle avoidance control device includes: a judgment unit whether an obstacle exists.
  • the judgment whether the obstacle exists unit is used to: before the decision acquisition module executes the acquisition of the robot obstacle avoidance decision based on the obstacle information fusion data, determine whether there is an obstacle according to the obstacle information fusion data; correspondingly, the The decision acquisition module is specifically used to: if there is an obstacle, obtain a robot obstacle avoidance decision based on the obstacle detection data.
  • the judging unit of the existence of the obstacle is specifically configured to: before the decision acquisition module executes the acquisition of the robot's obstacle avoidance decision based on the obstacle information fusion data, analyze the obstacle information fusion data to obtain the obstacle information fusion data analysis result, Determine whether there is an obstacle according to the analysis result of the obstacle information fusion data; correspondingly, the decision obtaining module is specifically used to: if there is an obstacle, obtain a robot obstacle avoidance decision according to the obstacle detection data.
  • the robot obstacle avoidance decision is obtained based on the obstacle detection data, the scientificity of the robot obstacle avoidance decision can be improved, which is beneficial to the robot's accurate obstacle avoidance.
  • the decision acquisition module includes: a distance acquisition submodule and a decision acquisition submodule.
  • the distance obtaining sub-module is used to obtain the distance between the obstacle and the robot according to the obstacle information fusion data if there is an obstacle.
  • the decision obtaining sub-module is used to obtain a robot obstacle avoidance decision according to the distance.
  • the decision acquisition submodule is specifically configured to: determine whether the distance is less than a preset distance threshold; if the distance is less than the preset distance threshold, generate a first robot obstacle avoidance decision, and the first robot avoids
  • the obstacle decision includes the decision information of the robot retreating; if the distance is not less than a preset distance threshold, a second robot obstacle avoidance decision is generated according to the position of the obstacle, and the second robot obstacle avoidance decision includes instructing the robot to move away from the obstacle
  • the direction of the obstacle is the decision information of the direction of movement.
  • the robot obstacle avoidance control unit 33 is configured to control the robot obstacle avoidance according to the robot obstacle avoidance decision.
  • the obstacle detection data includes: infrared sensor data, camera data, current sensor data, ultrasonic sensor data; obtaining obstacle avoidance decision of the robot based on the obstacle detection data; according to the robot
  • the obstacle avoidance decision controls the robot to avoid obstacles. Due to the relatively low cost of infrared sensors, cameras, current sensors, ultrasonic sensors and other devices, the robot obstacle avoidance control function is realized through infrared sensor data, camera data, current sensor data, and ultrasonic sensor data, which can improve the accuracy of the robot obstacle avoidance control function . That is, on the basis of low production cost, high accuracy of the robot obstacle avoidance control function can be realized.
  • the terminal device 4 of this embodiment includes: a processor 40, a memory 41, and a computer program 42 stored in the memory 41 and executable on the processor 40.
  • the processor 40 executes the computer program 42, the steps in the above embodiments of the robot obstacle avoidance control method are implemented, for example, steps S11 to S13 shown in FIG. 1.
  • the processor 40 executes the computer program 42, the functions of the units in the foregoing device embodiments are realized, for example, the functions of the units 31 to 33 shown in FIG. 3.
  • the computer program 42 may be divided into one or more modules/units, and the one or more modules/units are stored in the memory 41 and executed by the processor 40 to complete this invention.
  • the one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program 42 in the terminal device 4.
  • the computer program 42 may be divided into an obstacle detection data acquisition unit, a robot obstacle avoidance decision acquisition unit, and a robot obstacle avoidance control unit.
  • the specific functions of each unit are as follows:
  • the obstacle detection data acquisition unit is used to acquire obstacle detection data, and the obstacle detection data includes: first infrared sensor data, second infrared sensor data, camera data, current sensor data, and ultrasonic sensor data;
  • a robot obstacle avoidance decision acquisition unit configured to obtain a robot obstacle avoidance decision based on the obstacle detection data
  • the robot obstacle avoidance control unit is used to control the robot obstacle avoidance according to the robot obstacle avoidance decision.
  • the terminal device 4 may be a computing device such as a desktop computer, a notebook, a palmtop computer and a cloud server.
  • the terminal device may include, but is not limited to, the processor 40 and the memory 41.
  • FIG. 4 is only an example of the terminal device 4 and does not constitute a limitation on the terminal device 4, and may include more or less components than the illustration, or a combination of certain components or different components.
  • the terminal device may further include an input and output device, a network access device, a bus, and the like.
  • the so-called processor 40 may be a central processing unit (Central Processing Unit, CPU), or other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), Ready-made programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
  • the general-purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
  • the memory 41 may be an internal storage unit of the terminal device 4, such as a hard disk or a memory of the terminal device 4.
  • the memory 41 may also be an external storage device of the terminal device 4, such as a plug-in hard disk equipped on the terminal device 4, a smart memory card (Smart, Media, Card, SMC), and a secure digital (SD) Cards, flash cards, etc. Further, the memory 41 may also include both an internal storage unit of the terminal device 4 and an external storage device.
  • the memory 41 is used to store the computer program and other programs and data required by the terminal device.
  • the memory 41 can also be used to temporarily store data that has been or will be output.
  • each functional unit and module is used as an example for illustration.
  • the above-mentioned functions may be allocated by different functional units
  • Module completion means that the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above.
  • the functional units and modules in the embodiment may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.
  • the above integrated unit may use hardware It can also be implemented in the form of software functional units.
  • the specific names of each functional unit and module are only for the purpose of distinguishing each other, and are not used to limit the protection scope of the present application.
  • the disclosed device/terminal device and method may be implemented in other ways.
  • the device/terminal device embodiments described above are only schematic.
  • the division of the module or unit is only a logical function division, and in actual implementation, there may be another division manner, such as multiple units Or components can be combined or integrated into another system, or some features can be ignored or not implemented.
  • the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may be in electrical, mechanical or other forms.
  • the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.
  • the above integrated unit can be implemented in the form of hardware or software function unit.
  • the integrated module/unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium.
  • the present invention can realize all or part of the processes in the methods of the above embodiments, and can also be completed by a computer program instructing relevant hardware.
  • the computer program can be stored in a computer-readable storage medium. When the program is executed by the processor, the steps of the foregoing method embodiments may be implemented.
  • the computer program includes computer program code, and the computer program code may be in a source code form, an object code form, an executable file, or some intermediate form, etc.
  • the computer-readable medium may include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disc, a computer memory, and a read-only memory (ROM, Read-Only Memory) , Random Access Memory (RAM, Random Access Memory), electrical carrier signals, telecommunications signals and software distribution media, etc.
  • ROM Read-Only Memory
  • RAM Random Access Memory
  • electrical carrier signals telecommunications signals and software distribution media, etc.
  • the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in jurisdictions. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media Does not include electrical carrier signals and telecommunications signals.

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Abstract

一种机器人避障控制方法、装置、终端设备（4）及计算机可读存储介质，方法包括：获取障碍检测数据（S11），障碍检测数据包括：第一红外传感器数据、第二红外传感器数据、摄像头数据、电流传感器数据、超声波传感器数据；根据障碍检测数据获取机器人避障决策（S12）；根据机器人避障决策控制机器人避障（S13）。方法能够在低生产成本的基础上，实现机器人避障控制功能的高准确性。

Description

机器人避障控制方法、装置及终端设备 技术领域
本发明属于机器人控制技术领域，尤其涉及一种机器人避障控制方法、装置、终端设备及计算机可读存储介质。
背景技术
随着科学技术的不断发展，机器人的功能也越来越丰富。现有的机器人已经能实现语音交互、视觉检测、躲避障碍控制等功能。目前机器人的躲避障碍控制功能可通过震动传感器或超声波传感器来实现，此方法虽然生产成本低，但是准确性较低；目前机器人的躲避障碍控制功能也可利用视觉技术来实现，此方法虽然准确性较高，但是生产成本较高。以上所述的方法都不能同时具备生产成本低和准确性高的优点，导致机器人的运用不广泛。
技术问题
有鉴于此，本发明实施例提供了一种机器人避障控制方法、装置、终端设备及计算机可读存储介质，以解决现有技术中不能在低生产成本的基础上，实现机器人避障控制功能的高准确性的问题。
技术解决方案
本发明实施例的第一方面提供了一种机器人避障控制方法，所述机器人至少包括：第一红外传感器、第二红外传感器、摄像头、电流传感器、超声波传感器，所述第一红外传感器安装在机器人左前方，所述第二红外传感器安装在机器人右前方，包括：
获取障碍检测数据，所述障碍检测数据包括：第一红外传感器数据、第二红外传感器数据、摄像头数据、电流传感器数据、超声波传感器数据；
根据所述障碍检测数据获取机器人避障决策；
根据所述机器人避障决策控制机器人避障。
本发明实施例的第二方面提供了一种机器人避障控制装置，所述机器人至少包括：第一红外传感器、第二红外传感器、摄像头、电流传感器、超声波传 感器，所述第一红外传感器安装在机器人左前方，所述第二红外传感器安装在机器人右前方，包括：
障碍检测数据获取单元，用于获取障碍检测数据，所述障碍检测数据包括：第一红外传感器数据、第二红外传感器数据、摄像头数据、电流传感器数据、超声波传感器数据；
机器人避障决策获取单元，用于根据所述障碍检测数据获取机器人避障决策；
机器人避障控制单元，用于根据所述机器人避障决策控制机器人避障。
本发明实施例的第三方面提供了一种终端设备，包括：存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现如所述机器人避障控制方法的步骤。
本发明实施例的第四方面提供了一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，其特征在于，所述计算机程序被处理器执行时实现如所述机器人避障控制方法的步骤。
有益效果
本发明实施例与现有技术相比存在的有益效果是：通过获取障碍检测数据，所述障碍检测数据包括：红外传感器数据、摄像头数据、电流传感器数据、超声波传感器数据；根据所述障碍检测数据获取机器人避障决策；根据所述机器人避障决策控制机器人避障。由于红外传感器、摄像头、电流传感器、超声波传感器等装置的成本比较低，通过红外传感器数据、摄像头数据、电流传感器数据、超声波传感器数据实现机器人避障控制功能，能提高机器人避障控制功能的准确性。即能够在低生产成本的基础上，实现机器人避障控制功能的高准确性。
附图说明
为了更清楚地说明本发明实施例中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1是本发明实施例提供的一种机器人避障控制方法的流程示意图；
图2是本发明实施例提供的一种预训练好的障碍检测数据融合模型的示意图；
图3是本发明实施例提供的一种机器人避障控制装置的结构示意图；
图4是本发明实施例提供的终端设备的示意图。
本发明的实施方式
以下描述中，为了说明而不是为了限定，提出了诸如特定系统结构、技术之类的具体细节，以便透彻理解本申请实施例。然而，本领域的技术人员应当清楚，在没有这些具体细节的其它实施例中也可以实现本申请。在其它情况中，省略对众所周知的系统、装置、电路以及方法的详细说明，以免不必要的细节妨碍本申请的描述。
为了说明本申请所述的技术方案，下面通过具体实施例来进行说明。
应当理解，当在本说明书和所附权利要求书中使用时，术语“包括”指示所描述特征、整体、步骤、操作、元素和/或组件的存在，但并不排除一个或多个其它特征、整体、步骤、操作、元素、组件和/或其集合的存在或添加。
还应当理解，在此本申请说明书中所使用的术语仅仅是出于描述特定实施例的目的而并不意在限制本申请。如在本申请说明书和所附权利要求书中所使用的那样，除非上下文清楚地指明其它情况，否则单数形式的“一”、“一个” 及“该”意在包括复数形式。
还应当进一步理解，在本申请说明书和所附权利要求书中使用的术语“和/或”是指相关联列出的项中的一个或多个的任何组合以及所有可能组合，并且包括这些组合。
如在本说明书和所附权利要求书中所使用的那样，术语“如果”可以依据上下文被解释为“当...时”或“一旦”或“响应于确定”或“响应于检测到”。类似地，短语“如果确定”或“如果检测到[所描述条件或事件]”可以依据上下文被解释为意指“一旦确定”或“响应于确定”或“一旦检测到[所描述条件或事件]”或“响应于检测到[所描述条件或事件]”。
具体实现中，本申请实施例中描述的终端包括但不限于诸如具有触摸敏感表面(例如，触摸屏显示器和/或触摸板)的移动电话、膝上型计算机或平板计算机之类的其它便携式设备。还应当理解的是，在某些实施例中，上述设备并非便携式通信设备，而是具有触摸敏感表面(例如，触摸屏显示器和/或触摸板)的台式计算机。
在接下来的讨论中，描述了包括显示器和触摸敏感表面的终端。然而，应当理解的是，终端可以包括诸如物理键盘、鼠标和/或控制杆的一个或多个其它物理用户接口设备。
终端支持各种应用程序，例如以下中的一个或多个：绘图应用程序、演示应用程序、文字处理应用程序、网站创建应用程序、盘刻录应用程序、电子表格应用程序、游戏应用程序、电话应用程序、视频会议应用程序、电子邮件应用程序、即时消息收发应用程序、锻炼支持应用程序、照片管理应用程序、数码相机应用程序、数字摄影机应用程序、web浏览应用程序、数字音乐播放器应用程序和/或数字视频播放器应用程序。
可以在终端上执行的各种应用程序可以使用诸如触摸敏感表面的至少一个公共物理用户接口设备。可以在应用程序之间和/或相应应用程序内调整和/或改变触摸敏感表面的一个或多个功能以及终端上显示的相应信息。这样，终端的公共物理架构(例如，触摸敏感表面)可以支持具有对用户而言直观且透明的用户界面的各种应用程序。
实施例一：
图1示出了本申请实施例提供的第一种机器人避障控制方法的流程示意图，详述如下：
所述机器人至少包括：第一红外传感器、第二红外传感器、摄像头、电流传感器、超声波传感器，所述第一红外传感器安装在机器人左前方，所述第二红外传感器安装在机器人右前方。当然，若还存在其他红外传感器，则可根据实际需要，安装在机器人的相应位置。
以上所述的第一红外传感器、第二红外传感器、摄像头、电流传感器、超声波传感器都是成本较低的装置。
步骤S11，获取障碍检测数据，所述障碍检测数据包括：第一红外传感器数据、第二红外传感器数据、摄像头数据、电流传感器数据、超声波传感器数据。
可选地，所述步骤S11包括：若第一红外传感器没有检测到目标信号，则将第一红外传感器数据设置为预设第一红外传感器数据，所述目标信号为除所述机器人外的物质所辐射出的红外线信号；若第二红外传感器没有检测到目标信号，则将第二红外传感器数据设置为预设第二红外传感器数据。
具体地，若目标在机器人的右侧，则第一红外传感器很可能没有检测到该 目标对应目标信号；同理，若目标在机器人的左侧，则第二红外传感器很可能没有检测到该目标对应目标信号。如此，当出现第一红外传感器或/和第二红外传感器没有检测到目标信号的情况时，能根据所述预设第一红外传感器数据或/和预设第二红外传感器数据，分析障碍的存在情况，提高了机器人避障控制的准确性。
可选地，所述机器人还包括电机。所述步骤S11包括：若电流传感器没有检测到目标电流信号，则将电流传感器数据设置为预设电流传感器数据，所述目标电流信号为大于预设电机电流阈值的电机电流信号。
如此，当出现电流传感器没有受到外力阻碍的情况，电机电流等于或小于预设电机电流阈值，电流传感器没有检测到目标电流信号，能根据预设电流传感器数据分析障碍的存在情况，提高了机器人避障控制的准确性。
步骤S12，根据所述障碍检测数据获取机器人避障决策。
可选地，所述步骤S12包括：
A1、根据所述障碍检测数据和预训练好的障碍信息数据融合模型获取障碍信息融合数据。
A2、根据所述障碍信息融合数据获取机器人避障决策。
可选地，所述A1包括:提取所述障碍检测数据的障碍特征；根据所述障碍特征获取障碍信息数据，所述障碍信息数据的数据格式是向量；根据所述障碍信息数据和预训练好的障碍检测数据融合模型获取障碍信息融合数据。
具体地，提取所述障碍检测数据的障碍特征，所述障碍特征包括以下至少一种：障碍有无、障碍方位、障碍距离、障碍形状。之后，再根据预设障碍信息数据确定规则和所述障碍特征获取障碍信息数据，最好根据所述障碍信息数据和预训练好的障碍检测数据融合模型获取障碍信息融合数据。
例如，提取所述障碍检测数据的障碍特征，所述障碍特征包括：障碍有无、障碍方位、障碍距离、障碍形状。假设所述预设障碍信息数据确定规则为：将障碍信息数据表示为X＝(a，b，c，d)，a表示障碍有无，若有障碍，则a为1，若无障碍，则a为0；b表示障碍方位，若无障碍方位，则b为0，若障碍方位为左前方，则b为1，若障碍方位为正前方，则b为2，若障碍方位为右前方，则b为3；c表示障碍距离，若无障碍距离，即无障碍，则c为无穷大，若有障碍，则c为实际距离检测值；d表示障碍形状，若无形状，即无障碍，则d为0，若障碍形状是规则形状，则d为1，若障碍形状是不规则形状，则d为2。根据该预设障碍信息数据确定规则，获取第一红外传感器数据、第二红外传感器数据、摄像头数据、电流传感器数据、超声波传感器数据各自对应的障碍信息数据。以超声波传感器数据为例，根据该超声波传感器数据提取出对应的障碍特征，假设该超声波传感器数据对应的障碍特征为在距机器人0.5米处存在不规则形状的障碍，且该障碍处于该机器人的正前方，根据该预设障碍信息数据确定规则，可得该超声波传感器数据对应的障碍信息数据是(1，2，0.5，2)。
图2所表示的是预训练好的障碍检测数据融合模型的其中一个例子，如图2所示，该预训练好的障碍检测数据融合模型是利用反向传播算法的多层感知器模型建立的一个三层人工神经网络，该障碍检测数据融合模型由输入层、中间层和输出层组成，其中，输入层由X1、X2、X3、X4、X5和输入层权值Ai组成，其中，i＝{1，2，3，4，5}，中间层权值用Bj表示，其中，j＝{1，2，3，4，5}，Y表示输出结果，即Y为障碍信息融合数据。假设X1、X2、X3、X4、X5分别表示的是第一红外传感器数据对应的障碍信息数据、第二红外传感器数据对应的障碍信息数据、摄像头数据对应的障碍信息数据、电流传感器数据 对应的障碍信息数据、超声波传感器数据对应的障碍信息数据，根据函数关系式
Figure PCTCN2019124353-appb-000001
获得障碍信息融合数据。
由于第一红外传感器数据、第二红外传感器数据、摄像头数据、电流传感器数据、超声波传感器数据，涵盖有共性内容且具有不同特点，根据以上障碍检测数据获得障碍信息数据，再根据所述障碍信息数据和预训练好的障碍检测数据融合模型获取障碍信息融合数据，所述障碍信息融合数据保留了第一红外传感器数据、第二红外传感器数据、摄像头数据、电流传感器数据、超声波传感器数据之间的联系，提高了障碍信息融合数据的准确性，提高机器人避障控制功能的准确性。
可选地，在所述A2之前，包括：根据所述障碍信息融合数据判断是否存在障碍；对应地，所述根据所述障碍信息融合数据获取机器人避障决策具体为：若存在障碍，则根据所述障碍检测数据获取机器人避障决策。
具体地，分析所述障碍信息融合数据，获取障碍信息融合数据分析结果，根据所述障碍信息融合数据分析结果判断是否存在障碍；对应地，所述根据所述障碍信息融合数据获取机器人避障决策具体为：若存在障碍，则根据所述障碍检测数据获取机器人避障决策。
由于根据所述障碍检测数据获取机器人避障决策，因此能提高所述机器人避障决策的科学性，有利于机器人准确地避障。
可选地，所述若存在障碍，则根据所述障碍检测数据获取机器人避障决策，包括：若存在障碍，根据所述障碍信息融合数据获取所述障碍与所述机器人之间的距离；根据所述距离获取机器人避障决策。
可选地，所述根据所述距离获取机器人避障决策包括：判断所述距离是否 小于预设距离阈值；若所述距离小于预设距离阈值，则生成第一机器人避障决策，所述第一机器人避障决策包括机器人后退的决策信息；若所述距离不小于预设距离阈值，则根据所述障碍的位置生成第二机器人避障决策，所述第二机器人避障决策包括指示机器人以远离所述障碍的方向为运动方向的决策信息。
例如，所述距离是0.2米，预设距离阈值为0.5米，则判定所述距离小于预设距离阈值，则生成第一机器人避障决策，所述第一机器人避障决策包括机器人后退的决策信息。
步骤S13，根据所述机器人避障决策控制机器人避障。
例如，所述机器人避障决策为第一机器人避障决策，则根据所述第一机器人避障决策控制整个所述机器人转动180度，朝转动180度后的方向前进。
本发明实施例中，通过获取障碍检测数据，所述障碍检测数据包括：红外传感器数据、摄像头数据、电流传感器数据、超声波传感器数据；根据所述障碍检测数据获取机器人避障决策；根据所述机器人避障决策控制机器人避障。由于红外传感器、摄像头、电流传感器、超声波传感器等装置的成本比较低，通过红外传感器数据、摄像头数据、电流传感器数据、超声波传感器数据实现机器人避障控制功能，能提高机器人避障控制功能的准确性。即能够在低生产成本的基础上，实现机器人避障控制功能的高准确性。
实施例二：
与上述实施例一对应，图3示出了本申请实施例提供的一种机器人避障控制装置的结构示意图，为了便于说明，仅示出了与本申请实施例相关的部分。
所述机器人至少包括：第一红外传感器、第二红外传感器、摄像头、电流传感器、超声波传感器，所述第一红外传感器安装在机器人左前方，所述第二 红外传感器安装在机器人右前方。当然，若还存在其他红外传感器，则可根据实际需要，安装在机器人的相应位置。
该机器人避障控制装置包括：障碍检测数据获取单元31、机器人避障决策获取单元32、机器人避障控制单元33。
障碍检测数据获取单元31，用于获取障碍检测数据，所述障碍检测数据包括：第一红外传感器数据、第二红外传感器数据、摄像头数据、电流传感器数据、超声波传感器数据。
可选地，该障碍检测数据获取单元31用于：若第一红外传感器没有检测到目标信号，则将第一红外传感器数据设置为预设第一红外传感器数据，所述目标信号为除所述机器人外的物质所辐射出的红外线信号；若第二红外传感器没有检测到目标信号，则将第二红外传感器数据设置为预设第二红外传感器数据。
可选地，所述机器人还包括电机。该障碍检测数据获取单元31用于：若电流传感器没有检测到目标电流信号，则将电流传感器数据设置为预设电流传感器数据，所述目标电流信号为大于预设电机电流阈值的电机电流信号。
机器人避障决策获取单元32，用于根据所述障碍检测数据获取机器人避障决策。
可选地，机器人避障决策获取单元32包括：障碍信息融合数据获取模块、决策获取模块。
所述障碍信息融合数据获取模块，用于根据所述障碍检测数据和预训练好的障碍信息数据融合模型获取障碍信息融合数据。
所述决策获取模块，用于根据所述障碍信息融合数据获取机器人避障决策。
可选地，所述障碍信息融合数据获取模块具体用于：提取所述障碍检测数据的障碍特征；根据所述障碍特征获取障碍信息数据，所述障碍信息数据的数据格式是向量；根据所述障碍信息数据和预训练好的障碍检测数据融合模型获取障碍信息融合数据。
可选地，该机器人避障控制装置包括：障碍是否存在判断单元。
所述障碍是否存在判断单元用于：在所述决策获取模块执行所述根据所述障碍信息融合数据获取机器人避障决策之前，根据所述障碍信息融合数据判断是否存在障碍；对应地，所述决策获取模块具体用于：若存在障碍，则根据所述障碍检测数据获取机器人避障决策。
所述障碍是否存在判断单元具体用于：在所述决策获取模块执行所述根据所述障碍信息融合数据获取机器人避障决策之前，分析所述障碍信息融合数据，获取障碍信息融合数据分析结果，根据所述障碍信息融合数据分析结果判断是否存在障碍；对应地，所述决策获取模块具体用于：若存在障碍，则根据所述障碍检测数据获取机器人避障决策。
由于根据所述障碍检测数据获取机器人避障决策，因此能提高所述机器人避障决策的科学性，有利于机器人准确地避障。
可选地，所述决策获取模块包括：距离获取子模块、决策获取子模块。
所述距离获取子模块，用于若存在障碍，根据所述障碍信息融合数据获取所述障碍与所述机器人之间的距离。
所述决策获取子模块，用于根据所述距离获取机器人避障决策。
可选地，所述决策获取子模块具体用于：判断所述距离是否小于预设距离阈值；若所述距离小于预设距离阈值，则生成第一机器人避障决策，所述第一机器人避障决策包括机器人后退的决策信息；若所述距离不小于预设距离阈 值，则根据所述障碍的位置生成第二机器人避障决策，所述第二机器人避障决策包括指示机器人以远离所述障碍的方向为运动方向的决策信息。
机器人避障控制单元33，用于根据所述机器人避障决策控制机器人避障。
本发明实施例中，通过获取障碍检测数据，所述障碍检测数据包括：红外传感器数据、摄像头数据、电流传感器数据、超声波传感器数据；根据所述障碍检测数据获取机器人避障决策；根据所述机器人避障决策控制机器人避障。由于红外传感器、摄像头、电流传感器、超声波传感器等装置的成本比较低，通过红外传感器数据、摄像头数据、电流传感器数据、超声波传感器数据实现机器人避障控制功能，能提高机器人避障控制功能的准确性。即能够在低生产成本的基础上，实现机器人避障控制功能的高准确性。
应理解，上述实施例中各步骤的序号的大小并不意味着执行顺序的先后，各过程的执行顺序应以其功能和内在逻辑确定，而不应对本发明实施例的实施过程构成任何限定。
实施例三：
图4是本发明一实施例提供的终端设备的示意图。如图4所示，该实施例的终端设备4包括：处理器40、存储器41以及存储在所述存储器41中并可在所述处理器40上运行的计算机程序42。所述处理器40执行所述计算机程序42时实现上述各个机器人避障控制方法实施例中的步骤，例如图1所示的步骤S11至S13。或者，所述处理器40执行所述计算机程序42时实现上述各装置实施例中各单元的功能，例如图3所示单元31至33的功能。
示例性的，所述计算机程序42可以被分割成一个或多个模块/单元，所述一个或者多个模块/单元被存储在所述存储器41中，并由所述处理器40执行， 以完成本发明。所述一个或多个模块/单元可以是能够完成特定功能的一系列计算机程序指令段，该指令段用于描述所述计算机程序42在所述终端设备4中的执行过程。例如，所述计算机程序42可以被分割成障碍检测数据获取单元、机器人避障决策获取单元、机器人避障控制单元，各单元具体功能如下：
障碍检测数据获取单元，用于获取障碍检测数据，所述障碍检测数据包括：第一红外传感器数据、第二红外传感器数据、摄像头数据、电流传感器数据、超声波传感器数据；
机器人避障决策获取单元，用于根据所述障碍检测数据获取机器人避障决策；
机器人避障控制单元，用于根据所述机器人避障决策控制机器人避障。
所述终端设备4可以是桌上型计算机、笔记本、掌上电脑及云端服务器等计算设备。所述终端设备可包括，但不仅限于，处理器40、存储器41。本领域技术人员可以理解，图4仅仅是终端设备4的示例，并不构成对终端设备4的限定，可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件，例如所述终端设备还可以包括输入输出设备、网络接入设备、总线等。
所称处理器40可以是中央处理单元(Central Processing Unit，CPU)，还可以是其他通用处理器、数字信号处理器(Digital Signal Processor，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、现成可编程门阵列(Field-Programmable Gate Array，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。
所述存储器41可以是所述终端设备4的内部存储单元，例如终端设备4的硬盘或内存。所述存储器41也可以是所述终端设备4的外部存储设备，例 如所述终端设备4上配备的插接式硬盘，智能存储卡(Smart Media Card,SMC)，安全数字(Secure Digital,SD)卡，闪存卡(Flash Card)等。进一步地，所述存储器41还可以既包括所述终端设备4的内部存储单元也包括外部存储设备。所述存储器41用于存储所述计算机程序以及所述终端设备所需的其他程序和数据。所述存储器41还可以用于暂时地存储已经输出或者将要输出的数据。
所属领域的技术人员可以清楚地了解到，为了描述的方便和简洁，仅以上述各功能单元、模块的划分进行举例说明，实际应用中，可以根据需要而将上述功能分配由不同的功能单元、模块完成，即将所述装置的内部结构划分成不同的功能单元或模块，以完成以上描述的全部或者部分功能。实施例中的各功能单元、模块可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中，上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。另外，各功能单元、模块的具体名称也只是为了便于相互区分，并不用于限制本申请的保护范围。上述系统中单元、模块的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
在上述实施例中，对各个实施例的描述都各有侧重，某个实施例中没有详述或记载的部分，可以参见其它实施例的相关描述。
本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本发明的范围。
在本发明所提供的实施例中，应该理解到，所揭露的装置/终端设备和方法，可以通过其它的方式实现。例如，以上所描述的装置/终端设备实施例仅仅是示意性的，例如，所述模块或单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通讯连接可以是通过一些接口，装置或单元的间接耦合或通讯连接，可以是电性，机械或其它的形式。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本发明各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。
所述集成的模块/单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本发明实现上述实施例方法中的全部或部分流程，也可以通过计算机程序来指令相关的硬件来完成，所述的计算机程序可存储于一计算机可读存储介质中，该计算机程序在被处理器执行时，可实现上述各个方法实施例的步骤。。其中，所述计算机程序包括计算机程序代码，所述计算机程序代码可以为源代码形式、对象代码形式、可执行文件或某些中间形式等。所述计算机可读介质可以包括：能够携带所述计算机程序代码的任何实体或装置、记录介质、U盘、移 动硬盘、磁碟、光盘、计算机存储器、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、电载波信号、电信信号以及软件分发介质等。需要说明的是，所述计算机可读介质包含的内容可以根据司法管辖区内立法和专利实践的要求进行适当的增减，例如在某些司法管辖区，根据立法和专利实践，计算机可读介质不包括电载波信号和电信信号。以上所述实施例仅用以说明本发明的技术方案，而非对其限制；尽管参照前述实施例对本发明进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本发明各实施例技术方案的精神和范围，均应包含在本发明的保护范围之内。

Claims (10)

  1. 一种机器人避障控制方法，其特征在于，所述机器人至少包括：第一红外传感器、第二红外传感器、摄像头、电流传感器、超声波传感器，所述第一红外传感器安装在机器人左前方，所述第二红外传感器安装在机器人右前方，包括：
    获取障碍检测数据，所述障碍检测数据包括：第一红外传感器数据、第二红外传感器数据、摄像头数据、电流传感器数据、超声波传感器数据；
    根据所述障碍检测数据获取机器人避障决策；
    根据所述机器人避障决策控制机器人避障。
  2. 如权利要求1所述的机器人避障控制方法，其特征在于，所述获取障碍检测数据包括：
    若第一红外传感器没有检测到目标信号，则将第一红外传感器数据设置为预设第一红外传感器数据，所述目标信号为除所述机器人外的物质所辐射出的红外线信号；
    若第二红外传感器没有检测到目标信号，则将第二红外传感器数据设置为预设第二红外传感器数据。
  3. 如权利要求1所述的机器人避障控制方法，其特征在于，所述根据所述障碍检测数据获取机器人避障决策包括：
    根据所述障碍检测数据和预训练好的障碍信息数据融合模型获取障碍信息融合数据；
    根据所述障碍信息融合数据获取机器人避障决策。
  4. 如权利要求3所述的机器人避障控制方法，其特征在于，所述根据所述障碍检测数据和预训练好的障碍信息数据融合模型获取障碍信息融合数据，包 括：
    提取所述障碍检测数据的障碍特征；
    根据所述障碍特征获取障碍信息数据，所述障碍信息数据的数据格式是向量；
    根据所述障碍信息数据和预训练好的障碍检测数据融合模型获取障碍信息融合数据。
  5. 如权利要求3所述的机器人避障控制方法，其特征在于，在所述根据所述障碍信息融合数据获取机器人避障决策之前，包括：
    根据所述障碍信息融合数据判断是否存在障碍；
    对应地，所述根据所述障碍信息融合数据获取机器人避障决策具体为：
    若存在障碍，则根据所述障碍检测数据获取机器人避障决策。
  6. 如权利要求5所述的机器人避障控制方法，其特征在于，所述若存在障碍，则根据所述障碍信息融合数据获取机器人避障决策包括：
    若存在障碍，根据所述障碍信息融合数据获取所述障碍与所述机器人之间的距离；
    根据所述距离获取机器人避障决策。
  7. 如权利要求6所述的机器人避障控制方法，其特征在于，所述根据所述距离获取机器人避障决策包括：
    判断所述距离是否小于预设距离阈值；
    若所述距离小于预设距离阈值，则生成第一机器人避障决策，所述第一机器人避障决策包括机器人后退的决策信息；
    若所述距离不小于预设距离阈值，则根据所述障碍的位置生成第二机器人避障决策，所述第二机器人避障决策包括指示机器人以远离所述障碍的方向为 运动方向的决策信息。
  8. 一种机器人避障控制装置，其特征在于，所述机器人至少包括：第一红外传感器、第二红外传感器、摄像头、电流传感器、超声波传感器，所述第一红外传感器安装在机器人左前方，所述第二红外传感器安装在机器人右前方，包括：
    障碍检测数据获取单元，用于获取障碍检测数据，所述障碍检测数据包括：第一红外传感器数据、第二红外传感器数据、摄像头数据、电流传感器数据、超声波传感器数据；
    机器人避障决策获取单元，用于根据所述障碍检测数据获取机器人避障决策；
    机器人避障控制单元，用于根据所述机器人避障决策控制机器人避障。
  9. 一种终端设备，包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序，其特征在于，所述处理器执行所述计算机程序时实现如权利要求1至7任一项所述方法的步骤。
  10. 一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，其特征在于，所述计算机程序被处理器执行时实现如权利要求1至7任一项所述方法的步骤。
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